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AIML Generative AI.pptx

A brief introduction to Generative AI AIML Synthetic Data Generation Team 922023 01 02 03 04 Generative AI and its applications Overview of important algorithms Neural Networks LSTM Transformer GANs Fractals Data Generator architecture References Agenda What is generative AI Generative AI refers to a class of artificial intelligence algorithms that focus on generating new previously unseen data that conforms to certain patterns or characteristics learnt from actual real world data Resolving data scarcity unavailability Enabling data privacy Improve accuracy of AIML solutions Image Generation on Text prompt Generating HRES versions of images Enabling Humanlike conversation Semantic Image to photo translation Video prediction Music generation Applications Neural networks are a type of artificial intelligence modelled after the structure and function of the human brain. Neural Networks are the foundational block for Generative AI algorithms A neuron in a neural network is a basic unit of computation that receives inputs processes them and outputs a result. Neural Network Neuron Recurrent Neural Networks processes sequential data by retaining memory of previous inputs. RNNs were the initial choice for processing sequences LSTMs Long ShortTerm Memory Networks solves the problems of traditional RNNs by retaining memory over long sequences. Recurrent Neural Network LSTM LSTMs are further enhancements on RNNs capable of better handling long sequences LSTMs Long ShortTerm Memory Networks are a type of Recurrent Neural Network that solves the problems of traditional RNNs by retaining memory over long sequences. Transformers in deep learning are a type of neural network architecture designed to process sequential data efficiently using parallel computation. Transformers have revolutionized processing sequential data by introducing the concept of attention Discriminator Loss Noise input Generated data Deep Neural Network Output Score Deep Neural Network Real Data Generated Data 1 0 Generator Discriminator Tries to replicate the real data distribution so that it can trick the Discriminator. Is trained to distinguish between real and fake distributions. The two models compete with each other in a zero sum game with opposing goals to optimize. Generator Loss MLP in VGANs MHAN in TTS GANs CNNs in DC GANs MLP in VGANs MHAN in TTS GANs CNNs in DC GANs GANs introduced a unique architecture for data generation Summary report Scoring Model Selection Model Elimination Load In our utility we have combined the best bits of multiple algorithms with an aim to generate most accurate synthetic data PSI score based Data connectors to load from various sources RAW DATA PREPROCESSING MODULE Clean Clipping Imputation Transform Encode Transform Scale DATA GENERATION MODULE SYNTHETIC DATA EVALUATION MODULE SETUP GRID Creates a grid of GANs VGAN WGAN CTGAN TTSGAN Network single MLP vs Diverging MLP Hyperparams Learning rate activation epochs TRAIN Training the Generator and Discriminator on every combination in the grid. GENERATE Generating data on every trained model STORE Store the generated data for further evaluation MAE MAPE MI metrics Cross correlation Comparative analysis Summary of NN for advanced users only References Research Papers Ian J. Goodfellow Jean Pouget Abadie Mehdi Mirza Bing Xu David Warde Farley Sherjil Ozair Aaron Courville Yoshua Bengio 2014 June 10. Generative Adversarial Networks . Retrieved from arXiv1406.2661   stat.ML Mehdi Mirza Simon Osindero 2014 November 6. Conditional Generative Adversarial Nets . Retrieved from arXiv1411.1784   cs.LG Martin Arjovsky Soumith Chintala Lon Bottou 2017 January 26. Wasserstein GAN . Retrieved from arXiv1701.07875  stat.ML Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin Aaron Courville 2017 March 31. Improved Training of Wasserstein GANs . Retrieved from arXiv1704.00028   cs.LG Lei Xu Maria Skoularidou Alfredo CuestaInfante Kalyan Veeramachaneni 2019 July 1. Modelling Tabular data using Conditional GAN . Retrieved from arXiv1907.00503   cs.LG Code Repositories SDV CTGAN httpsgithub.comsdvdevCTGAN Generation and Evaluation of Synthetic Tabular Data using GANs OntheGenerationandEvaluationofSyntheticTabularDatausingGANs Generating Tabular Synthetic Data using GANs httpswww.maskaravivek.compostgansyntheticdatageneration Deep Convolutional Generative Adversarial Networks using Gradient Tape httpswww.tensorflow.orgtutorialsgenerativedcgan Improved WGAN Implementation httpsgithub.comkerasteamkerascontribblobmasterexamplesimproved_wgan.py Suggested Selflearning topics before GANs A neuron in a neural network is a basic unit of computation that receives inputs processes them and outputs a result. Neural Network Architecture Transformer encoders are responsible for encoding input sequences into hidden representations. Transformer Encoders MultiHead Attention is a mechanism that allows the model to attend to different aspects of the input simultaneously. MultiHead Attention Inner workings One Forward and Backward pass Fake Data Gz Label 1 Label 0 Discriminator Gx Gz W D DISCRIMINATOR TRAINING GENERATOR TRAINING Maximize log Dx log1 DGz H yperparameters and Evaluation metrics Scaling Scaling can be done to improve training time and can be used to apply limits on the range of the input data. MODEL TUNING Epochs and Learning Rate Training faster or training the model more can be achieved by tweaking epochs and learning rate. Number of Layers and Neurons For the model to learn the relationship between the columns better the layers and neurons can be tweaked. Activations The activation layer can be adjusted according to the scaling to limit values to ranges according to the desired output. EVALUATION METRICS KL Divergence It measures how one probability distribution diverges from a second probability distribution JS Distance Is a measure of similarity between two probability distributions bounded by 01. It is symmetric Cosine Similarity Quantifies the similarity between two or more vectors. Its the cosine of angle between the vectors. PCA Reduce the dimensionality of data to visually compare two datasets.

Automotive_Customer_360_POV (1).pptx

Fractal POV on Customer 360 in Automotive Industry Complexity and relevance of todays customer experience demand a clear understanding of all relevant touchpoints and all possible journeys The concept of fragmented phases in a traditional funnel logic to be replaced by an ongoing infinity loop through which manufacturers dealers and customers jointly drive and shape their experiences in a connected automotive ecosystem T he buying journey in the automotive industry is an iterative dynamic circular process ideally binding the customer to the brand Lack of guidance and expertise due to limitless unstructured information tend to overwhelm customers. Lack of personalization customers seek for optimal offerings for themselves given their unique circumstances Awareness Consideration Consultation Purchase Handover After Sales Two major pain points emerge throughout automotive customer journey lacking personalization and lacking guidance or expertise Lack of personalization Personal interactions between the salesperson and the customer strongly influence the choice of model and can even alter brand preference. Dealership visits in the contact phase are perceived as stressful customer experience may be impacted in a pushy environment Customers wish to be in a state of complete control and have an open discussion with dealers prior to signing the contract. Lack of personalization can lead to generic products being sold to customers which are eventually of no value to them Customers look for a clear and proactive communication from the dealer especially since a car purchase represents a major expense. Massive decrease in interaction and communication down to nearly zero contact can lead to severe customer dissatisfaction Poor follow up on the customers satisfaction with the purchase by displaying a lack of interest in identifying new needs or by failing to interact with the customer at further touchpoints lead to poor customer experience Lack of personalization tend to overwhelm customers with untargeted sales letters and invitations Build a comprehensive platform that potential customers can visit in order to obtain all necessary information in a onestop shop manner Considering existing sources on mobility behavior e.g. from other apps devices or sources and expose potential customers to the information content that is most relevant to them Complete customer profile by interlocking online and offline touchpoints can help lead customers seamlessly from online contacts to offline prospects Awareness Consideration Consultation Purchase Handover After Sales Product seamless chat support by immediately forwarding quotes and offers to customer devices Improve customers perceived efficacy and control by making contracts and legal documents available in an easily understandable manner in customers devices Drive personalization by offering needbased products or having settings adapted to personal information Target and provide customers with the relevant information at the right time via the right channel drive customers from seeing the brand on Instagram to the manufacturers website to confirming a local dealer appointment in minutes Build an app through creation of a singleaccess login that grants customers access to all brand channels and sites and provide them with immediate answers Enable transparent timely and proactive communications by helping customers track production status occasionally providing photos and a realtime estimate of the handover date Keep customers engaged by providing digital concierge on adjacent requirement like renting a garage spot or registering the license plate Align the handover event to customers personal preferences Create customer database and build a unified customer profile to enable sales associate greet them with their full profile and history at hand and provide a personalized and appreciative service experience. Help dealers get to know their customers better and help them proactively suggest matching products that aligns with the customers new lifestyle changes Product Type Passenger Car Truck Van Region Europe UK North America China APAC Customer Group Private Customer B2C Business Customer B2B Distribution Level Retail Wholesale Business Division New Cars Used Cars Financial and Mobility Services Service Parts Accessories Customer Journey Phase Prepurchase Purchase PostPurchase Potential Scope Introducing Genomics to build the customer 360 and drive personalization scale Consumer Insights Hub Consumer DNA Sales patterns Demographics profiling Consumer Next Best Action Product Offer Opportunity Browsing Drivers of behavior Decision explainability EMOTION LAYER Define enquiry Seek insights Validate insights Gamified Research Emotional appraisal ENTERPRISE CONSUMPTION Marketing platforms Sales platforms CRM platforms Dashboards and Apps Selflearning AI models  Explainability of drivers EMOTIONAL ASPECTS KNOWN UNKNOWNS Consumer or customer goals motivations preference sets SENSORIZE Consumer response to communication as a feedback loop AUTOMOTIVE ORG DATA KNOWN Demographics Transactions Ridesharing apps Word of mouth Magazines Advertising Automobile shows Contract signing Financing Insurance Waiting period handover status information Service Repair Requests newsletters mailings INTELLIGENCE LAYER Leverage nonconscious design principles INTERVENTION LAYER PERSONALIZED ConsumerLEVEL RECOMMENDATION DESIGN EXECUTION LAYER Automated ETL Data integrity and quality checks Curated statistical features HARMONIZATION LAYER SOLUTION ARCHITECTURE Solution Highlights 2021 Fractal Analytics Inc. All rights reserved Confidential Alwayson automated and explainable client decisioning LEARN Responsible AI toolkit Customer Genomics accelerator modules can help accelerate business value across the data to decision spectrum Data Decision How a Dealer of an auto showroom would consume personalization insights through Customer Genomics Priority Ranking and Next Best Action Recommendations Deepdive insights Deepdive insights behavioral markers Illustrative Example AWS Step Functions workflow Storage Ingest BATCH Processing Governance AWS Identity and Access Management IAM We are live on AWS marketplace and can deploy using AWS native components Appendix Channels of contact DRIVING SUCCESSFUL CROSSSELL THROUGH CG Drive sense of urgency NCD recommendation Customer 360 When a service request has had positive resolution would be the best moment to spontaneously recommend. z Highly likely to buy Pet insurance Inapp popup Channel When exploring Moment Intelligence Layer Sarah Joe An email with a comparison summary or checklist and key policy highlights would be the best way to get her onboard. Creating a sense of urgency with a timebound discounted offer for the policy would help him make the decision. Felix Intervention Layer Prediction Drive sense of economic utility NCD recommendation Customer 360 Highly likely to buy Auto insurance Email Channel Late evening Moment Intelligence Layer Intervention Layer Prediction Drive sense of control NCD recommendation Customer 360 Likely to buy HomeRenters insurance Call Channel Servicing session Moment Intelligence Layer Intervention Layer Age 45 CLV Low Recently updated home address Prediction Emotion Segment Distrustful High of call center operational queries no product addons Employed Browsing peak 8pm10pm Recently applied for an auto loan Emotion Segment Pragmatic Initiates enrollment into new productservice but seldom completes the journey Location Rural Digital engagement High Recent transactions in pet stores vet clinics Emotion Segment Ambiguous Long frequent selfinitiated inapp info browsing no action 2021 Fractal Analytics Inc. All rights reserved Confidential

AWS Manufacturing Fractal Capabilities_20230124.pptx

AWS Fractal Assetheavy industry workshop Jan 2023 Data to Decisions scale Building Connected Data Computing on the edge running enterprise ops insights on the go self service BI capability Building Manufacturing nerve center Overview of solutions and offerings across the value chain Manufacturing nerve centre Cloudbased cognitive control tower to assist in strategic and operational decisions Remote monitoring and providing feedback for actionability Parts management Visibility across parts availability to manage daily operations Predicting parts requirements  shortages to manage production maintenance schedules Asset utilization Determining long term capacity requirements to assist in Capex decisions Maximizing asset utilization for tactical operations by optimizing the schedules ESG consulting analytics ESG process consulting for assessing clients technology and process maturity Holistic ESG data model with prototypes for visibility across all the ESG KPIs Top focus solutions for todays discussion in the assetheavy industry 01 Manufacturing nerve center Fractals vision of manufacturing nerve centre Diagnose Predict Digital enablement Control towers visibility to transform offline siloed decision making to online connected decision making Cross functional Insights and Early warning predictive alerts recommendations Self serve diagnostics with automated RCA Early warning nudges to proactively manage deviations Automate Optimize Diagnostic capability to deep dive into the production metrics maintenance quality issues machine metrics etc. Prediction assessment to drive value for shop floor operators and plant managers Identify the bottlenecks for future n days Alternative option evaluation with financial service lead times Scenario planning Intelligent decision support system supported by robust cloud platform Smart automation for translating decisions to actions on underlying systems Automating work flows for actions on the shop floor Optimization of processes considering the real time plant constraints Manufacturing nerve center Enabling cognitive and autonomous decision making for synchronized operations ITOT Integration Manufacturing control tower case study CS1 We delivered 30 dashboards across multiple workstreams at various granularity levels Region BU country site levels Monthly quarterly yearly Category Brand Brand segments Executive insights across supply chain operations cost KPIs through supply chain control tower Manufacturing control tower Illustrative 1 Manufacturing control tower Illustrative 2 Real time insights for operators Shift summary for plant managers Manufacturing control tower Illustrative 3 Warehouse control tower CS2 Mission Control in a 3 phase approach Outcome Optimized action recommendation ML based learning system to plan operational activities basis real time delays AIML Interventions Create models to predict Target end times Pick Pack Stage OT Delivery risks Automated recording and rule creation Outcome Integrated realtime insights Decision cockpits for all Warehouse functions Enable MisCon pilot to undertake all operational decisions AIML Interventions Data ingestion harmonization Standardized KPIs Design intuitive and actionable UI 1 Outcome Automated alerts to MisCon Pilot through notification center Trigger actions from MisCon Auto execution of WMS MHE transactions through XMLs and screen automation AIML Interventions Smart automation through XML and screen automation all the operational interventions Automated triggers or alerts 2 3 Mission Control Mission Control Mission Control Pilot Semi auto Auto pilot 02 Parts management Parts Forecasting Parts segmentation to determine the critical vs regular parts Accurate prediction for uninterrupted production using ML algos Leveraging machine parameters to sense exact requirements Parts Optimization Determining minmax quantities of parts across nodes Recommending the safety cycle stocks based on the demandsupply volatility Visibility to planners on availability and shortages Predict shortages Part consolidation based on demand aggregation Predicting the parts availability based on lead time contracts volatility POs etc. Alerts warnings to better plan production Optimized production plan Providing timely signals to planners for Clear to build scenarios Optimizing the production plan and using flexibility to rejig schedules Determining impact on downstream systems Ensuring the right parts at the right moment for uninterrupted production Parts forecasting inventory optimization CS1 Automated e2e forecasting inventory optimization for spares Forecast the consumption of Spare parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process 2Mn savings across 30 of spare parts accessories globally Huge potential savings with scale Client ask Solution Result AIML driven methodology eventually leading to a fully automated procurement system Differential evolution optimization Performing various statistical analysis and hypothesis Differential evolution optimization for SKU Positioning into quadrants to understand Past behaviors and global behaviors Decide modelling technique for Forecasting demand Replenishment parameters at SKU level Min Max ROP safety stock EOQ Dynamic recommendations on minmax inventory norms Estimating and highlighting emerging trends and disconnects. Simulation of inventory norms w.r.t service levels supply demand parameters DL CNN LSTM ML Random forest Statistical A SRIMAX GARCH Parts shortage clear to build CTB CS2 Improved OTIF through predicting Product Shortage and integrated insights on Clear to Build Improve customer service level OTIF across network Proactively identify material shortages and enable early warning alerts by predicting material supply volatility Project material shortages to arrive at a potential Clear To Build status of their finished goods Estimating Receipt variance confirmed vs receipt at different days lag Delays in confirmed orders date by suppliermaterial Probability of variance at MaterialSupplier fulfillment pattern other Supply disruptions Project material shortages to arrive at potential Clear To Build at Finished goods Model to estimate upcoming receipt quantities for all confirmed purchase orders Simulator for Supply chain planners to capture Clear to build status mitigate shortages by supplier connect additional stock or stock transfer Client ask Solution Result High level solution approach for resilient material supply Integrate with CTB Project the product shortages to arrive at potential CTB at Finished goods Recommend best potential fulfilment date Recommendations on optimizing the components usage to maximize CTB of finished goods Predict Shortages Predict upcoming receipts quantity and ETA based on volatility in receipt quantity PO delays demand surge etc. Predict future shortages of parts Determine automated root causes for shortages Potential Fill rates Predict potential fill rates through Supply reliability Demand Mitigate the fill rate risks through optimal part allocation to CTB Capacity utilization planning 03 Long Term Capacity Optimization Determine the unconstrained demandbased capacity requirements across years Optimize longterm capacity plan to determine constrained demand which can be sufficed Identify the CAPEX requirement considering the CD ratio Scenario Planning Visualize opportunities and problems in network for overutilizedunderutilized capacities over years for simulation Integrate the optimization model to iterate based on userdefined constraints and inputs Decision Enablers Recommendations to mitigate capacity gaps for respective years Decision enablers to determine magnitude and timing of the excess capacity including CAPEX requirement in each year Capacity Utilization Impact in capacity production and demand gap w.r.t constraints input parameters Process reliability and schedule utilization achieved and RCA for improvement opportunity Capacity utilization planning approach Capacity Planning Capacity utilization 5Mn per line savings by recommending right changeover time based on demand Ideal changeover between presses to accommodate new demand O ptimize utilization based on mould to press compatibility Maintain the right CD ratio considering the outlook utilization metrics Identifying ideal CD ratio looking at CFR investment and asset utilization metrics Optimizing the ideal resources required with number of change overs Real time run time identification of next system based on Idle time and availability Long term simulated outcomes with optimum CD with lowest total cost There exist an optimal combination of Capacity and Inventory given a certain CFR CD ratio of 1.25 came out to be optimal ratio given all constraints Client ask Solution Result Manufacturing sub system Molds unit subsystem Molds availability Molds compatibility Press unit subsystem 6 cavity presses vs 12 cavity presses Our approach towards solving through optimization Maximum utilization of resources by risk pooling across pooling demand Reducing machine changeover times and demand supply gap to optimize schedules Generate optimized packaging schedules considering available capacity constraints Reduce the demandsupply gap while minimizing packsize change overs make time Provide offshelf site specific solution for validation and assess the change Optimal packaging schedule through linear programming Automated userdriven optimization with inputs Risk highlights for potential over underutilization of machines Optimized PPDS with following results Achieved avg demand supply gap 2.5 vs Current state 8 gap Reduced average changeover time by 5 Efficient short term plan with visibility into rolling 2 weeks Client ask Solution Result Oven L5 Machine 1 Machine 2 Machine 3 Output 4107 kg hr for all flavors One Flavor at a time Demand at SKU and Week level Machine 1 97g116g Machine 2 97g116g 194g233g Machine 3 194g233g 388g466g Machine 4 Portion Bulk i.e. 3kg Each machine Portion different capacity of production per hrs. Flavor change timecost is varying from 1 to 2 hrs depends Pack size change over is 1 hrs change over Whenever there is flavor and package change over comes together addup the both time Stop production for module cleaning 30 min. every 48 hours i.e. every alternate day of flavor 9 40 SKU Flavor Pack size Brand Production runs in 3 shift 24 7 Always should be on Weekly Demand of SKU vs Actual Production should be in 10 range Portion always being used incase when it m1 m2 m3 are not able to compensate op pf oven to fullfill the demand of SKUs is related to Pack size Bulk OP Production Plan for rolling 2 week at SKU level with details about SKU Machine Start time end time flavor change over Pack size setup time etc. details Diversion of Flow to portion incase Machine capacity incase not able to compensate Only one Portion can be active at time Machine 4 Solution schematics objective inputs constraints output Building capacity asset utilization Assist production managers analyze the asset utilization of machines across the plant Help organizations unleash capacity opportunities in their manufacturing lines 04 ESG consulting analytics For manufacturers to embark on ESG journey they often come across a multitude of challenges Energy Waste Water Management Energy Transition Emissions Management Data Security and Risk Management Occupational Heath Safety Lack of a centralization Lack of a uniform structure of info across cotton farms No track record of labor rights irregular pays . No opportunity to create crossfunctional insights and learnings High Manual Intervention High Manual intervention for KPI reporting High probability of errors and inaccuracies Due to high manual intervention more time and effort required Lack of endtoend visibility Absence of frameworks that can co relate different ESG pillars to each other No sense of status at desired time and frequency Data Collection Data collection for ESG from multiple departments and vendor. For traceability we use verified sources of data e.g. SAPPM FACTSET CSRHUB OAG supply chain systems POS Data Quality Data available at different granularities and frequencies across different stages of merchandise procurement. E.g. Quality of data from 3PL logistics not consistent thus we perform data cleaning and harmonization. Lack of standardization Lack of standard definitions of KPIs across different regions categories Inconsistent terminology Issues with measurement units of measure Data Challenges Priorities Accelerate ESG journey through a centralized data platform across enterprise Integrated data model Leading sustainability indicators Algorithmic decisions Decision cockpit Manufacturing SAPPM MES Enablon Human Rights Sedex SMETA Audit files OEM sourcing Logistics TMS SAPBW Mapping files Sourcing Supplier files Packaging SAP ECC PLM Mapping files Corporate Giving and DI inclusivity flat files Data harmonization KPI definition Source to target mapping Data model development Enterprise level automated audits Governance Supply chain vendor transparency Data security risk management Environment Energy Waste Intensity Water stress Manufacturing emissions. Fugitive Leakage Emissions Social Sustainable Sourcing Ethical supply chain transparency Supplier compliance Occupational health safety Waste Driver analysis Carbon Footprint prediction Simulation sandbox Safety incidents prediction ESG decree compliance Sustainability target glidepath Sustainable procurement Invest in right green projects Built an e2e data platform for ESG reporting and democratizing data for CPG manufacturing majors Integrated data platform to Automate Standardize Collaborate and Harmonize data across all functions for e2e ESG visibility on SAP BW platform 40 ESG KPIs across functions for periodic tracking and auditing Reduced TAT by 88 from 68 months to 1 month through automated standardized reporting Client ask Solution Result To build e2e data platform for ESG reporting and Democratizing data Automate data collection and minimize manual intervention Ability to align with key external frameworks on ESG This data model represents the logical structure of how ESG data is captured for different verticals within an organization and how the relationships are mapped between them. Example Below data model represents Water and Energy Verticals Holistic ESG d ata model to drive vertical startup on analytics journey ESG solution snapshot CS3 Inventory Optimization Monitor inventory across network Visualize current inventory across SKUs brands categories inventory segments regions DCs Monitor KPIs Current Minmax Days forward coverage stock OH overstock outofstock inventory turns etc. Identify opportunity areas for improvement across SKUs DCs segments Predict inventory risks OOS risk identification at SKUDC level and impact assessment on cost and CFR Prediction of Inventory waste due to excess stock assessment on cost Codify business rules thresholds for alertsrisks Highlight inventory risks due to product obsolescence SLOB inventory Determine inventory minmax norms Generate optimal recommendations for minmax stock at each granularity Generate stock projections into 12 weeks horizon Trigger automated alerts for current week future 2 weeks horizon MEIO optimization simulation Simulation of inventory requirements basis CFR demand supply lead time etc. Incorporating constraints like MOQ Customer Service level DC Capacity Holding cost to optimize MEI Recommendations on inventory allocations based on Case fill Promotion demand factors AI enabled approach for e2e inventory optimization 1Bn savings through e2e inventory optimization Reduce NonPerforming Inventory NPI across 13 categories for both Finished Goods and Raw Materials Establish business review process to track actions Optimize SKU portfolio and inventory Consultantled monthly inventory review process Key insights on NPI trends root causes recommendations on depleting inventory Action monitoring system to track Inventory reduction 1Bn savings across 21000 Finished product and 5300 raw packing product code over 4 years Client ask Solution Result Optimized e2e inventory enabling business decisions through AED approach Root causes and recommendations Automated root causes for NPI at Category Plant and SKU level Recommendations to enable MRP Category planners on MOQs Production orders Sourcing plan Purchase orders Inventory classification Blocked Discontinued Obsolete and Excess classification Enabled business to address what where why and how of Nonperforming inventory Business review sessions to tackle top contributors Potential Phase out SKUs Predict patterns of declining SKUs for upcoming quarters their product life cycle transitions Guidance on revising production parameters sourcing decisions for potential phase out SKU Optimized Inventory norms across the network nodes through AIML interventions Improve customer service levels Reduce high on hand inventory levels Static Inventory targets thresholds Optimize Inventory norms safety stocks at every Supply network node Predict Safety stocks based on demand and supply volatilities Optimize for Overage and Shortage at SKU Supply network node level Dynamic determination of Inventory norms process automation 40 reduction in Inventory norms and safety stocks with improved customer service levels Client ask Solution Result Dynamic Safety Stock through predictive algorithms Safety stock prediction Outcome Recommended safety stock Dynamic calculation to enable early interventions Scenario planning Driver analysis for all components of supply and demand Supply disruptions Demand fluctuation Supplier reliability Fulfilment rate Quality checks Leas time variance Supplier capacity Actual sales Demand forecast Promo events Returns exchange Similar products Calculate historical supply related KPI at required time granularity For demand factors calculate risk profiles to capture variance Leverage modelling techniques like Random forest SVM NN to predict safety stock

AWS Manufacturing Fractal Capabilities_Consolidated.pptx

AWS Fractal Manufacturing use cases Oct 2022 Index Predictive maintenance 01 Predictive maintenance for varied time to failures Unsupervised anomaly detection failure prediction for Industrial printers Unscheduled printer component failures resulting loss of printing time ink revenue high operational expenditure Predict anomalies failures for proactive scheduled maintenance Identified features impacting printer performance Unsupervised anomaly detection model using adaptive distribution at dailycomponentprinter level and survival models to predict time to failure 662 anomalies for 26 printers for the entire year Accuracy measures 67 on Ink consumption as of print area 72 on aborted jobtotal Jobs Client ask Solution Result Unsupervised anomaly detection to predict the failures Key Features indicating printer performance Enabled packaging line with real time anomaly detection Production line with multiple machines robots. OEMs equipped to provide static thresholds alerts for individual critical parameters Better understanding of machines swift decision making Integrated shop floor with data streaming Developed various analytical insights to enable smarter machines Adaptive algorithms on data from 21 sensors Real time anomaly detection for course correction at minute time granularity Client ask Solution Result Real time anomaly detection through adaptive algorithms from 21 sensors Reduced 57 of unplanned downtimes by continuous monitoring of process parameters Develop predictive maintenance algorithms based on the historical manufacturing data Failure prediction with 37 days in advance Power BI based UI with machine level predictions and early warnings Unsupervised anomaly detection through Gaussian classification failure prediction model Classification failures by creating lag variables Heuristics rule to reduce false positives RegressionTime series forecasting failures Machine level downtime risk prediction Reduced unplanned downtimes by 57 compared to previous qtr. Client ask Solution Result Increasing reliability of assets with Prescriptive analytics Early Anomaly Detection Actionable Insights The model Unsupervised anomaly detection for gearbox using sensor data Develop algorithmic workflow to enable automatic detection of anomalies utilizing gearbox sensor data Data streaming from various sensors for identifying anomalies in real time and predicting failures Anomaly detection model using adaptive machine learning technique capable of predicting from live streaming sensor data Support Vector Regression technique for model development Identified 32 days in 9 months with spectral data was observed to contain an anomaly Maximum anomalies observed in September for consistently longer period 17 days indicating high likelihood of a breakdown post this period Client ask Solution Result Reduced batch cycle time through discrete event simulation To simulate for most optimal batch cycle times with achievable combinations of parameter metrics schedules Simulator with statistically viable and robust solution to ensure that historical anomalies leading to longer Batch Cycle Times can be avoided in future Reduced number of Change overs across manufacturing lines Outcomes Multiscenario matrix of CPPs using Monte Carlo Simulation Genetic algorithm Simulation to find best possible combination Simulate CPPs and assess the impact on BCT Comparison with BIC batches Optimize BCT Monte Carlo simulations of risks for the optimal BCT relevant CPPs Genetic algorithm for integrated Manufacturing Scheduling Identify best possible combinations of CPPs to attain optimum BCT Simulate CPPs using conditional probabilities from BNN modeling Shop floor management Product Quality 02 3Mn reduction by avoiding product off spec Identify the root cause leading to issues with firmness of product Provide recommendation on how to control the various controllable factors to produce products as per firmness specification Critical process parameters for Golden batches Developed supervised learning drivers models to identify the significance of critical tags impacting product firmness Enabled SOP enhancements alerts for course correction 3Mn reduction by avoiding product off spec reducing rework product waste Improved Customer satisfaction Client ask Solution Result Business problem Nearly 50 of quality test samples of a dairy product whipping agent had lower firmness value than specification threshold Resulting in rejection of production batches Annual loss of 22MN Euros Customer complaints High amount of rework Samples having firmness values below threshold 70 Objective Identify the root cause leading to issues with firmness of product Provide recommendation on how to control the various controllable factors to produce products as per firmness specification A European dairy cooperative wanted to identify root causes for a product firmness issue monitor the quality in real time Sensor tags used in the dairy process manufacturing not exhaustive Collection consolidation of data Data Consolidation of all batches with corresponding quality samples Mapping batch sample output with Sensor tag values Feature identification Trend analysis of firmness of samples within a batchbetween batches Univariate bivariate analysis between 63 Tags Outlier identification Driver analysis Identified important Tags which influence firmness Built and evaluated competing supervised learning models LIKE decision trees for finding drivers Real time monitoring Directional relationship between important tags and Firmness Optimal ranges for controllable tags to achieve firmness level 70 Sensors like Temperature nozzle pressure valve pressure etc. Control charts trend charts Predicting product firmness based on real time monitoring of critical process parameters Clustering Decision Tree models Recommended threshold values of sensor tags Identified critical manufacturing process parameters leading to a Golden Batch Identified important tags that drive the firmness of the product during process manufacturing. E.g. Plenum Temperature Nozzle pressure HD Flow Pump Inlet Temperature etc. Line operators can focus on the controllable tags in the recommended operational ranges for better firmness of product Revised the SOP ranges of various sensory tags through the decision tree approach Useful for business to get sense of which valuescombination of parameters will lead to a Golden Batch Would help line operators to identify thresholds Workload improvement for operator and analysis Recommendations for NPD process Decision Tree to arrive at tag thresholds Solution Capabilities Key results Clustering for identification of important tags Variable importance from model output Created saving opportunities of 400K Euros per product SCADA MES Gather EnrichStream Sensors Actuators IOT Gateway HTTP WiFi serial ports MQTT Event processing Storage Datalakes Hadoop clusters Realtime message brokering kafka ActiveMQ Complex event processing event pattern detection filtering Device registry AIML Models CNN LSTM Decision trees App backend HTTP Consumption Visualization Portal HTML5 Leveraging IIOT on a large scale for real time monitoring Process control chart for Nozzle Pressure Revised SOP range for Golden batch Nozzle Pressure 231 Consumption layer Identification of CPP Plenum Temperature 0 30 C Temperature 40 Flow HD Pump 5500 Flow 5000 Nozzle pressure 260 Pressure 231 Current status of sensor tags Predicted value of Product Firmness 68.5 Plenum Temperature 3 110 C Temperature 90 DP Plenum 1 70 C Temperature 40 Plenum Temperature 1 100 C Temperature 92 Plenum Temperature 2 105 C Temperature 90 DP Plenum 2 49 C Temperature 50 DP Plenum 3 59 C Temperature 60 Consumption layer Real time monitoring of sensor tags course correction MRO Spares 02 Automated e2e forecasting inventory optimization for spare parts Forecast the consumption of Spare parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process 2Mn savings across 30 of spare parts accessories globally Huge potential savings with scale Client ask Solution Result ESG Capability 02 Lack of standardization in data collection Limited data available for Private companies Low correlation in ESG scores by leading agencies Weightage given to ESG metrics differ by region Lack of transparency in underlying factors driving the ESG score The lack of a single regulatory framework The lack of consensus on terminology and definitions The lack of auditing which limits the reliability of disclosure reports. There is no universal system to verify reported data and public reports tend to highlight positive contributions to ESG and underplay or omit less flattering reporting this is called greenwashing . The fact that only larger public companies are required to report ESG data which is published in their annual reports. This limits the data pool to what these companies selfdisclose. There is no regulation governing the assessment of sustainability requirements and performance and every ESG rating agency has its own methodologywhich evolves over time. Copyright 2023 fractal. All rights reserved. Challenges commonly faced by organizations during data gathering for ESG metrics Need to update as per heavy asset Analyze data relating to traffic patterns realtime demand and networkresource availability It allows for quick automated decisions on the parts of the system that can be put into sleep mode or shut down Shutting down frequency carriers or shutting down a site momentarily in areas where there is overlapping coverage Smart shutdowns IOT sensors can gauge gridpower input fuel levels the number of hours the generation set has been running Battery voltages and consumption by different types of equipment Analyzing the data operators could uncover potentially costly anomalies Real time alerts. IOT enabled optimization In cement plants the AI realtime optimizer RTO adapts to continually improve optimization outcomes. AI can help optimize energy consumption and throughput of cement kilns and mills by improving equipment productivity in view of rising environmental concerns and CO2 costs. AI powered optimization Identifying Leak detection and repair systems at compression stations. Using analytics to predict the failure of the equipment at compression stations and pipelines. Predictive analytics Purchase or generate green energy Direct procurement and competitive sourcing. Sustainable sourcing Energy savings levers for Heavy Industries Heavy Industries like Cement Oil Gas Utilities metals mining can leverage upon multiple areas like AI infra site optimization sustainable procurement etc. Need to update as per heavy asset Embedding Analytics Capabilities into Sustainability building blocks Need to update as per heavy asset VISIBILITY OF SCOPE 1 2 AND 3 EMISSIONS ACROSS THE VALUE CHAIN IS KEY TO JUMPSTART THE JOURNEY Scope 1 emissions are direct emissions from owned or controlled sources. Scope 2 emissions are indirect emissions from the generation of purchased energy. Scope 3 emissions are all indirect emissions not included in scope 2 that occur in the value chain of the reporting company including both upstream and downstream emissions. 1 2 3 EP Emissions from drilling well testing Operational spills Methane leaks and gas flaring at the wellhead 1 Shipping Transportation Emissions from fuel consumed by company owned vehicles such as trucks ships helicopters Processing Combustion of natural gas and other fuels in refineries plants or other facilities Chemical emissions from plants 1 1 Operating IT emissions on premise data centre 1 Purchase of electricity and heat in every company owned facility Plants mining sites Operational facilities Data centre operations 2 Employee Commuting Daily commute to the office Employee air and rail travel car rentals business travel Offshore site visits 3 Embedded emissions In buildings equipment In leased buildings vehicles assets 3 Transportation and distribution In the entire value chain from maritime shipping to the retail dealers if utilizing vendors rather than company owned vehicles 3 Other operations Franchises Paper consumption Endoflife disposal of unsold products Scrap from equipment or waste generated in the production facilities 3 Usage of products from end consumers 3 Copyright 2023 fractal. All rights reserved. Objectives Challenges Solution Approach Objectives To build e2e data platform for ESG reporting and Democratizing data Automate data collection and minimize manual intervention Ability to align with key external frameworks on ESG Business challenges Multiple data sources Regular Manual intervention Nonstandardized process and data flow Irregular data granularity Lack of Data Harmonization Integrated data platform to Automate Standardize Collaborate and Harmonize data across all functions for e2e ESG visibility on SAP BW platform 40 ESG KPIs across functions for periodic tracking and auditing KPIs and metrics to track Energy and Water consumption CO2 Emissions Benchmark emission across SC nodes Logistics Impact on emissions and longterm roadmap and projections Packaging Recyclability index Building a e2e data platform for ESG reporting and democratizing data A global database providing Standard Reports Benchmarking Baselines Single point of truth for nonfinancial performances Data validation and rectification at the source Outcomes impact Capacity utilization planning 03 Long Term Capacity Optimization Determine the unconstrained demandbased capacity requirements at SKUline across years Optimize longterm capacity plan at SKUplantline level to determine constrained demand which can be sufficed Scenario Planning Visualize opportunities and problems in network for overutilizedunderutilized capacities over years for simulation Integrate the optimization model to iterate based on userdefined constraints and inputs Decision Enablers Recommendations to mitigate capacity gaps for respective years Decision enablers to determine magnitude and timing of the excess capacity including CAPEX requirement in each year Capacity Utilization Impact in capacity production and demand gap w.r.t constraints input parameters Process reliability and schedule utilization achieved and RCA for improvement opportunity Capacity Utilization Planning approach Capacity Planning Capacity utilization 5Mn per line savings by recommending right changeover time based on demand for CPG giant Ideal changeover between presses to accommodate new demand O ptimize utilization based on mould to press compatibility Maintain the right CD ratio considering the outlook utilization metrics Identifying ideal CD ratio looking at CFR investment and asset utilization metrics Optimizing the ideal resources required with number of change overs Real time run time identification of next system based on Idle time and availability Long term simulated outcomes with optimum CD with lowest total cost There exist an optimal combination of Capacity and Inventory given a certain CFR CD ratio of 1.25 came out to be optimal ratio given all constraints Client ask Solution Result Manufacturing sub system Molds unit subsystem Molds availability Molds compatibility Press unit subsystem 6 cavity presses vs 12 cavity presses Our approach towards solving through optimization Maximum utilization of resources by risk pooling across pooling demand Improved packaging efficiency reducing both machine changeover times and demand supply gap Generate optimized packaging schedules considering available capacity constraints Reduce the demandsupply gap while minimizing packsize change overs make time Provide offshelf site specific solution for validation and assess the change Optimal packaging schedule through linear programming Automated userdriven optimization with inputs Risk highlights for potential over under utilization of machines Optimized PPDS with following results Achieved avg demand supply gap 2.5 vs Current state 8 gap Reduced average changeover time by 5 Efficient short term plan with visibility into rolling 2 weeks Client ask Solution Result 1 production line 5 Packaging lines 40 SKUs 9 flavors 7 Pack sizes 10 Constraints Oven L5 Machine 1 Machine 2 Machine 3 Output 4107 kg hr for all flavors One Flavor at a time Demand at SKU and Week level Machine 1 97g116g Machine 2 97g116g 194g233g Machine 3 194g233g 388g466g Machine 4 Portion Bulk i.e. 3kg Each machine Portion different capacity of production per hrs. Flavor change timecost is varying from 1 to 2 hrs depends Pack size change over is 1 hrs change over Whenever there is flavor and package change over comes together addup the both time Stop production for module cleaning 30 min. every 48 hours i.e. every alternate day of flavor 9 40 SKU Flavor Pack size Brand Production runs in 3 shift 24 7 Always should be on Weekly Demand of SKU vs Actual Production should be in 10 range Portion always being used incase when it m1 m2 m3 are not able to compensate op pf oven to fullfill the demand of SKUs is related to Pack size Bulk OP Production Plan for rolling 2 week at SKU level with details about SKU Machine Start time end time flavor change over Pack size setup time etc. details Diversion of Flow to portion incase Machine capacity incase not able to compensate Only one Portion can be active at time Machine 4 PPDS POC solution schematics objective inputs constraints output Region Business Unit Year Brand Saved scenarios Capacity Optimization Filters AMEA INDIA 2022 Brand 1 Create new scenario None Region Business Unit Year Brand Saved scenarios Capacity Optimization Filters AMEA INDIA 2022 Brand 1 Create new scenario None Optimized Capacity Plan Production quantity cases Total production cost Publish Capacity Plan Go back Total line capacity cases Building Capacity Asset utilization solution Assist production managers analyze the asset utilization of machines across the plant Help organizations unleash capacity opportunities in their manufacturing lines SOP 04 Digital enablement through integrated SOP visibility SOP Control Tower Nudges Early warning indicators Course correction using thresholds Visibility Single source of truth for track and trace Realtime monitoring control Collaboration Internal as well as External collaboration Instant feedback mechanism Selfserve Diagnostics Driver analysis for RCA Business scenario planning Security Governance Role and functions based access Secured data maintenance Deep Dives Deep dives into Siloed functions Performance and variance analysis Decision Support Cognitive based decision making Intelligent recommender systems Integrated Platform UpstreamDownstream integration Robust system with automated data pipelines Transformed SOP process with Supply Chain Planning Control tower Single source of truth with right data at right time across 11 Sourcing platforms 25 Plants 13 DCs Enable collaboration across functions to drive Operational Strategic decisions Forward looking capabilities Integrated Supply chain data platform Cross functional user stories Demand at risk Early warnings alerts on upcoming risks Optimized planning scheduling asset utilization Faster decision making across Sales Operations planning Client ask Solution Result Driving efficiencies across functions through Control tower Demand analysis Real time collaboration on forecast changes multifunctional stakeholders Raw Material Risk Evaluator Raw material supply risk demand at risk mitigation Manufacturing Driving efficiency through connected insights asset utilization and plant MRO Demand Supply Reconciliation Optimize Allocation priorities with Supply constraints Inventory visibility Capacity analysis Capacity constraints gaps between CoMan Inhouse Mfg alternative capacity plans change overs PR Financial Implications Reconcile financials against AOP IBP demand call significant COPS deviations Quality holds Working capital release through visibility and driver analysis of blocked inventory Customer Order Descriptive analysis to track customer orders allocations and processes Freight Costs and Drivers Insights on freight costs drivers load utilization and freight spot rates Supplier cockpit Speed to decision making through visibility in supplier management Integrated Demand analysis providing e2e Visibility of historical Forecast vs Actuals vs Forward projections Demand Supply reconciliation for informed decisions based on actual historical performance Financial Implications of demand supply financial CCH Mfg 03 Business needs Executive insights across manufacturing sustainability Challenges 16 data sources for gathering data 60 time was spent on data gathering 30 FTEs involved in creating executive reports Limited trust in data as everyone had their own interpretation Multiple tools were needed to get e2e picture Need for flexible views Comparison of data from different perspectives View historic vs. future trends View data as per specific needs for a user country vs. function vs. business level Need for next best actions Identify insights easily Identify and mitigate risks Identify next possible actions Create a single source of truth which would provide the right data at the right time to become more strategy focused Visibility across functional lines for 360 supply chain assessment Visibility across costs and enable benchmarking across business units and trend spotting Highlights and benchmarking key executive manufacturing KPIs Highlights and benchmarking key executive sustainability KPIs Plant performance visibility 04 Connected insights across manufacturing How to reduce manufacturing costs at the plantline level while overcoming the biggest challenge of global benchmarking and segmenting the plantslines for comparison on factors such as automation of the plant category produced in the plant and size of the plant. Improve Manufacturing KPIs by drawing insights and identifying opportunities for Productivity improvements Adopt analytical rigor in arriving at the KPIs across various lines within and across plants Dynamic Benchmarking capabilities across Plants Lines and Tracking against Designed and Operational Targets Process Reliability Manufacturing Yield Potential output PlannedUnplanned Losses PR opportunity losses Quality Compliance MTBFMTTF 2020 Fractal Analytics Inc. All rights reserved Confidential Measuring KPIs across reliability engineering for e2e visibility Scheduled Utilization Line utilization factor availability Designed Capacity Machine utilization rate Time loss Manufacturing Spend People Costs Indirect Costs Planned Unplanned Costs Manufacturing capacity planning and scheduling CU and SU visibility Business question answered What is the system availability at a shiftline level What are the root cause for machine failure and losses due to the failure What is the average and benchmark Batch Cycle Time of batches made during the selected time period for the chosen manufacturing site What raw materials impact system performance significantly The percentage of Process reliability Capacity utilized and schedule utilization achieved and opportunity for improvement Opportunity identification to reduce various manufacturing costs based on 1 improvement in process reliability as well as weighted volume produced Capacity Utilization Scheduled Utilization and Process Reliability facts and opportunity calculations Process reliability Based on the targets manually entered in the configuration the opportunity scope of improvement for the respective KPI is identified for the selected RegionPlantLine How is my plant doing and where are the problems Line 21 Analysis Unplanned Downtime is high for the line Inprocess measures impacting UPDT Line 21 is green on all the Inprocess measures except QA Completion Cost of production per MSU and opportunity is identified The opportunities identified are in par with the actual capacity for the selected RegionPlantLine and the Global Targets specified by the end user Volume and cost opportunity Planned and unplanned costs and number of production lines per day Based on the type of loss to be assessed the opportunities for losses along with changeover loss and the number of unplanned stops made at any plant per day is calculated. The targets are given in the configuration section Plant cost analysis and dynamic cost calculation to improve by a PR point The PR is calculated dynamically in proportion to the volume produced at the line and the respective opportunity is calculated. Scenario comparison between the fixed cost to improve a PR point versus dynamicactual cost to improve a PR point per line Cost per line and the PR loss at Line level Costs Supply cockpit Use case Supply chain cockpit for Global manufacturing users Final Supply KPI reports 60 KPIs categorized as Great People Great Plants and Great Pipelines at multiple levelsRegion Market Factory etc. and multiple periods Automated e2e calculations of KPIs at different levels as Quality People Supply Excellence Safety etc. Data Harmonizing in data bricks and tables setup in lake Flexibility to add new data or KPIs to the source Live data setup completion in Azure and automated pipeline to refresh data Business Problem and Objective Data Related Requirement Global Supply team wanted visibility across needs to develop a cockpit to get e2e visibility across supply chain network The cockpit will give visibility across assets people plants and their productivity Create a single source of truth as there were different reporting structures globally with different calculations logics and granularity across businesses and region Enable Supply Chain teams to use data to carry out self serve analytics Aligning KPIs across people pipeline plants and finance Fixed Asset Turnover Ratio Fixed Asset Addition TRS Adherence to Schedule Energy Water Intensity Total incident rate Loss time incident rate Right at First Time Logistics Conversion Cost Prod Conversion Cost Case Fill Critical Role Convergence Grand Mean Current Asset Turnover Ratio Great People Financials Great Plants Great Pipelines Fill rate ontime Factory Service Level Case Fill Rate Trade Attitude Survey Metric Engagement Score 20K 80K 80K 60K 40K Daily Plant Summary Trend Analysis Daily Network Summary Alerts Illustrative Snapshot 13 Non Periodic KPIs Production Line Periodic KPIs Global KPI Region Segment Customer Service Trends Snapshot 23 Loss BridgeMultiple Periods Market Performance Factory Snapshot 33 Mfg 360 We delivered 30 dashboards across multiple workstreams at various granularity levels Region BU country site levels Monthly quarterly yearly Category Brand Brand segments Executive insights across supply chain operations cost KPIs Vendor Supplier OTIF Total manufacturing cost Service Cost Effectiveness Severity total Health Safety environment Right at first time Quality CAPEX Cash Capital effectiveness Compliance to Schedule Customer OTIF Gross OTIF SFA Bias Gross Profit Net Profit Conversion PL by plants MR Labour parts services costs Waste costs Raw materials FG Fixed costs Total Accident Rate TAR Total Incident Rate TIR CO2 emissions Total Mfg waste Water incoming RFT sell composite Food safety total CTQ Consumer Complaints DIOH Inventory costs Opex Days Sales Outstanding Day Payables Outstanding DPO  Cash Conversion Cycle  Enabling 360 o visibility across multiple KPIs Days Between Next Run DBNR  Visibility to Compliance to Schedule metric across Regions BUs business categories and trends Visibility to Total manufacturing costs across Regions Categories plants and brands Visibility to Total Manufacturing Costs comparison w.r.t Plants SKUs Brands Regions BUs Sourcing Enabled buying decisions through improved price forecasting of Vanilla bean prices Essence manufacturer forecasted vanilla prices for Madagascar using traditional and intuitionbased approaches Current forecast methods was more time consuming and lacked the external variables which can potentially impact the future prices Automated data ingestion to harmonize disparate data sources to create a single source of truth for forecasting vanilla prices Leveraged advanced forecasting algorithm and automate execution to improvise on vanilla prices Self serve diagnostics Dashboards enabled to understand the trend seasonality patterns and of the vanilla prices Scenario planning Enabled users to simulate and work out the best possible scenarios in cases of business uncertainties Client ask Solution Result Inbound Supplier risks mitigation Improved visibility predictability of risk across Supply Network nodes with external internal data Impact assessment of risk across the nodes Prescriptive recommendations on alternatives and tradeoffs for risk mitigation strategies and Scenario planning Collaborative platform for visibility Real Time Visibility Early warning signals on risks Recommendations to mitigate risks Identified 150Mn opportunities through Inbound supplier risk prediction and traceability Client ask Solution Result Result Demand pooling for Optimized purchase volume frequency Optimal ordering quantity with schedule to reduce overall cost and prevent obsolescence Alerts and triggers on products obsolescence and expiry Classify products on the basis of shelf life and demand to prioritize buying Early warning signals to enable decision making at store level Recommendations on purchase frequency and volume at SKUstore level Annual savings of 1Mn per SKU group Scaled analytics product across categories Client ask Solution Result Total Landed costs for imports across regions with internal external data Explore ways to determine total landed costs from publicly available information Assess total cost to serve components Procurement Carrier SH and Duties with many subcomponents HTC codes across 3 Merchandising categories Driver analysis for total landed cost Modeled the Total Landed Costs through linear calculations Simulation to change routevendors to see probable cost impact 99 accuracy on category for Cotton Balls Absolute cost difference of 0.003 per unit was observed Client ask Solution Optimized Logistics Costs with e2e visibility cost drivers through AIML interventions Standardize definition of Logistics cost performance KPIs e2e visibility of cost components Standardize ways to attribute costs Forward looking view of costs rather retrospective view of costs Integrated Logistics data platform with reduced latency in data to decision making from 1QTR to 1 Week e2e cost performance management with descriptive predictive prescriptive analytics Drivers mix with customer level BBN models 25M reduction in Logistics costs through Transportation cost drivers improving routing guide improved dynamic spot price Client ask Solution Result Smart machines A manufacturing firm wanted to detect the anomalies occurring in the machines proactively and thus prevent the operational failures Business Problem Business Objective Analytics enabling smarter machines to help determine the anomalies real time Multi variate clusters on critical process parameters Determine thresholds dynamically Identify anomalies in real time Alert the machine operator Adaptive learning taking operators feedback Live Data Streaming Real time Analytics 1 2 3 Various data management and analytical components rendered to detect anomalies Multiple Signals Processing Real Time Processing Adaptive Algorithms Ability to evaluate multiple signals together Ability to process realtime Algorithms that adapt with live data Real time monitoring and clustering for anomaly detection Machine Learning Principal Component Analysis K means Clustering BigData technology Adaptive Algorithms Online Kmeans AutoEncoders Real time data from sensors were classified into clusters every 110 th of second to identify the potential machine with anomaly Data for 21 sensors of a machine measuring Temperature Pressure Oil Flow rate Water flow rate Etc... A reading is collected every 110 th of a second Real time streaming and clustering engine Output One output every 110 th of second 600 instances of data every minute 12000 data points every minutes UI fetc

es a instance of data every 110 th of second Real time streaming and plotting of clusters Plotting anomalies on bubble plots Taking user feedback for adaptive learning Solution helped business take appropriate corrective action against the machine and thus saving further operational time

CPG Future_ Fractal BCM Solution.pptx

BCM as a transformative solution A GoToMarket Shareout Business Continuity Planning for Supply Chain Resilience October 2022 Flow What is BCM in this context raw materials supplier risk 18 TH Oct22 Why is it so important today secondary research facts challenges post pandemic 18 TH Oct22 What kind of problems can we help you solve impact 19 TH Oct22 each case What kind Identification of raw material criticality View into supplier position risk qualification Monitoring and assessing Risk in BCP Recommending alternative materials suppliers Process eng e2e digitization DT of data systems business rules logic KPI Demo NO ACTION Pilot 6 to 8 weeks Shreya Who can we take this to PS BU COE Competition existing ERP solutions research Case studies Flow What is BCM in this context Why is it so important today  Disruptions not seen as a one off event Greater supply chain visibility efficiency and resilience are top of mind. changes in commodity prices supply chain bottlenecks continuing need to assess implications of choosing alternatives cost monitoring What kind of problems can we help you solve impact Identification of raw material criticality  View into supplier position risk qualification  Monitoring and assessing Risk in BCP   Recommending alternative materials suppliers  Process eng e2e digitization DT Demo Who can we take this to PS BU COE  Execution approach  Competition existing ERP solutions research Case studies  BCM Define criticality Assess supplier risk Assess BCP strength Identify subs materials Identify subs materials suppliers Business continuity management is critical for procurement and RD teams 01 02 05 03 04 A business users views and concerns pertaining to BCM planning execution Manufacturing RPM Material Planner Manufacturing RD leader Material quality Lead Procurement manager RegionalGlobal Procurement Head We dont adopt a projectionbased inventory monitoring Sourcing Spends are increasing post COVID Supplier OTIF within top 10 critical suppliers are dropping We focus on reacting but not anticipating There is no S2P data KPI visibility RM Quality notification trends have increased by 20 We bring bespoke services and accelerators to enable BCP risk identification mitigation Planning monitoring BCP strategy planning Supplier Material performance Design thinking approach Nireeha to add Enhanced E2E visibility to smoothen collaboration across nodes and post event analysis Risk prediction Alternative strategies Supplier material risks Criticality scoring clustering Single source vs multisource Alternative material suggestion optimization Alternate sourcing location supplier strategies Cognitive recommendations Cognitive automation Whatif simulations Smart automation with existing systems and landscape Cloud based planning capability to assess impact of various parameters and tradeoffs among scenarios How Might We help build a Business Continuity Plan and design a solution that _______ PRE DISCOVERY DISCOVERY SYNTHESIS IDEATION Understand current process of BCM Validate the current process Understand enduser painpoints gaps and needs through user focused research methods DESIGN FOR PILOT Define needs and opportunity areas and ideate on possible interventions Mapping painpoints and needs for ideation Design the solution as POC Pilot Give a sneak peak of the solutions Solution Flow Wireframes UI Screens Using Using Using Do we do some sort of discovery to understand the lay of the land Does the 68 weeks include development time as well Business and DataTech aspects to be added Walkthroughs and Sketches Identifying painpoints and needs DEVELOPMENT WIP What is BCM in this context BC for raw materials  what are my critical materials will I be able to source these materials from the right suppliers What substitutes do I have and supplier visibility who are my suppliers by critical materials What is their history with us Are they qualified to continue Therefore this business continuity plan is to be proactive rather than reactive to major disruptions in the inbound supply chain which are sourced by high impact supply risks KPI Secured Business Value Risk Exposure Rating x Business Impact Target is to Minimize risk exposure rating Process Steps prioritization of critical raw materials risk identification risk assessment and risk treatment. KPI to understand the completeness of your BCP Program secured business value Initial filtering Critical material Frame work Value at Risk Risk Exposure Rating x Business Impact Why is it so important today Disruptions not seen as a one off event Greater supply chain visibility efficiency and resilience are top of mind. changes in commodity prices supply chain bottlenecks continuing need to assess implications of choosing alternatives cost monitoring Supply disruptions may lead to severe costs for purchasing companies by causing supply chain delays which result in stockouts of raw materials and thus inability to meet customer demand and cost increases. Major disruptions may even threaten the very survival of a business therefore adopting a solid risk management approach is crucial Top executives at Global 1000 firms mention that supply chain disruptions and their associated operational and financial risks are their single most pressing concern Finch 2004. Supply disruptions result in severe costs for purchasing companies if handled poorly. They can cause supply chain delays which in turn trigger stockouts of raw materials and thus inability to meet customer demand and cost increases Blackhurst et al. 2005. Impact Radjou 2002 also lists a number of examples of quantifiable supplychain disruptions including General Motors experience of an 18 day labour strike at a brake supplier factory in 1996 which led to disruptions at 26 assembly plants with an estimated reduction in quarterly earnings of 900 million. Similarly Boeing experienced in 1997 supplier delivery failure of two critical parts with an estimated loss of 2.6 billion to the company. Likewise the lightning bolt that struck a Philips semiconductor plant in Albuquerque in March 2000 created a 10minute blaze that contaminated millions of chips and subsequently delayed deliveries to its two largest customers namely Nokia and Ericsson. As a result of this inbound disruption a 400 million loss was reported by Ericsson due to late chip deliveries from the Philips plant Latour 2001. How to identify critical materials impacted sales number of suppliers product grade count corresponding to supply chain complexity impacted customers corresponding to impacted reputation number of purchasing plants corresponding to supply chain complexity etc. Risk profile for supplier poor financial health of the supplier flexibility constraints of the supplier market constraints low number of qualified suppliers high lead time supplier being under no legal liability and geographical density of suppliers. One of the most effective ways of implementing risk assessment is to introduce risk registers Patterson 2002. Risk registers are tables that include information on the cause of a risk i.e. the risk source the risk event the outcome of the risk statement the risk consequence supply disruption and the impact and the probability of risk. Risk registers prove to be useful as a risk assessment tool as they provide an overview of all the active risks and their risk rankings. Enabled Recipe Mix Recommendation for the biggest US FMCG CASE STUDY Objective To minimize the complexity and diversity of Raw materials strengthening supply chain resilience and supporting the business c ontinuity p lans by recommending raw material in manufacturing for RD team to help them choose an alternate to prepare the final product Impact This will lead to a reduction of specs and higher share of market standard specs which will enhance buying power through volume A more competitive supply base therefore drive productivity which results in overall cost reduction Recommendations Recommendations for RD SMEs to replace a complex Spec with another better Spec based on similarity which helps in times of shortages and cost reduction Automated data workflow for seamless process integration output which results in elimination of manual clustering work Challenges Usage of similar raw materials with price variations which leads to increase in overall costs Shortage of certain raw materials in production which causes clear to build issues Solution Feature vectors Cleaning transformation and TFIDF to get feature vectors Clustering To a ssess the composition similarity between raw materials using hierarchical clusters Similarity Scores T o identify the most similar spec against each of the other specs within a cluster Model Explanability T o understand how why and with which features the model reaches a decision Shobhit to read Raw Material Specs Comparison based on the compositions VolumeSpend Distribution in selected Cluster Analyze CostSpend Variation to enable RD team to shortlistsubstitute Raw Materials in each region Hierarchical and KMedoids clustering approaches were followed to cluster the raw materials Spec distribution Clusterwise Raw Material comparison taking composition description spend and volume  Assess Similarity between individual Raw Materials using cosine similarity score Enables Raw Material substitution incase of shortage supply chain disruptions Spec comparison Supplier Uncertainties across value chain CASE STUDY Objective To improve endtoend experience of our users for strategic tactical supply chain resilience planning via proactive holistic decision making  to ensure business continuity which helps us to Identify Assess and Mitigate risks Impact 360 visibility into current and historic spend Identify suppliers that are most at  risk Mitigate risks associated with product availability and improve customer service Recommendations Generate multiple scenarios to visualize whatif state of the spend allocation and r ecommend multiple alternatives Suppliers to tackle the incoming risk Higher negotiation control to enable strategic partnership with lowrisk suppliers Risks Is there any opportunity to reduce overall yearly spend for product portfolio across suppliers Which factors to be considered to perform supplier risk evaluation and selection How to monitor and control supplier performance to track ontime infull delivery across value chain Solution Optimal spend allocation using forecasting optimization and scenario planning Determine supplier risk clusters based on risk profilesscoresfactors Predict risk severity occurrence its impact on OTIF Shobhit to read Data Requirement Plant level data SupplierVendor level data Qualified supplier table Critical Material Information Risk Tiering Supplier BCP Material composition Material Properties Raw materialProduct mapping Sales Product Data External Data Supplier Network Data Requirement VendorSupplier Manufacturing Consumption PropertiesDimension What is BCM in this context BC for raw materials  what are my critical materials will I be able to source these materials from the right suppliers What substitutes do I have and supplier visibility who are my suppliers by critical materials What is their history with us Are they qualified to continue Therefore this business continuity plan is to be proactive rather than reactive to major disruptions in the inbound supply chain which are sourced by high impact supply risks KPI Secured Business Value Risk Exposure Rating x Business Impact Target is to Minimize risk exposure rating Rethink the name Supply Resilience with Business Continuity Planning Nireeha Business continuity management is critical for procurement and RD teams 01 02 05 03 04 Shobhit Minimize text Check for sources articles Add some pointers from FLOW Business persona questions Manufacturing RPM Material Planner Manufacturing RD leader Material quality Lead Procurement manager RegionalGlobal Procurement Head We dont adopt a projectionbased inventory monitoring General Efficiency trends across lines are dropping Manufacturing plants are having low Compliance to Schedule We focus on reacting but not anticipating There is no S2P data KPI visibility We are unable to predict when MRO inventory norms S iddhartha to help explain the BQs Business persona questions BCM Define criticality Assess supplier risk Assess BCP strength Identify subs materials Identify subs materials suppliers Data Requirement VendorSupplier Manufacturing Consumption PropertiesDimension Demo Snippets from the Demo Nireeha Summary tiering Vendor qualification risk assessment Take substitute tool screen from Siddhartha Who to take this to What is the typical execution approach Competition Key Stakeholders and Org Functions Product Supply Team Business Units who would like to start their own BCPs Centre of Excellence Think of kind of problem and where they are at Full scale process engg digitization Recommend step 1 and 2 1 MVP Pilot 2 Scale What exists today BCP solutions can cater to multiple stakeholders and can be built in a phased manner Key Stakeholders Organisation Functions Centre of Excellence Product Supply Teams Business Units who would like to start their own BCPs 1 Execution Approach 2 3 Understand the problem at hand and where the group is at Phase 1 Pilot covering key part of the BCP process using a design infused approach Phase 2 Full Scale covering end to end BCP process with full scope engineering and digitization Typical solutions offer e2e risk management however analysis and plan creation is a niche Case Studies Transfer Pricing Map View Existing raw materials inventories by country location size of circle inventory amount Description of transfer pricing policies and standards by country Listing of available raw material suppliers by location and their payment terms Table View Planned supply volume and price for material from SOP incl. supplier payment terms consuming location Data entry simulation where changes could be made to source units or price Results screen to show outcomes of transfer pricing scenarios along with potential additional costs e.g. logistics and benefits incl. recommendations FINAL DO NOT EDIT THIS SECTION What is BCM Enable organizations to respond to events in such a manner that critical business functions can continue Target is to minimize risk exposure rating Proactive vs Reactive Focus Raw Materials Supplier visibility What are my critical materials Who are my suppliers Will I be able to source these materials from the right suppliers What is their history and associated risks What substitutes alternatives do I have What is BCM in this context BC for raw materials  what are my critical materials will I be able to source these materials from the right suppliers What substitutes do I have and supplier visibility who are my suppliers by critical materials What is their history with us Are they qualified to continue Therefore this business continuity plan is to be proactive rather than reactive to major disruptions in the inbound supply chain which are sourced by high impact supply risks KPI Secured Business Value Risk Exposure Rating x Business Impact Target is to Minimize risk exposure rating Rethink the name Supply Resilience with Business Continuity Planning Nireeha Can be deleted once previous slide is done Why BCM now 01 02 03 04 Business continuity management is critical for procurement and RD teams 01 02 05 03 04 Shobhit Minimize text Check for sources articles Add some pointers from FLOW Can be deleted once previous slide is done Steps prioritization of critical raw materials risk identification risk assessment and risk treatment. Wha t does Fractal Offer Risk assessment mitigation Identify material risk supplier risks Tier your materials and supliers Assess impact to business plan for Alternate sourcing location supplier strategies Alternative materials Planning and Monitoring Create BCPs Enable multi stakeholder collaboration Enable testing and monitoring E2E Digitization Process analysis and design standardization Digitize to gain e2e visibility for BC planning Shreya Can we deleted once next slide is done Risk assessment mitigation Identify material risk supplier risks Tier materials and suppliers by criticality Assess impact to business Identify alternate sourcing location supplier strategies alternative materials Fractals Solution Areas Planning and Monitoring Create BCPs Enable multi stakeholder cross functional collaboration Enable simulated testing E2E Digitization Process analysis and design standardization Digitization for e2e visibility and BC planning Shreya Data Requirement Shobhit to update Data Requirements Data Requirements Conceptual Dashboard LANDING PAGE to view critical materials by Type Criticality Product Region impact Status of the business units plan Detailed view of each critical material Conceptual Dashboard Tiering View historic history Tiering defined by all departments Define tier for each critical material Review conflicting tiers Collaborate live on one platform Conceptual Dashboard Supplier Risk History of supplier Status of qualification Conceptual Dashboard BCP Analyzer View factors determining risk Plan health by factor Recommendation on action BCP solutions can cater to multiple stakeholders and can be built in a phased manner Key Stakeholders Organisation Functions Centre of Excellence Product Supply Teams Business Units who would like to start their own BCPs 1 Execution Approach 2 3 Understand the problem at hand and where the group is at Phase 1 Pilot covering key part of the BCP process using a design infused approach Phase 2 Full Scale covering end to end BCP process with full scope engineering and digitization Fractal Solutions Delivered Logo Objective Logo Objective Logo Objective Shreya to add Nireeha to clean up Competitor What are the Top Supply Chain Management Software E2open SAP SCM Logility Perfect Commerce Oracle SCM Infor SCM JDA SCM Manhattan SCM Epicor SCM Dassault Systemes SCM Descartes SCM Highjump SCM IFS Watson Supply Chain BluJay SCM are some of the examples of best Supply Chain Management Software. Shobhit Keep only the top competitors Updated on next slide Competitor SAP SCM Oracle SCM Dassault Systems SCM Watson Supply Chain are some of the examples of Supply Chain Management Software. Provides 1 View into current ecosystem 2 Future order planning 3 Inventory levels Transportation planning Missing 1 Planning for what if scenario 2 AI material criticality benchmarking 3 AI Recommended alternate materials Custom Test plans Cost Disadvantage 1 License cost is by of users 2 Customization costs are very high Majority of the solutions today provides Supply chain analysis but provides zero outlook into robustness of your supply chain. Business Continuity Plan is not a priority of these solutions. Appendix Data Requirements Data Requirements Potential data landscape to consider PRODUCT MARKET FIT DESIGN EXECUTION MARKET EXECUTION AGILITY TO RESPOND DRIVERS OF LAUNCH EXTERNAL FACTORS REFERENCE Strategic Intent Product positioning Order of entry Product attributes Ingredients Flavor profile Packaging Benefits Concept testing scores  Raw material quality Packaging Labeling Health and wellness positioning Sustainability positioning Distribution Pricing Trade Spends Media Spend Competitive Launches GDP growth rate Per capita income Inflation Population and population density Regulatory compliance Ratings and Reviews Consumer sentiment Social chatter Feedback from D2C platforms snacks.com pantryshop.com

Direct Store Delivery & Inventory Allocation.pptx

Direct Store delivery Inventory allocation May 2022 01 Direct Store Delivery Benefits of enhanced fulfilment process Key steps to enhance fulfilment Product classification to identify suitable products for direct store delivery Order fulfilment through direct shipping of ordered DSD products from plant based on optimized DSD policy else through DC Guidance on inventory to hold at plant for direct store delivery and replenishment of products at DC to p rovide supply coverage to urgent normal orders of DSD products and for nonDSD products Order allocation optimization to provide consolidationwhat of items to be fulfilled from plants or DC based on shipment size Inventory levelsage at Plant DC fast turnover high consumer demand items   current inv. levels at stores Highlevel actions for an agile and costeffective fulfilment approach As fashion products are perishable in nature due to short demand cycle. Higher number of direct shipments leads to shorter lead time thus reduces overall time to market Direct shipments reduces to stocking of DSD inventory at DC leads to overall capital blockage and holding cost Guidance DSD fulfilment and optimized order allocation provides agile fulfilment to stores at reduced overall fulfilment cost Additional revenue by selling seasonal goods and fast turnover products. Short time in delivering new products to markets. Gives manufacturer regular market insights prices and trends. Preventing outofstocks. Savings on transportation costs. Key criteria to identify products for Direct Store Delivery Demand variability Products with stable or near stable demand trend make fulfilment process cost effective by predicting future demand much accurately Higher demand variability tends to increase urgent orders in system that leads to stock high buffer stock at DC Revenue contribution Group of fewer products contributes high in revenue and volume would requires optimum efforts for achieving business goals. Visibility of high margin products provides any loss on sales vs overall profitability Growth Market events Fast moving and growth in recently introduced products enables to predicts the SKUs which going to contributes to sales predominantly Solution construct Define data requirement input constraints Acquire required data build views Data transformation in required form for optimization model Products segmentation for direct store delivery to fulfill store orders from manufacturing plant Product with high in revenue with low demand variability and high RFM score or fastmoving can be qualify for Direct to store Delivery Develop mathematical and heuristics models for arriving at optimal replenishment plans At DC to fulfill nonDSD product orders and urgent DSD orders Quantity of DSD products to be stock at plant based on future orders and plant stock capacity Optimization of order allocation for urgent last minutes orders of DSD products from PlantDC and all nonDSD products from DC based on Current inv. Agelevels at DC Required delivery date Inventory levels at stores Order size Production Capacity Inventory stock at plant   High Inventory turnover products Solution construct P lan delivery routes automatically optimize delivery schedules and handle ondemand orders along with scheduled orders with rerouting on the go. Planning the shortest and most optimal routes for multiple delivery locations S orting daytoday delivery packages and improve distribution efficiencies in Direct Store Deliveries.. Routes can be printed on the shipping label to eliminate the manual dependence Product segmentation for direct store delivery Required information Product segmentation Final Interpretation Historical Demand Sales order history Retail store sales history Store geographical hierarchy Cost matrix Production cost Transportation cost Custom charges etc. External factors Seasonality Offers on weekends Holidays Promotion LocalNational Events ABC classification Using KMeans clustering XYZ classification for demand variability profit RFMFSM classification for product demand activity Vector Auto RegressionVAR for future consumption trend Ensemble modeling Multidimension segment grid to select product with high in revenuevolume AB with low demand variability XY and high RFM score or fastmoving can be qualify for Direct to store Delivery VAR modeling to capture the change in dynamics of future sales proportion of products. Classification modeling works with historical sales VAR model is to validate and change sales accordingly Twodimensional segmentation of product for illustration Replenishment Output Ship from PlantDC ShiptoStore Quantity Replenishment cycle Shipment Cost breakup Item Shipment volume DIFC Min Max boundary Shipments Lanes RDC details Item No. product group Size Color KPIs Cost of fulfilment Customer Service level Inventory levels Lead times Publish layer Consumption layer We will consider multiple layered approach to arrive at solution Cost Parameters Manufacturing cost Sales price Holding cost at DC Logistics cost Stockout cost Production Stock Production plan Throughput lot size Stock cover Product category DSDnonDSD Open RQ Priority Inputs and constraints Stores input Store stocking parameters Store geographical hierarchy Optimization of Orders Allocation Design Mathematical Model considering required objective constraints and business inputs Objective1 Maximize DSD SKUs from the plant Obj2 Minimize cost of fulfilment to stores Obj3 Minimize lead time to stores Optimize for the selected multiobjective function and measure impact on supply chain KPIs Constraints DC constraints capacity docks inventory Plant constraints Production capacity and plan finish good holding capacity line orders constraint Cartons pallets constraint Lane constraints Carrier constraints Enabled dynamic order fulfilment for eCommerce for the biggest US sporting goods retailer 1 Background and Challenges 2 Objective Sub optimal inventory fulfilling BOPIS eCom orders Ship from store Ship from DC Extend distribution network 800 stores 1 MFC as extended fulfilment centers with 2 additional MFCs Increased fulfilment costs due to splitshipments and long transit times Lack of workbench to simulate order fulfilment options from upcoming new stores Our 3step solution framework to optimize order fulfilment for quick turnaround Increased 1 day transit shipments by 40 reduced costs by 20 Business Benefits 99 shipments are within 1 2 transit days 95 MFC shipments from earlier 30 90 shipments within zone 2 4 from earlier 70 2018 Fulfilment by store type 2019 Fulfilment by store type 2018 Fulfilment by transit time 2019 Fulfilment by transit time Direct Store Delivery Plant All GTIN All Customer All Priority All FY Quarter All All Month All Product Segmentation Direct Store Delivery Plant All GTIN All Customer All Priority All FY Quarter All All Month All Gross Margin Volume Direct Store Delivery Plant All GTIN All Customer All Priority All FY Quarter All All Month All Products Products Sold To Ship To All All Illustrative purpose only Dashboards DSD Orders Customers GTIN Inventory Revenue Deliveries Settings Last 1 month Plant GTIN Customer Priority FY Month Quarter Revenue 5000 GM 70 All All All All All All All Plant All Plant All Month All Illustrative purpose only Dashboards DSD Orders Customers GTIN Inventory Revenue Deliveries Settings Last 1 month Product Category Sold To Ship To FY Month Quarter Revenue 5000 Volume 70 All All All All All All Last 1 month Plant Sold To Ship To Date All All All All Direct Store Delivery Summary Manufacturing Plant Manufacturing Plant For example the visuals should be able to answer which items are candidates for DSD why are they candidates What is the comparison of shipping these items DSD instead of from the DC impact on cost time etc.  DataPoints Challenges Highlevel actions for an agile and costeffective fulfilment approach Careful planning and management of delivery fleet is vital Rising transportation costs Lack of packaging and shipping expertise Lack of coordination on delivery status product inventory pricing changes Visibility and monitoring issues across retail locations Accommodate realtime order requests or dynamic changes in delivery schedules 02 Order allocation engine Potential enhancements Current process Manual selection of order lines based on filtering ageing orders for the desired customers No visibility into overall marginrevenueinventory holes of the order lines being selected Selection based on heuristics Eg 100 order lines1 truck. No way to optimize selection for target of trucks Due to the manual nature of the process no visibility into the tradeoff of selecting order lines based on different objectives age vs revenue vs margin vs holes Our understanding of the current allocation process Multi objective allocation considering ageing revenue margin and inventory holes to allocate orders Define a cost metric for singlemultiple objective that need to optimize in order to achieve effective dynamic order allocation model A user guided simulationbased tool to achieve optimal allocation for desired objective to achieve Work in sync with the load build solution to create an endtoend automated analytical solution Solution construct to optimize order allocation Order Allocation Solution Select Date Range Revenue Target Margin Target of SKUs of Trucks Available Soldto Shipto All Truck Type All Brand All Style All Style Family All Order Allocation Solution Summary DC 20M Revenue 50 Margin 45 Holes 600 Distinct SKUs Summary View Order Line View Soldto Shipto All Truck Type All Brand All Style All Style Family All Order Allocation Solution Order Line DC 20M Revenue 50 Margin 45 Holes 600 Distinct SKUs Summary View Order Line View Order Allocation Solution Customer Order Allocation Solution Customer Run Date DC Soldto Shipto All Truck Type All Brand All Order Allocation Solution Targets Style All Style Family All 20M Revenue 50 Margin 45 Holes 600 Distinct SKUs Revenue Holes Margin Run Date DC Soldto Shipto All Truck Type All Brand All Style All Style Family All Revenue Margin Holes Distinct SKUs Across all Metrics Order Allocation Solution Trade Offs Objective function Maximize the composite score across age revenue margin and holes Output Order allocation KPI and tradeoffs Connection to E1 for automation Dynamic order allocation model to optimize selected criteria Inventory Allocation Order Allocation model Problem Formulation Improved order allocation process for leading CPG client resulting in increased service levels at retail stores Challenge Less efficient manual order allocation process led to high OOS rates at retailers 14 of retailers in North America 70 of supply nodes 3.5 K SKUs Supply chain disruptions i.e. COVID Solution Components Impact delivered 90 Contractual Adherence while maximizing service levels 2 0 Workforce efficiency by elimination of cumbersome manual work Optimized inventory levels based on shelflife intelligence KPI Insights Intelligent allocation Cross functional collaboration Integrated and automated Scorecards Intuitive dashboards for KPI monitoring Automated diagnostics and exception management Early warning systems Intelligent allocation Allocate right SKU quantity to the right customer Simulate scenarios and final decisions C ross functional collaboration Enable weekly allocation decision process with intelligent tools Facilitate cross functional collaboration through integrated platform Integrated and automated Integrated with legacy systems Automated robust for data ingestion Scalable across new categories customers regions Transformed order allocation process KPI Insights KPI Insights Intelligent Allocation Integrated automated Cross functional collaboration Retailer scorecard for risk monitoring Drill drown and deep dive for evaluating category performance KPI Insights Intelligent Allocation Integrated automated Cross functional collaboration Exception management to identify products with constrained supply Improve allocation decisions with advanced optimization Simulate allocation Conduct simulations to view change in total delivery costs service inventory levels Assess tradeoff between different scenarios via automated scenario planner Intelligent order allocation Allocation decision based advanced multiobjective optimization algorithms i.e. improve service levels and sales reduce costs Combine business heuristics and s with information of retailer inventory product shelf life quota and order customer prioritizations Connected systems Integrate allocation recommendation at retailer level with legacy systems Build automated seamless system to upload allocation actions KPI Insights Intelligent Allocation Integrated automated Cross functional collaboration Constraints Inventory availability Order priority Target service levels MOQEOQ Contract clauses Storage capacity Input Parameters Forecast Open orders Scheduled receipts Inventory shelflife OnHand Qty Substitute products DC capacity Replenishment Visibility Optimum customer order allocation principles with maximize service levels E2e transformation of reporting allocation KPIs to provide insights on completeincomplete orders Tradeoffs between different optimization objectives to aid decision making Build right shipment projections for better warehouse and logistics planning Optimization Optimize fulfilment process by defining objective functions Obj 1 Maximize service level Obj 2 Minimize cost Explored optimization techniques like Linear programming GA simulated annealing Flexibility to incorporate business heuristics dynamics KPI Insights Intelligent Allocation Integrated automated Cross functional collaboration and operations research techniques Enabling weekly allocation decisions through cross functional collaboration Monday Tuesday Wednesday Thursday Friday Review results Review analyze performance scorecards Action on learnings from previous week Supply chain tactical direction setting to max supply availability Allocation outcomes review Review internal scorecard and allocation results Inweek order adjustments Discuss customer and order risks and opportunities Demand Supply reconciliation Review supply demand and inventory change scorecards SOE discussion Share allocation outcomes and shifts in demand mix Discuss inventory levels and any production changes Align on tradeoffs and actions from supply and commercial Integrated Tactical Planning Align on changes to production plan Summarize supply chain actions Action on learnings and outcome of tradeoffreconciliation meeting Supply Chain Sales Customer service KPI Insights Intelligent Allocation Integrated automated Cross functional collaboration Automated and connected systems for seamless data to decision journey Data Ingestion Process Organize Sample Business Questions Historic purchases retailer Quotes Supply and fulfilment Retailer inventory Volumes Source Types Manual Collection Forms ExcelCSV Entries ERP Advanced planning suites SnowflakeDB Schema What is the OOS performance in top retailers How should inventory be allocated to retailers in short term and longterm supply constrained situations What is the root cause and drivers for poor OOS performance Lead time value forecast contracts What categories are driving high OOS Data Mapping Use column names that can link tables through the keys Create a schema that comprises the Dimension and Fact tables Calculate derived metrics as per requirement of the subsequent analysis Use this base schema for modelling and visualization Data Harmonization In this layer we map the data sets and nomenclature followed in different functions of the organizations Data Mapping Transformation Sources Create SharePoint forms with input criteria for data capture Migrate to SharePoint for input criteria entry collection Use ETL jobs to make transformations and upload to cloud Use ETL jobs to create derived tables that can used for analysis Usecases KPI Insights Intelligent Allocation Integrated automated Cross functional collaboration Improved CPG clients order allocation process for priority customers Current scenario Whats the need Inventory allocation across customers is done at EOD each day by running GATP tool Customers are prioritized in categories A1 A2 A3 B1 B2 B3 etc. Current GATP tool considers constraints like Product substitution customer priorities MSTN etc. The inventory allocation for same day delivery customers is not efficient Efficient optimal ATP allocation for priority customers to enable same day deliveries Running optimizer on hourly basis to allocate inventory on the fly Incorporate multiple constraints like Service levels Product substitutions Inventory availability MSTN max stock to norm ratio Customer priorities Inter depot stock transfers What are the technical enablers Robust Advanced Analytics to optimize inventory allocation Standardize Optimization model to facilitate hourly inventory allocation shipment Interactive visibility solution to showcase future trend in inventory availability Ability to handle dynamic factors that could influence inventory allocation Allocation optimization Deep dive Output Hourly inventory allocation plans across open customer orders Same day delivery feasibility MILP Simplex or LPP based optimization Sample data requirement

Drug Similarity.pptx

Inventory Substitution by Drug Similarity Harini and Harshita 16 th Aug22 . Agenda Usecase Creating an approach on Inventory Substitution for Drugs Drug Substitute based on Similarity Main questions in this research How do we understand the properties of drugs What are the factorsproperties that needs to be considered for drug similarity How do we decide on drugs which are similar What can we recommendsuggest drugs using Similarity Benefits Recommend drugs that can be used as substitutes in the inventory Can discard drugs if they are not used Can eliminate those poor druglike compounds in advance and avoid more research and development expenses High Level challenges C ost of innovation and evolution R esearch and development expenses Effective Inventory control substitution Ways for Inventory Substitution To be effective pharmacists require to have an effective inventory control system that will Account to make substitutions . Find the demand for a drug within a set of medicines that share an active component and offer the same therapeutic effect. Drug Repositioning  Aims to identify new indications for existing drugs offers a promising alternative to reduce the total time and cost of traditional drug development. T o find potential new uses for existing drugs and apply the newly identified drugs to the treatment of diseases other than the drugs originally intended disease drug discovery The novel use of a drug is based on the assumption that if two diseases share some similar treatment profiles then the drugs used for only one of the two diseases could also be used for the other. By integrating drug or disease features information with known drugdisease associations the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed and they are incorporated into a heterogeneous network with known drugdisease interactions. Automate replenishment by moving existing demand within a specific group of drugs from one to another when different manufacturers make orders be it because of a change in the preferred state or take in account availability or pricing problems. Pharmacists cannot simply substitute an instock item like the prescribed medicine for one that is out of stock. Suppose a medication is unavailable and the pharmacist cannot supply it. In that case they will require consultation with the medical professional who prescribed the medication to determine what substitutions are and arent suitable for the client Questions Challenges How do we decide on drugs which are similar How do we understand the important properties of drugs What are the various properties of drugs in terms of structure functionality etc What are the set of drugs labelled Preferred amongst the gambit of generic drugs How do we identify drugs that may not be suitabledangerous for switching The FDA has identified certain drugs that may be more dangerous to switch called narrow therapeutic index  NTI drugs Combinatorial drugs used in treatments estimate interactions amongst drugs combination changed for patients with allergic reactions .What drugs can be combined or substituted for each other A candidate drug with what kinds of elements and element proportion may have a greater possibility to be developed into an approved drug How do we deal with mixed data How can we come to conclusion on drugdrug interaction How do we deal with very less variability in the data What is their proper proportion Labelled data for similarity amongst existing drug types how to assess similarity amongst new drugs Choose drugs Drug lifecycle Initial Growing Maturity Sustain Decline Obsolete Based on upper and lower limit choose drugs which can be used as substitutes Clustering Perform text embeddings feature vectors Drug2vec scaling onehot techniques for mixed data and arrive at important vectors by feature selection methods Perform clustering model on the selected features to cluster the drugs Similarity Similarity within each cluster based on Drug Structure Drug functionality Drug Ingredients By drugdisease interactions drug related similarity disease related similarity etc Weighted similarity score Recommendations Rule based workflow on top of similarity scores to suggest drugs Solution Approach Drug representationlifecycle filtersFeature selectionClassification algorithmSimilarity calculationsdistance probabilistic correlation association coefficients Recommendations Calculating Similarity Similarity based on Drug Structure Drug functionality Drug Ingredients By drugdisease interactions drug related similarity disease related similarity etc Weighted similarity score Calculating Similarity for DDIs Integrated SimilarityConstrained Matrix Factorization ISCMF to predict DDIs . Eight similarities based on the drug substructure targets side effects offlabel side effects pathways transporters enzymes and indication data as well as Gaussian interaction profile for the drug pairs are calculated. Subsequently a nonlinear similarity fusion method is used to integrate multiple similarities and make them more informative. Finally we use ISCMF which projects the drugs in the interaction space into a lowrank space constrained to obtain new insight about DDIs . Drug repositioning data and approach Data requirements   Drug substructures D isease phenotypes P roteinprotein interactions PPI G ene profiles network profiles Approach C alculate similarity scores as well as drugdisease treatment priori. T he similarity matrices and the priori are integrated to construct drugdisease pair graph. Finally Semi Supervised Graph Cut algorithm is applied to predict drugdisease treatment relations How do we quantify Similarity If two drugs exhibit similar activities across multiple disease functionality then they are likely similar If two drugs has similar properties then they are likely similar structural shape color size pattern composition. Structurally similar molecules are assumed to have similar biological properties. Similar biological propertiesDrug discovery. Compounds protein binding site in the protein dockinggenerate many possible conformations of the compounds in the binding site Drugrelated similarity data has 10 networks based on   1 chemical structures 2 target protein domains 3 GO target protein annotations a target protein interactions b side effects c chemical structures 4 GO molecular functions 5 GO biological processes 6 GO cellular components 7 metabolism enzymes 8 protein sequences 9 anatomical therapeutic chemical classification codes 10 drug pairwise interactions. Diseaserelated similarity data has 14 networks based on   1 curated genes 2 HPO genes 3 literaturebased genes 4 curated variants 5 literaturebased variants 6 microRNAs 7 long noncoding RNAs 8 HPO phenotypes 9 ISA taxonomy 10 informationtheoretic similarity 11 GO terms 12 implicit semantic similarity 13 semantic and gene functional 14 curated association type ontology. Data Requirements Type Functionality Drug Molecular Structure Chemicalbiological Activity Descriptor Representation Patterns Chemical composition Date Data Requirements Categorical functionality functional group disease type cure sideeffects Text description Nature feedback sideeffects Numerical dosage sales composition proportion Chemical formula Structure Ingredients Protein No of ringsaromaticnonaromatic amino acids etc Substitution patterns Molecular Number of aromatic atomsNumber of atomsNumber of heavy atomsNumber of hydrogen atoms Number of aromatic bondsNumber of bondsNumber of double bondsNumber of rotatable bondsFraction of rotatable bondsNumber of single bondsNumber of triple bonds Composition made of chemical components Data Requirements Mockup Drug Representation Chemical representation Molecule similarity Protein structure onehot How do we choose the Suggestion High Level challenges Key Challenges in Pharmaceutical Industry 1 Demand forecasting enhance their customer service levels.. Ensuring that all the goods are produced at the desired time and delivered seamlessly poses a big challenge to the pharma companies. 2 Risk management Managing risks in the drug manufacturing process and quality systems is of great importance.Hence it becomes challenging for the pharma companies to identify possible risks associated with a product or processes involved in manufacturing development and distribution of the product. Limited clinical trials during the pandemic C ost of innovation and evolution Disruption in Availability of Raw Material for generic drugs Ways for Inventory Substitution To be effective pharmacists require to have an effective inventory control system that will Account to make substitutions. Find the demand for a drug within a set of medicines that share an active component and offer the same therapeutic effect. Drug Repositioning  Aims to identify new indications for existing drugs offers a promising alternative to reduce the total time and cost of traditional drug development. Automate replenishment by moving existing demand within a specific group of drugs from one to another when different manufacturers make orders be it because of a change in the preferred state or take in account availability or pricing problems. Pharmacists cannot simply substitute an instock item like the prescribed medicine for one that is out of stock. Suppose a medication is unavailable and the pharmacist cannot supply it. In that case they will require consultation with the medical professional who prescribed the medication to determine what substitutions are and arent suitable for the client pharmacies must be able to set up their inventory control system to recognize which manufacturers have received the preferred designation automatically or to use preestablished criteria such as prices or availability of to determine which business is the best supplier to place an order with. Based on past sales data a sophisticated system might translate the general demand for a category or the requirement for the selected product to the desired result Challenges How do we decide on drugs which are similar existing demand can only be transferred within a particular group of medications from one to another. What are the set of drugs labelled Preferred amongst the gambit of generic drugs keep track of changing local rules How do we identify drugs that may not be suitable for switching The FDA has identified certain drugs that may be more dangerous to switch called narrow therapeutic index  NTI drugs which may warrant further drug blood monitoring after a generic to branded drug substitution. What are the various properties of drugs in terms of structure functionality etc How do we deal with mixed data How do we understand the important properties of drugs How can we come to a conclusion on drugdrug interaction How do we deal with very less variability in the data W hat kinds of elements are beneficial to the druglike properties What is their proper proportion A candidate drug with what kinds of elements and element proportion may have a greater possibility to be developed into an approved drug Combinatorial drugs used in treatments estimate interactions amongst drugs combination changed for patients with allergic reactions .What drugs can be combined or substituted for each other Labelled data for similarity amongst existing drug types how to assess similarity amongst new drugs KGDDS A System for DrugDrug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation PubMed nih.gov High level of challenges pharma industrysubstitute Drug Substitution an overview ScienceDirect Topics Questions How do we decide on drugs which are similar existing demand can only be transferred within a particular group of medications from one to another. What are the set of drugs labelled Preferred amongst the gambit of generic drugs keep track of changing local rules How do we identify drugs that may not be suitable for switching The FDA has identified certain drugs that may be more dangerous to switch called narrow therapeutic index  NTI drugs which may warrant further drug blood monitoring after a generic to branded drug substitution. What are the various properties of drugs in terms of structure functionality etc How do we deal with mixed data How do we understand the important properties of drugs How can we come to a conclusion on drugdrug interaction How do we deal with very less variability in the data W hat kinds of elements are beneficial to the druglike properties What is their proper proportion A candidate drug with what kinds of elements and element proportion may have a greater possibility to be developed into an approved drug Combinatorial drugs used in treatments estimate interactions amongst drugs combination changed for patients with allergic reactions .What drugs can be combined or substituted for each other Labelled data for similarity amongst existing drug types how to assess similarity amongst new drugs KGDDS A System for DrugDrug Similarity Measure in Therapeutic Substitution based on Knowledge Graph Curation PubMed nih.gov High level of challenges pharma industrysubstitute Drug Substitution an overview ScienceDirect Topics Similarity for drug repositioning The goal of drug repositioning is to find potential new uses for existing drugs and apply the newly identified drugs to the treatment of diseases other than the drugs originally intended disease drug discovery the novel use of a drug is based on the assumption that if two diseases share some similar treatment profiles then the drugs used for only one of the two diseases could also be used for the other. I dentify potential novel indications for a given drug. By integrating drug or disease features information with known drugdisease associations the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed and they are incorporated into a heterogeneous network with known drugdisease interactions. Based on the drugdisease heterogeneous network BiRW algorithm is adopted to predict novel potential drugdisease associations. Overlapped KEGG pathways between Alcohol dependence and the predicted drugs. The blue hexagon nodes represent drugs predicted to treat Alcohol dependence the red vee nodes represent overlapped KEGG pathways between drugs and Alcohol dependence

Fractal_MedTech Supply Chain Art of Possible.pptx

Supply Chain Analytics solutions for MedTech industry Art of Possible June 2022 01 02 Transforming MedTech Supply Chain Art of Possible Relevant capabilities case studies Topics 01 Supply Chain Analytics solutions for MedTech industry Art of Possible Changing dynamics in MedTech industry.. Enable digitization Transforming from Doing digital to Being digital to provide connected solutions and achieve operational excellence Advance patient centricity Transitioning to patient centric ecosystem to empower and engage patients throughout their health journey Improve flexibility and sustainability Engaging in stresstesting operating models and increasing transparency to strengthen resilience and make the operations sustainable and future ready Drive value amongst customers Evolving from a traditional supplier role to a valuedriven strategic business partner to their customers posing critical Supply chain challenges 01 02 03 04 Transforming MedTech Supply Chain Art of possible Risk Resilience Maximize efficiency Control Tower End to end visibility across MedTech lifecycle Suppliers RD sites Production sterilization sites Distributors 3PL GPO Hospitals Pharmacies Enable cross functional decision making for ex. RD pre and post device launch analytics IT and design groups for demand at risk future service levels warning alerts Predict risk of delayed supply shortages and their impact to build assemble specifically complex devices Alternative options of Supply Chain planning to counter the disruptions Mitigation plans for delays through recommendations at Plant DC or HCOs such as Pharmacy Hospitals etc. Optimize inventory levels based on product portfolio Premium high value low velocity ex. Devices stents Commoditized low value high velocity ex. syringes Improved service levels by optimizing allocation decisions and thus minimize penalties Failure to Serve FTS warning alerts Optimize production and distribution processes to minimize supply chain inefficiencies across multimodal nodes Device components optimization based on criticality and Predictive maintenance to proactively predict a device failure Inventory optimization 02 01 03 04 MedTech Supply chain focus Fractals assessment of current need state Fractal research Analysis based on the challenges cited in MedTech firms annual reports investor presentations INTERNAL SLIDE A Control Towers Relevant capabilities case studies 02 Digital enablement e2e Supply chain visibility Real time metrics tracking across the Medical devices continuum of care Prevention Diagnosis Monitoring Treatment and Care Environment metrics such as sensor drift temperature etc. during device manufacturing Early warning alerts and cross functional nudges in case of critical dip in devices quality metrics Enabling decisions through Control towers Digital analytics and transformation to enable A. cross functional decision making B. shifting dynamics to homecare personalized healthcare C. Remote interactions with multiple stakeholders such as payers Build analytics engine and scalable technology plan for a successful digital adoption Transformed SOP process with Supply Chain Planning Control tower Single source of truth with right data at right time across 11 Sourcing platforms 25 Plants 13 DCs Enable collaboration across functions to drive Operational Strategic decisions Forward looking capabilities Integrated Supply chain data platform Cross functional user stories Demand at risk Early warnings alerts on upcoming risks Optimized planning scheduling asset utilization Faster decision making across Sales Operations planning Client ask Solution Result B Risk and Resilience Build resilience framework Build supply resilience Supplier analytics scorecard using key inputs such as performance spend and cycle times financial vulnerability of Tier 123 suppliers ESG performance etc. Dualalternate sourcing for raw materials redeploy from other networks Tracking Counterfeit risks at any node of the supply chain Building Risk and Resilience Build a MedTech resilience framework through the lens of 1. Regulatory factors supplier quality 2. Process factors for ex. Standard operating procedures 3. Product factors pre vs. post device launch performance 4. Structural factors manufacturing supplier location etc. 5. Operational factors sourcing concentration risk product complexity etc. Enabled Inbound supplier risk prediction and traceability Limited visibility of supply at risk Limited impact identification on supply demand reconciliation Slow tradeoffs analysis for mitigation strategies Collaborative platform for inbound shipment visibility that enabled Real Time Visibility Early warning signals on risks Recommendations to mitigate risks Product Traceability Impact of Risk across network nodes Enhanced E2E visibility to smoothen collaboration across nodes and post event analysis Client ask Solution Result C Inventory optimization Service levels Demand prediction Demand prediction by u sing AIML techniques by using the relevant MedTech input factors 1. Product Device sales price patent expiry efficacy safety etc. 2 . Market Formulary Therapeutic area spending etc. 3. External GDP growth Healthcare spending Disease prevalence etc. Manage optimized levels and prioritize inventory utilization based on MedTech variables such as Product shelf life Unique suppliers Product type highlow velocity Systematic alerts for expired and recalled inventory Inventory optimization through improved demand prediction and service levels Improve key levers impacting service levels such as Network optimization Existing alternate supplier proximity Driver analysis and RCA ex. Impact of inhouse distribution vs 3PL Improved forecast accuracy led to reduced inventory and improved service levels Poor customer service levels despite having huge inventory levels across the network static Inventory policy settings AIML solutions for improvements across Demand forecast accuracy Prediction of Inventory settings basis the volatility in Demand Supply Simulations for MoQs Service level Drivers analysis Improved accuracy with demand signals as input 20 reduction in safety stock across Top 3 brands Projected 100 service level with 27 reduction in working capital block Monte Carlo simulations to determine optimal supply replenishment from Factory to Warehouse Client ask Solution Result Improved accuracy by Demand sensing Working capital released by optimizing safety stock 14 1.9m Optimal replenishment recommendations Improved service level 1 D Maximize efficiency Optimizing distribution Optimizing p roduction Predictive maintenance to proactively predict a device failure and thus intervene prior to break down Device components optimization based on criticality Production Digital twins to enable simulation across all manufacturing levels Maximize efficiency by optimizing production and distribution T racking Multimodal shipment across multiple levels D evice components SKUs Purchase orders etc. around key metrics such as OTIF Fill rates Understanding distribution impact of productSKU fragmentation across regions owing to differing regulations approvals Improved packaging efficiency reducing both machine changeover times and demand supply gap Generate optimized packaging machines schedule within available capacity constraints A chieve 10 demandsupply gap packsize change overs minimize makespan time Optimal packaging production schedule Intelligent optimization Automated dashboard Reduced demandsupply gap by 70 Achieved average demand supply gap 2.5 vs. Current state 8 gap Reduced average changeover time by 5 Client ask Solution Result 1 plant in China 3 Packaging machines 40 SKUs 9 flavors 7 Pack sizes Impact created across Optimized Logistics Costs with e2e visibility cost drivers through AIML interventions Standardize definition of Logistics cost performance KPIs e2e visibility of cost components Standardize ways to attribute costs Forward looking view of costs rather retrospective view of costs Integrated Logistics data platform with reduced latency in data to decision making from 1QTR to 1 Week e2e cost performance management with descriptive predictive prescriptive analytics Drivers mix with customer level BBN models 25 million reduction in Logistics costs through Transportation cost drivers improving routing guide improved dynamic spot price Client ask Solution Result

Inventory RnR.pptx

A Inventory Resilience PRIORITY Risk Resilience Mapping Resilient Planning Strategic Tactical Responsive  Execution Collaborate Integrate Streamline OTIF deliveries Allocation optimization Network planning Supplier uncertainty Product  Flow Structural Inbound Our POV on supply chain risk and resilience Downtimes Material availability Production plan Operational Tactical Strategic How might we predict risks mitigate product flow across the network from sourcing lens How to monitor and control supplier performance to track ontime infull receipts How to manage inbound Supply chain nodes to ensure right product at right place and at right time How to maximize equipment utilization and optimize production schedule to meet demand surges How to minimize unplanned downtimes via predictive maintenance How to plan for material availability basis demand volatility and projected production plan What should my target stock levels be based on long term demand trends What is my OutOfStock and Excess risk based on supply and demand volatilities and how to mitigate What is my current inventory profile and how do I obtain optimal product mix What is the optimal distribution network to minimize costs and maximize fulfilment How to continuously monitor outbound shipments and enable early warnings for interventions How to find the optimal fulfilment center allocation for orders basis supply and demand constraints Inventory settings Safety stock Strategic build up Inventory related Business questions that can be addressed through the solution 01 Strategic Risk Ensuring Strategic stock levels at various nodes based on latent demand patterns and external perturbances Approach What Your Organization Can Do to Stay Ahead Analyze product life cycle to understand current phases of products Identify inventory drivers and its impacts observed in history Develop Bayesian networks to approximate impacts due to internal and external events Challenges The Challenges Behind building strategic stock Inconsistencies due to localized Inventory Management leading to inconsistencies Poor visibility on inventory drivers No recommendation on anticipatory build up for phaseinphaseout products Impact Achieving financial and service goals Improved Service Level with right inventory level Right balance between inventory and working capital Reduction in stock holding and handling cost SKU life cycle assessment to identify products in planning phase model their risk network Inventory Drivers Impact of internal or external factors on historical inventory positions Markup Adjustment factors for different internal external factors Understand impact of different markup ranges on different events Product Life Cycle List of products moving towards EndofLife EOL phase and products in EOL phase Patterns of SKUs staying in a specific life cycle stage Patterns on SKU characteristics before moving into Degrowth Forecast Production and Inventory details for products in EOL and moving towards EOL phase Bayesian Risk Networks Risk network with impact of external vs internal disruptions Impact on costs and inventory due to external internal events i.e. supplier behavior global local behavior Generate warning alerts recommend mitigation actions Recommend mitigation options in case of event occurrence Determine amount of time spend by products in product life stages Calculate average life of products within a defined grouping Using classification models determine placement of the products in its life cycle Utilize inventory data and sales forecasts along with business logics to fine tune prediction of early EOL Modelling a Bayesian network of impact of external vs internal disruptions Using a compound Poisson process to assess the probability distribution of events and estimating the joint probability of any event occurring Analytical process to recommend strategic stock adjustment factor Causal Network Conduct root cause analysis to identify impacts observed on inventory due to internal external factors Simulate impacts by creating causal network based on RCAs identified Calculate adjustment factors from historical gaps observed in inventory Segregating SKUs into planning and EOL Stage Product life cycle stages EOL prediction Classification model Determine time spend by products in each product life stages by mapping historical data Utilize inherent product hierarchy to group similar products Calculate planning stage end of life stage and transition point for product grouping Calculate average life of products within a defined product grouping Identify transition point for defined product groups Use classification models to de termine whether product is in end oflife stage or not over a period of next 24 months Utilize inventory data and sales forecasts along with business logics to fine tune prediction of early EOL C Category B Brand PL Product Line Transition Point Product Hierarchy Not in EOL EOL Bayesian Risk Network for Planning Stage SKUs Bayes Network HillClimbing algorithm to determine best network with an optimized score and arc strength Blacklist weakest arc strength andor do not make business sense Retrain the model with blacklist in place to obtain final solution Supervised model to detect probability of internalexternal event occurrence considering all evidence present Variable Node Decision Node Utility Node SC Disruption Triggers Mitigation Strategies SC Risks Decisions Consequences Utility Inventory Risk Procurement Adjustment Material Shortfall Quality Issues Transport Disruptions Sporadic Demand Inconsistent Lead Time Original vs Simulated Inventory units dollars Original vs Simulated forecast units dollars External Natural Calamity War Geopolitical Pandemic Internal Financial health Unplanned Breakdown Supplier disruption Forecast Adjustment Advance Procurement CAPA Production Adjustment Anticipatory Built Supply Risk Manufacturing breakdown Risk Transportation Risk Sovereign Risk Production Plan Adjustment Forecast Adjustment Data Required 02 Tactical Risk Review current inventory profile and optimize for assortment Approach What Your Organization Can Do to Stay Ahead Inventory portfolio assessment to segregate products based on their behavior Balancing width and depth of assortments with range and options planning Assortment planning to answer what when and how much of inventory to keep Challenges The Challenges behind assortment optimization Addiction to SKU led growth model more SKUs the merrier it is for Sales growth Fear of competitor not following suit and losing sales Tried assortment cost remained same and sales plummeted Impact Achieving right assortment mix Reduction in Nonperforming Inventory Ability to drive initiatives to reduce anticipatory build Lesser supply chain cost less picking less shipments less warehouse Optimizing assortment through range and options planning Range Options Planning Planning Categories SubCategories SKUs Styles Sizes Quantities Other Product Attributes Costs Prices Margins Deciding factors include Sales Plan Management Strategy Competition Campaigns New Stores etc. Inventory Portfolio Product classification based on ABC and XYZ segmentation Inventory norms for different categories Right balance between service level and inventory based on business requirement Assortment Plan Recommend product mix for the portfolio based on segment Optimal Inventory recommendation for identified product mix Codify business rules thresholds for alertsrisks Clustering and profiling products in categories based on historical 12 months demand impact and revenue impact Develop a multicriteria clustering system based on SKUs demand supply volatility value Volume and velocity Define a hierarchy for grouping SKUs Geographic Volume Velocity Develop costbased liner optimization model to maximize profit Determine top product attribute groups based on expected sales Demand analysis based on historical sales and growth trend to forecast expected sales for identified product grouping locations across the network Determine which SKUs to stock based on various constraints like cost space minimum quantity Analytical process to review inventory profile and do assortment optimization Develop Mixed Integer Linear Programming MILP models to maximize sell in Plant to DC efficiency for range plan and sell throughsold to customers efficiency for option plan Model outputs Optimized range plan by PPG and option plan by PPG by Cluster Range options plan and Assortment plan is more suitable for Retail businesses Optimization for Range Planning Upper Limit Lower Limit Upper Limit Mixed integer Linear programming Lower Upper Limit Range Plan Predicted Demand Historical sales Product master data Sell In SI Qty Quantity shipped from plant to DCs Optimization for Option Planning Predicted Demand Historical sales Output from Range planning Capacity data Upper Limit Lower Limit Upper Limit Upper Limit Upper Limit Mixed integer Linear programming Option Plan OP Sell ThroughST Qty Quantity of products sold to customers Optimization for Assortment Plan Predicted Demand Historical sales Output from Range planning Capacity data Upper Limit Lower Limit Upper Limit Upper Limit Upper Limit Cost based liner Optimization Retail Price 1 Cost 1 x i Min Qty 1 Retail Price 2 Cost 2 x i Min Qty 1 ... Pr ofit Data Required 03 Operational Risk Ensuring optimal stock availability at different nodes in supply chain amid demand and supply uncertainties Approach What Your Organization Can Do to Stay Ahead Assessing SKUs to understand possible risk of over stock or understock Determine shortages and overages based on generated forecast and planned supply Eventbased simulation to identify understock or overstock situations Challenges The Challenges behind inventory levels Localized operations and reporting leading to lack of visibility and collaboration. Lack of dataoriented decision making Lack of scenario analysis to simulate inventory requirements service levels at different nodes in supply chain Impact Mitigating Overstock and Understock Improved Service Level with enhanced customer satisfaction Less deadstock inventory lower holding cost and increased net revenue  Improved Cash flow for working capital Assessing SKUs at risk to simulate for overstockunderstock Simulation Time based details for understock and overstock for up to 12 weeks Inventory level situations at various predefined service levels and inventory parameters WarningAlerts for possible OOS and Excess inventory situations Risk Profiling Understand SKUs which are at risk Inherent trends in demand supply and distribution Predicted OOS and Excess situations Scenario Analysis Ability to create and compare possible mitigations scenarios Time and Cost impact associated upon exercising mitigation options Recommended optimal mitigation plan Narrowing down SKUs atrisk using Statistical analysis Demand Supply variability Demand Supply distribution Average Demand Interval Analyze historical forecast planned supply and shipment data for patterns and trends Calculate OOS and Excess inventory situations based on Forecasted demand planned supplies and shipments Scenario analysis for mitigation OOS risk by what if analysis on Inter facility transfers by overriding existing allocation Negotiation delivery timelines Tweaking production schedules Scenario analysis for mitigation excess inventory risk by what if analysis on Inter facility transfers by overriding existing allocation Tweaking production schedules Analytical approach to assess and mitigate OOS and Excess inventory Forecast Optimize Simulation of inventory levels w.r.t. Desired Service levels Predicted demand Planned Supply distribution parameters Incorporating constraints like MOQ Customer Service level DC Capacity Holding cost distribution shipment schedules Data Required Thank you

KOP - Optimization for Assortment Planning.pptx

Optimization in Assortment Planning 31 March 2022 adidas China account Contents Merchandise Planning Process adidas China project Assortment Planning AP Process Optimization techniques implementation in AP Key takeaways 01 An Overview of Merchandise Planning 1 3 Year plan by year quarter and month Sales margins markdown targets Multichannel considerations Planning on new stores brand Competition Demand Forecasting Strategic Planning Forecasting Sales Plan Inventory Plan Open to Buy OTB Buying Plan Inputs from Category Managers Focus group discussions Retail Operations teams feedback historical sales analysis are all inputs for the design team Design team picks up styles for range Production lead time for e.g. approx. 34 months Samples are sent by the design team 34 months ahead of the season Budgeting Buying Max. SKUs styles Sizes Quantities per style costs prices margins are defined in range planning Balancing width and depth of assortments with options planning. Deciding factors include Management Strategy New Stores Brands Competition Campaign etc. 2 Seasons Spring Summer April to August Autumn Winter September to March 1520 Core styles Carry forward styles 80 New Seasonal Styles Range Planning Process of selecting the products to put on the shelf in order to maximize sales and margins for every season Cluster stores by location and product attributes Plan product mix SKU rationalization. Discontinue unprofitable products What to buy how much to buy and in what size ratio Consider Historical Sales Category planning Men Women Kids etc. SKUs Style and Size Stores Capacities Store Clusters Assortment Planning Optimization Tracking sales on weekly basis and deciding on inter store transfers stock transfers between stores promotions placing orders Achieving Sellthrough targets at full prices Recalculate OTB based on trend Pricing Revising Prices and Discounts Reduce need for markdowns End of Season sale Promotions based on sellthrough targets margins inventory on hand actual sales In Season Planning Retail Merchandise Planning RANGE AND ASSORMENT PLANNING 02 About adidas China project Overall adidas project tracks Efficiency as a foundation while improving Range Assortment effectiveness Improve Range Assortment Planning OBJECTIVE approach A. Efficiency initiatives Increase analysis maturity B. Effectiveness initiatives Increase value to business Adopt efficient processes to improve speed ease agility Improve effectiveness of RA decisions Adidas Product Range 03 What is Assortment Planning Range Plan How many unique SKUs to carry in a product group Option Plan How many unique SKUs to carry in a product group within a store cluster Assortment Plan Which SKUs to carry in a store Range and Assortment Planning 12 Range and Option Plans SKUs Apparel Footwear Accessories Men Women Unisex Football Basketball Running Etc. TShirt Jacket Shorts Etc. 200300 300500 500700 Etc. Cluster 1 Cluster 2 Cluster 3 Etc. SKUs SKUs SKUs SKUs Range Plan Option Plan Division Gender Category Product Type Price Point Store Cluster Assortment Plan Assortment Plan Red Cotton Blue Knitted Black Yellow DoubleKnit DoubleKnit Colour Fabric Design Sizes SKU 1 SKU 3 SKU 11 SKU 17 Division Gender Category Product Type Price Point Store Cluster Range and Assortment Planning 22 Demand forecast Extract insights from historical performance analysis Decide on selective delisting and carryover products at subcategory level Decide on strategic listing at sub category level Split by channel Store Cluster Arrive at of SKUs ASP by Brand Category SubCategory Price Band etc. Hand off to Buying Decide at store level Decide Size Ratio Stagger units across weeks of the season aligned with Marketing calendar Create Assortment Plan Create Range Plan Option Plan Qualitative Review Input Qualitative Review Input Decide on selective delisting and carryover products at SKU level Decide on strategic listing at article level Business Goals Basic Analysis Advanced Analytics Data Science Rate of Sales ROS ST Qty per store per week ROS ST Qty 90D No. of Stores 90D 12 SI Efficiency SI Qty SKUs ST Efficiency ST Qty SKUs Sell In SI SI Qty Quantity shipped from adidas factories to DCs SI Value Yuan SI Qty Retail Price Sell Through ST ST Qty Quantity of products sold to customers ST Value Yuan ST Qty Average Selling Price Sell Through ST ST Qty 90D SI Qty Key Performance Metrics 04 Problem Statement Problem Statement Challenges Existing process are based on excel based analysis of historical sales data involving lot of manual effort Multiple decision points with subjective inputs based on experience Planning process distributed within different teams and is disconnected Scope for improvement in analytical sophistication Business Problem Improve article efficiency and thereby improving planning effectiveness Generate the Range Plan Option Plan and Assortment Plan each quarter and season SpringSummer and FallWinter Devise a analytics based methodology that does better planning as compared to existing processes Reduce cannibalization and increase cross cluster differentiation Solution Approach Understand existing planning processes data sources generate process maps and identify improvement areas Develop an automated multistage optimization solution based on MILP algorithm with An integrated workflow linking range and option planning stages Flexibility to assign various business constraints based on strategy and ART Integrated data sciencebased and flexible planning Siloed solutions. Heavy on planners experience. Efficiency NOT optimized mathematically. Cluster product differentiation NOT quantified. No quantification of product cannibalization. Very long list of KPIs. Time consuming process. Range Plan Option Plan Assortment Plan Automated Process Merchant Business Inputs Three enablers for effectiveness improvement Quick flexible and scalable process Automated process and optimized on Sales efficiency. Input of Option Plan. Visualized result and review. Optimized by cluster recommender with differentiation and insights. Input of Assortment Plan. Visualized result and review. Product attribute level recommender. Cannibalization measurement. Similar top product mapping. 1. Range optimization 2. Option plan optimization 3. Assortment insights Current state Future state with effectiveness solutions 05 Optimization in Assortment Planning Developed Range Optimization RA engine Automated Range PLAN Architecture Template for additional constraints input by users Data Processing Optimized Range Plan by PPG Optimization Process Input to OP Option Planning SI data ST data Python Based Framework Visualization of optimizer output Additional User constraints Flexible to add strategic constraints Range Plan Option Plan Assortment Plan Optimization for Range Planning Upper Limit Lower Limit Upper Limit Mixed integer Linear programming Lower Upper Limit Range Plan Range Plan Option Plan Assortment Plan Developed Option Plan Optimization Engine Automated OPTION PLAN Architecture Data Processing Optimized Option Plan by PPG by Cluster Optimization Process Option Plan Range Plan ST data Python Based Framework Visualization of optimizer output Capacity data Range Plan Option Plan Assortment Plan Optimization for Option Planning Upper Limit Lower Limit Upper Limit Upper Limit Upper Limit Mixed integer Linear programming Option Plan Range Plan Option Plan Assortment Plan Sales Transferability Cannibalization How much sales are lost when a product is delisted Or Which new articles will cause least cannibalization Sales transferability gives the amount of sales that will be lost and transferred to other products on delisting Top Product Attributes Which articles belong to the top product attribute groups based on expected sales. The attribute groups for example could before example colourfabric combinations like BlackCotton etc. Financial Sales Performance Which articles did not perform well in previous season based on metrics like sales value volume sales per store per week distribution etc. Potential Sales Which of the new articles are expected to have more sales Expected sales of an article using latent demand analysis based on historical sales and growth trend of similar articles Assortment Planning Listing DeListing Range Plan Option Plan Assortment Plan 06 Benefits and Key Takeaways Challenges Key takeaways Solution Learnings Role of art vs math in the decisionmaking process To allow manual inputs for art in the assortment consider modular solution approach instead of integrated solution New process adoption change management For business buyin multiple workshop meetings elaborating the underlying optimization logics assumptions and constraints Changing business processes meta data etc. To follow agile delivery project plan Highlight improvements and similarities by comparing with output from existing business process. Benefits Summary Improved Flexibility and Speed Planners can provide additional business constraints based on ART and Business Strategy Faster time to insights into next seasons Range Assortment Feasibility to do more planning iterations before final plan Improved Effectiveness Up to 48 shift in Range to highly efficient PPGs from low efficient PPGs Increase in Sell in Efficiency Increase in Sell through efficiency Improved cross cluster differentiation Decision Enablement Optimized range decisions considering cluster differentiation strategy and assortment insights The solution removes the effect of subjective inputs and biases A robust and scalable analytical decision methodology Potential and actual benefits Retail Merchandise Planning Flow Forecasting Range Planning Budgeting Assortment Planning InSeason Planning Demand Forecasting Sales Plan Inventory Plan Open to Buy OTB Buying Plan Planning Categories SubCategories SKUs Styles Sizes Quantities Other Product Attributes Costs Prices Margins Deciding factors include Sales Plan Management Strategy Competition Campaigns New Stores etc. Selecting Products to maximize sales and margins every season Clustering stores by location and product attributes Planning Product Mix Quantities Size ratios SKU Rationalization ListDelist Tracking sales Recalculate OTB Placing Orders Inter store transfers Price revisions Markdowns Discounts Design Planning Inputs from Category Managers Focus group discussions Retail Operations teams feedback historical sales analysis Design team picks up styles for range Production End of season sales Promotions based on sellthrough targets margins inventory on hand actual sales End of Season Planning Production with a lead time of for e.g. 34 months Merchandise is allocated to stores based on plans and inventories Merchandise Allocation Purchase Orders Sales Plan Designs Samples Finished Goods Market China Channel Wholesale Store Channels CoreSP OriginalsOCSBCS Customers Franchise Customers Project Scope Adidas sells through 12000 stores in China 400 own retail stores in China Ecommerce through adidas.com.cn TMALL JD etc.

Manufacturing Use Case - Waste Reduction.pptx

Supply chain DS Manufacturing Use cases Waste Reduction May 2022 Use Case Understanding Sustainability To improve efficiency and effectiveness To save significant costs To improve resilience and competitiveness To attain net zero To add value to the customer Procurement Supplier issues Lead times Material defects Quality E rrors in ordering or procurement Inventory Excess Inventory Cost of excess inventory Inventory bottlenecks Out of date stocks Inventory mismanagement Less Inventory turnover Production Over production Under production Unplanned downtime Usage of nonrenewable resources nonreuse Production scrap fifo Improper packaging Excess gas emission Transportation Waiting times Routing issues Logistics delays docking Delivery delays Excess gas emission What is a waste in Supply chain Why do we have to reduce waste Factors that causes wastes in Supply chain 2021 Fractal Analytics Inc. All rights reserved Confidential Proposed Solutions C ollaboration between buyers and suppliers Ratio of defective parts Supplier delivery rates Ensure quality Lean approaches like JIT Justintime logistics can help you get closer to a 100 perfectorder measure. Inventory turns visibility Adjust inventory levels based on demand Lean inventory management accurately predict customers future demand and allows your warehouse to switch to a pull model of inventory management in which stocks are only refreshed when theyre depleted below a certain threshold. Safety stock Recycling and Reuserecycled materials are used as input materials and are heavily processed into end products Use of raw materials can be reduced or expensive materials be replaced Waste can be reduced by reusing materials using less hazardous substitute materials nonrenewable resources Identify which products are generating waste minimizing raw material wastage Review production lines Packaging production replace expensive materials with cheaper ones. Monitor firstin firstout FIFO to reduce scrap Bulk order and packaging quantities to reduce the need to dismantle and repackage product Material requirements planning This demanddriven supply chain sees products being manufactured according to the amount of recent sales and the number of indemand items currently in inventory. This allows manufacturers to accurately calculate how many items are needed to replenish inventory which reduces waste in the factory Demand forecasting Reduce the need to expedite materials and the steps in processing individual orders Plan the Movement of Goods Based on Demand Lean approaches like JIT Justintime logistics can help you get closer to a 100 perfectorder measure. Inventory Procurement Production Transportation Factors which influence the use case 2021 Fractal Analytics Inc. All rights reserved Confidential Proposed Solutions Procurement Spend and Supplier analytics Analysis of Dailymonthly Inventory to increase visibility Inventory optimization techniques Distribution center capacity optimization cross docks logistics managementalternate routes Streamlining portfoliodata Forecasting demand Digital transformation Analysis of procurement chain to understand purchase patterns raw materials and supplier groups Predict and optimize Spend across suppliers. Check supplier clusters in high demand regions Understand spend patterns and improve visibility. Supplier analytics performance spend and cycle times Dual sourcing and alternatives for raw materials Redeploy raw materials from other networks Placing atrisk products on customer allocation to avoid stockpiling Analysis of dailymonthly raw material inventory for cash flow and bottom line improvement Inventory monitoring Inventory management Optimization Safety stock and Inventory cost analytics Waste reduction and improve performance using lean and six sigma techniques Analysis of batchwise production patterns raw material consumptions and plant utilization Manufacturing capacity management increase inhouse manufacturing Efficient production Supply levels to match the needs Supply Demand Planning S OP Shift production to most critical products Demand Forecasting based on history data to predict the future demand using AIML techniques Recommendations to mitigate risks Analyze trends to spot spikes and anomalies in inventory levels demand production and distribution Logistics Port alternate routes Trip and Tradelane analytics Manage lane Expedite shipments air S OE Updates on product availability to distribution centers Cross docks stocking locations distribution center capacity Merge physical and digital supply chain to increase visibility Create digital supply chain twins Enable MedTech with endtoend supply chain visibility Predict risk of delayed supply and their impact Mitigation strategies or alternatives Appendix What Waste be it material gas or excess inventory etc is the enemy of efficiency and effectiveness. expensive waste process inefficiencies communication gaps lack of or delayed responses and even errors in ordering or procurement transport inventory motion waiting overproduction overprocessing and defects Unplanned downtime is a giant problem The cost of excess inventory Defective products they produce more than they have to Packaging reducing pershipment packaging weight . Challenge Statistical tests were the first piece but they are essentially only useful when products have already been shipped in more than one package type textbased data that customers find on the Amazon Store the item name description price package dimensions and so on Our model detects the packaging edges to determine shape identifies a perforation a bag around the product or light shining through a glass bottle Why improve their resilience and competitiveness saves significant manufacturing related costs. Many benefits can be realized by waste minimization or source reduction including reduced use of natural resources and the reduction of toxicity of wastes. Dry cleaning example petroleumbased compounds reduce costs Impact to reduce costs improve return on investment manage resource scarcity and reduce the environmental impact of operations fail to manage their waste will find their operations become less sustainable those who do not operate in an environmentally friendly way pay higher costs leading to waste when outofdate groceries and pharmaceuticals had to be disposed of Overproduction of products means youll have to pay for warehouse space to store the excess. Worse still if the products in question are perishable such as food goods you could end up having to throw some away if they expire Reasons 1. Lead Times 2. TransportationRouting Issues Waste in this area includes the obvious like extra fuel costs due to idling trucks incomplete loads and unpredictable costs of the fuel itself. 3. Inventory Mismanagement Weve hinted at issues in this area already but  inventory mismanagement  is a huge source of logistics delays. Go back to our initial example of the common item stored at the opposite end of the warehouse from the loading docks. Thats a delay in stocking the item when it arrives and another delay waiting for it to be brought out and loaded for delivery. 5. LastMile Overproduction of products means youll have to pay for warehouse space to store the excess How switching to electric vehicles  Prevention or elimination of waste is of course the golden standard however redistributing unpreventable surpluses to alternative markets is the next best option. What needs to be avoided is sending waste to landfill. Recycling is therefore viewed as the third best option in the UK Waste Hierarchy. collaboration between buyers and suppliers developing lean methods for supply chain management. Recycling and Reuse recycled materials are used as input materials and are heavily processed into end products use of raw materials can be reduced or expensive materials be replaced Review production lines and administrative processes for unnecessary procedures and rework Review equipment and delivery policies INBOUND OPERATING HOURS PALLET LOSS AND BREAKAGE AVERAGE DELIVERY LEAD TIME AVERAGE TIME IDLE DURING DELIVERY FUEL COSTS INVENTORY ONHAND SUPPLIER DELIVERY RATES SHIPPING HANDLING RATES RATIO OF DEFECTIVE PARTS The manufacture of products is often based on the demand of consumers. This demanddriven supply chain sees products being manufactured according to the amount of recent sales and the number of indemand items currently in inventory This allows manufacturers to accurately calculate how many items are needed to replenish inventory which reduces waste in the factory Analyze Product Design One way of not just  reducing  waste but also optimizing your production is to examine and reevaluate your products design. Identify any areas or methods to reduce raw material use or replace expensive materials with cheaper ones. If you can shave off small costs they might result in substantial savings. You should also  evaluate your product packaging options  and see if theres any way you can use cheaper materials. Manage Resources Apart from just looking at just using cheaper materials you should examine each of your production processes to identify which ones are generating waste. Redesign processes that are creating nonrecyclable or nonreusable waste. Even recyclable waste should be assessed and you should take the cost of recycling into account. Improving Production Quality Quality control is often focused on finished products but one of the goals of quality management should be minimizing raw material wastage Inventory Management Lean approaches like JIT Justintime logistics can help you get closer to a 100 perfectorder measure. Apart from reducing order errors it can also help you cut down tremendously on a lot of associated costs of inventory management like warehousing utility costs rentals and even insurance and taxes. However placing multiple orders may increase transport costs and your vendors might charge higher rates if each order is of a lower value eight types of waste in lean manufacturing Overproduction Waiting Inventory Transportation Overprocessing Motion Defects Workforce eKanban increased inventory turns by as much as 91 and cut inventory costs in half  Materials can spend as much as 90 of cycle time in the queue learn how to measure and accelerate queue turns Reduce the need to expedite materials and the steps in processing individual orders Automatically adjust inventory levels based on demand Monitor firstin firstout FIFO to reduce scrap Transform purchasing into a more strategic function waste can be reduced by reusing materials using less hazardous substitute materials or by modifying components of design and processing This method of streamlining and reducing waste in processes is called lean supply chain management. Radio Frequency Identification RFID  allows automatic capture of data This allows you to more accurately predict customers future demand and allows your warehouse to switch to a pull model of inventory management in which stocks are only refreshed when theyre depleted below a certain threshold. use of nonrenewable materials  Sustainability Inventory Transportation Delivery Common areas that you can discuss with suppliers to reduce product waste are Ratio of defective parts Recyclability weight and volume of packaging materials Packaging takeback options Bulk order and packaging quantities to reduce the need to dismantle and repackage product ML Solutions Cost efficiency due to machine learning which systematically drives waste reduction and quality improvement Lastmile delivery Machine learning in supply chain can offer great opportunities by taking into account different data points about the ways people use to enter their addresses and the total time taken to deliver the goods to specific locations. ML can also offer valuable assistance in optimising the process and providing clients with more accurate information on the shipment status. Industrial waste makes up at least 50 of global waste. Much of that waste is the result of poor quality products from manufacturing processes. AI can help workers reduce waste and lower the cost of quality. Whats also clear is that poor quality is one of the most preventable sources of manufacturing waste. where to send the goods so we optimize inventory levels and how to make sure we dont use excess resources along the way.  achieved a 20 reduction in forecast error and a 30 reduction in lost sales by using machine learning to predict demand One of our customers achieved a 4 gain in gross profits because of a 30  drop in unplanned downtime . Optimize the Speed of the Supply ChainMachine learning can analyze timings and handovers as products move through the supply chain. It can compare this data to benchmarks and historic performance to identify potential holdups and bottlenecks and make suggestions to speed up the supply chain. Forecast Likely Demand From CustomersData can be sourced from many areas like the marketplace environment seasonal trends promotions sales and historic analysis. Machine learning will combine this data to predict demand for specific goods and help to manage the sourcing and manufacture of those products. Plan the Movement of Goods Based on Demand Ensure Quality from Suppliers Products and AssetsQuality is vital to good SCM as waste and faulty products create unnecessary rework and increase costs. Machine learning can monitor how quality varies over time and suggest improvements. This doesnt just apply to materials and products. It can track other areas such as shipping supplier and thirdparty quality. Machine learning delivers several benefits for SCM Organizations in the supply chain do not need to hold as much inventory because machine learning optimizes the flow of products from one place to another Costs are reduced due to machine learning driving quality improvement and waste reduction Products arrive in the marketplace just in time for sale as a result of upstream optimization Supplier relationship management becomes easier due to simpler proven administrative practices Stakeholders get more insight into meaningful information allowing for continual improvement and easier problem solving 2021 Fractal Analytics Inc. All rights reserved Confidential

Med Tech Supply Chain analytics - Art of Possible July 2022.pptx

Supply Chain Analytics solutions for MedTech industry Art of Possible July 2022 Changing dynamics in MedTech industry.. Enable digitization Advance patient centricity Improve flexibility and sustainability Drive value amongst customers Transforming from Doing digital to Being digital to provide connected solutions and achieve operational excellence Transitioning to patient centric ecosystem to empower and engage patients throughout their health journey Engaging in stresstesting operating models and increasing transparency to strengthen resilience and make the operations future ready Evolving from a traditional supplier role to a valuedriven strategic business partner to their customers posing critical Supply chain challenges Global complex Supply chain Actionable realtime data is often unavailable lacking or siloed and hence difficult to track devicesproducts across its lifecycle sourcing manufacturing warehousing and distribution MedTech must adapt quickly to rapidly shifting pandemic conditions while planning for multiple potential next normal scenarios Predict right demand and provide optimal service levels to customers need to optimize multiple factors such as demand volatility supply disruptions excess product on hand or shortages product recalls product portfolio etc. MedTech requires optimal supply chain processes to eliminate inefficiencies and adhere to high quality products to avoid product recalls and thus achieve cost optimization reduce revenue loss and brand reputation risk ShortLack of Resilience Lack of end to end Supply chain visibility Inventory optimization Demand prediction and optimal service levels Supply chain inefficiencies 01 02 03 04 Transforming MedTech Supply Chain Art of possible End to end visibility across MedTech lifecycle Suppliers RD sites Production sterilization sites Distributors 3PL GPO Hospitals Pharmacies Enable cross functional decision making for ex. RD pre and post device launch analytics IT and design groups for demand at risk future service levels Predict risk of delayed supply shortages and their impact to build assemble specifically complex devices Alternative options of Supply Chain planning to counter the disruptions Mitigation plans for delays through recommendations at Plant DC or HCOs such as Pharmacy Hospitals etc. Optimize inventory levels based on product portfolio Premium high value low velocity ex. Devices stents Commoditized low value high velocity ex. syringes Improved service levels by optimizing allocation decisions and thus minimize penalties Failure to Serve FTS Optimize production and distribution processes to minimize supply chain inefficiencies across multimodal nodes Device components optimization based on criticality and Predictive maintenance to proactively predict a device failure 02 01 03 04 Risk and Resilience Demand prediction and service levels Control Tower Minimize inefficiencies Digital enablement e2e Supply chain visibility Real time metrics tracking across the Medical devices continuum of care Prevention Diagnosis Monitoring Treatment and Care Environment metrics such as sensor drift temperature etc. during device manufacturing Early warning alerts and cross functional nudges in case of critical dip in devices quality metrics Digital analytics and transformation to enable A. cross functional decision making B. shifting dynamics to homecare personalized healthcare C. Remote interactions with multiple stakeholders such as payers Build analytics engine and scalable technology plan for a successful digital adoption Enabling decisions through Control towers Transformed SOP process with Supply Chain Planning Control tower Single source of truth with right data at right time across 11 Sourcing platforms 25 Plants 13 DCs Enable collaboration across functions to drive Operational Strategic decisions Forward looking capabilities Integrated Supply chain data platform Cross functional user stories Demand at risk Early warnings alerts on upcoming risks Optimized planning scheduling asset utilization Faster decision making across Sales Operations planning Client ask Solution Result Build resilience framework Build supply resilience Supplier analytics scorecard using key inputs such as performance spend and cycle times financial vulnerability of Tier 123 suppliers ESG performance etc. Dualalternate sourcing for raw materials redeploy from other networks Tracking Counterfeit risks at any node of the supply chain Build a MedTech resilience framework through the lens of Regulatory factors supplier quality Process factors for ex. Standard operating procedures Product factors pre vs. post device launch performance Structural factors manufacturing supplier location etc. Operational factors sourcing concentration risk product complexity etc. Building Risk and Resilience Result Enabled Inbound supplier risk prediction and traceability Limited visibility of supply at risk Limited impact identification on supply demand reconciliation Slow tradeoffs analysis for mitigation strategies Collaborative platform for inbound shipment visibility that enabled Real Time Visibility Early warning signals on risks Recommendations to mitigate risks Product Traceability Impact of Risk across network nodes Enhanced E2E visibility to smoothen collaboration across nodes and post event analysis Client ask Solution Service levels Demand prediction Demand prediction by using AIML techniques by using the relevant MedTech input factors 1. Product Device sales price patent expiry efficacy safety etc. 2. Market Formulary Therapeutic area spending etc. 3. External GDP growth Healthcare spending Disease prevalence etc. Manage optimized levels and prioritize inventory utilization based on MedTech variables such as Product shelf life Unique suppliers Product type highlow velocity Systematic alerts for expired and recalled inventory Improve key levers impacting service levels such as Network optimization Existing alternate supplier proximity Driver analysis and RCA ex. Impact of inhouse distribution vs 3PL Inventory optimization through improved demand prediction and service levels Client ask Solution Result Improved forecast accuracy led to reduced inventory and improved service levels Poor customer service levels despite having huge inventory levels across the network static Inventory policy settings AIML solutions for improvements across Demand forecast accuracy Prediction of Inventory settings basis the volatility in Demand Supply Simulations for MoQs Service level Drivers analysis Improved accuracy with demand signals as input 20 reduction in safety stock across Top 3 brands Projected 100 service level with 27 reduction in working capital block Monte Carlo simulations to determine optimal supply replenishment from Factory to Warehouse Improved accuracy by Demand sensing Working capital released by optimizing safety stock 14 1.9m Optimal replenishment recommendations Improved service level 1 Optimizing distribution Optimizing p roduction Predictive maintenance to proactively predict a device failure and thus intervene prior to break down Device components optimization based on criticality Production Digital twins to enable simulation across all manufacturing levels T racking Multimodal shipment across multiple levels D evice components SKUs Purchase orders etc. around key metrics such as OTIF Fill rates Understanding distribution impact of productSKU fragmentation across regions owing to differing regulations approvals Maximize efficiency by optimizing production and distribution Improved packaging efficiency reducing both machine changeover times and demand supply gap Generate optimized packaging machines schedule within available capacity constraints A chieve 10 demandsupply gap packsize change overs minimize makespan time Optimal packaging production schedule Intelligent optimization Automated dashboard Reduced demandsupply gap by 70 Achieved average demand supply gap 2.5 vs. Current state 8 gap Reduced average changeover time by 5 Client ask Solution Result 1 plant in China 3 Packaging machines 40 SKUs 9 flavors 7 Pack sizes Impact created across Optimized Logistics Costs with e2e visibility cost drivers through AIML interventions Standardize definition of Logistics cost performance KPIs e2e visibility of cost components Standardize ways to attribute costs Forward looking view of costs rather retrospective view of costs Integrated Logistics data platform with reduced latency in data to decision making from 1QTR to 1 Week e2e cost performance management with descriptive predictive prescriptive analytics Drivers mix with customer level BBN models 25 million reduction in Logistics costs through Transportation cost drivers improving routing guide improved dynamic spot price Client ask Solution Result

MedTech Clear to Build V1.pptx

CleartoBuild Material shortage Inventory Manufacturing plan September 2022 01 02 03 04 Problem statement Solution construct Data requirements Mockups Agenda 01 Problem statement Operational Tactical For what products critical do we clear to build What are my critical raw materials How to r eadjust and resequence manufacturing plan and prioritize the orders based on availability manufacturing strategy mto ato eto sales orders back orders planned orders Order allocation Are we adhering to the recommendedplanned build date How soon are we providing visibility to the customer incase of risksdelays How much can be produced as per Production plan in planning horizon How much lastminute changes can be accommodated How much will I be able to produce in subsequent days as purchase orders sales orders and manufacturing runs continue What will happen if I have long lead times supplier risks than expected How t o have visibility on all your shortages inter dependencies between orders at every stage before deciding prior to moving forward with your build Clear to build Tactical Operational risks in readjusting Clear to build This slide talks about the questions that we are going to answer from our capability Critical points Tactical Critical products which needs 100ctb priority so those raw materials must be critical I have orders I have the inventory. Plan based on priority availability alternate BOM 100 or 100 We have to plan and recommend for a build date and check whether we are adhering to it Incase of risks delays how soon are we providing that visibility to the customer to avoid penaltiesreputation issues Operational Min Max production levels Build projection Risk prediction Shortage prediction visibility on interdependencies between orders Readjust Clear to build to plan and schedule production based on priority Challenges Lack of visibility to BOM a global material flow dependency graph Lack of visibility beyond tier1 suppliers High risk potential from solesource suppliers and critical components Supplier uncertainties OTIF Q uality long lead times and V olatile demand Misalignment in manufacturing plan and schedule due to material shortages priority Sales Order SO impact on time delivery Objective Visibility to unpack the BOMs describing how material flows from raw to WIP to finished goods To visualize entire chain on a map to reveal highrisk supplier clusters and critical components To deliver orderbased scheduling by ranking to prioritize the orders based on availability of materials demand and delays To readjust clear to build dynamic Production scheduling based on material availability and provide visibility to the customer Outcomes Visibility provides the endtoend truth about the current state of material availability and clear to build Raw material segmentation to identify critical components and inbound risks associated with them Projection of material shortages to help provide visibility to customers on available to promise date To define prioritization strategy for orders in manufacturing to achieve 100 CTB for the important orders when multiple finished goods compete for the same raw material Clear to build This slide talks about the objective challenges and outcomes to improve ctb 02 Solution Construct Prediction Are the products available when I need them What are my Highrisk orders due to lack of component availability Raw material shortage prediction and product demandsale forecast to anticipate high demand shortfall and bottleneck situations Determine min and max number of products that can be produced with current available inventory To enable forwardlooking to deal with material shortages in the future in order to provide visibilityalerts while falling short on an order Visibility What are the critical materials and risks associated with them To have visibility across interconnected BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods Identify critical materials based on the requirement lead times production usage and break them into priority categories Determine supplier uncertainty supplier clusters high risk regions To determine order prioritization strategy for when multiple products compete for the same raw material Simulation What is the current state of material availability and clear to build Simulation enables whatif scenarios on suppliers manufacturing and capacity capabilities or end customer demand plans and forecasts Manufacturing plan to be adjust by order allocation logics based on supply available inventory capacity back orders priorities and demand To capture propagated impact of the production and arbitrary future states of the supply chain Clear to build Endtoend visibility to inbound network shortage projection and CTB simulation NW Optimization Min max prod opt Maximize production Manu strategies Alternate bom sourcing This slide talks about the highlevel solution sections content flow Segmentation Alert Simulation Prediction Simulation AIML algorithm to predict shortage and forecast demand Hierarchical Quantile regression to predict min and max number of products that can be manufactured on given inventory Scenario planning for Manufacturing based on supply available inventory capacity back orders priorities and demand Readjust and resequence planned production to clear to build metrics of important orders based on availability Visibility Identify BOMs Multiple BOMs for raw material requirement and material traceability from FG demand Classification of raw materials into priority categories Track and trace historical transit lead time from supplier to plant by RM Inbound risks Supplier uncertainty risk scores reputation high risk regions etc to prioritize critical shortages by suppliers Recommendations Enable supplier options based on risk categories alternate BOM dual sourcing Prescriptive recommendations on inventory Safety stock ROP based on material classification and manufacturing strategies Synchronize simulation to the manufacturing schedule and recommend potential fulfilment Recommend available to promise date for the orders in case of delays when clear to build date exceeds need by date Clear to build Leveraging analytics to readjust Clear to Build tied to the dynamic manufacturing plan This slide talks about the technical highlevel solution Goals Prediction RealTime Monitoring Recommendations Visibility across BOMs Multiple BOMs to select suitable alternate BOMs Determine supplier reliabilities associated with the raw materials Predict raw material shortages using historical delays lead time supplier factors demand POs Quantile regression to predict min and max number of products that can be manufactured on given inventory to plan production Forecast demand of the products using hierarchical timeseries to inturn get raw material requirement Classification of raw materials into priority categories and choose them Prescriptive recommendations on RM inventory Safety stock ROP based on material classification and manufacturing strategies Clear to build Solution Construct To readjust Clear to Build and synchronize the manufacturing schedule This slide shows the flow of the entire solution Segmentation visibility rm classification Prediction shortage Inventory related Optimization Simulation manu plan Recommendations Inventory BOM Scenario planning for Manufacturing based on supply available inventory capacity back orders priorities and demand Constraints Availability Quality supplier uncertainty factors lead times safety stock replenishment cycle capacity Readjust and resequence production plan based on availability and priority Simulation Synchronize simulation to manufacturing schedule to recommend potential fulfilment and available to promise date for the orders Enable supplier options based on risk category alternate BOM dual sourcing BOM is still the crux of manufacturing processes but rather that new manufacturing data is everywhere and is complex and poorly managed. No visibility to collaboration with a distributed group of people Vendors suppliers suboptimal decisions about what suppliers and what components to choose To find out which BOMsassemblies a given productpart belongs to Unhide common elements and dependencies of the product structures and their components Optimize Bill of Materials and make improvements in component usage as well as decisions about Vendor and suppliers With a 360degree view of BOM may leverage meaningful contextual data connections Performance and scalability enable realtime decision making no matter how complex the BOM Extracted insights from the BOM graph model can provide risk analysis supply chain bottlenecks find alternatives for suppliers and components as well as optimize product scheduled and deliveries Objective Challenges Outcomes Clear to build Graph based Analysis of multilevel BOMs to identify critical components Clear to build A graph model of multilevel BOM explosion Visibility of multiple BOMs to go for alternates w.r.t production purposes and for engineering purposes K Means Hierarchical clustering for raw material segmentation based on business inputs Derive supplier uncertainties associated with raw material categories based on supplier derived points Classification of RM based on requirement lead time product usage BOMs into priority categories and choose which ones to clear to build Determine supplier risk scores associated with RM based on lead times geographic defect rate delivery delays subtier compliances fill rate delivery performance and quality to provide alternate sourcing Near realtime visibility of inbound network and explosion of BOM by FG and supplier dependency Raw material segmentation and optimal purchasing patterns based on clustering analysis Segmentation Approach Outcomes Clear to build Realtime visibility and Segmentation to better understand Inbound network What are the critical materials and risks associated with them BOM Visibility Critical components Surgical steel Titanium Bulk purchase PTFE FEP PVDF Plastics Make to Order Titanium Steel Polyurethane Polyvinyl chloride Polyetherimide Liquid silicon rubber Make to Stock Plastics Fluoropolymer Make to stock demand forecast Ex Dialyzers Syringes Plastics Make to order Multiple BOMs Ex Pacemakers Xray machines patient monitors Plastics Critical components used for implants knee implants braces Ex Surgical steel titanium Metals Easily forecastable Ex lab equipment Catheters test tubes Plastics Different raw material categories based on usage BOM priority lead time consumption rate lifecycle etc Clear to build BOM explosion and Raw material Segmentation What are the raw material categories Should name the categories Identify influencing factors of lead time comparisons across raw materials Identify the opportunities to improve LT and assess the impact on the inbound network Assess the demand to capacity ratio and determine the maximum and optimum capacities and variance in demand can be absorbed Dynamic monitoring of Inbound database with entities nodes BOM explosion from RM to WIP to FG Identify bottlenecks of sourcing manufacturing and deepdive into reasons Identify tier1 and subtier suppliers and analyse the performance based on delivery time defects compliance rate ontime delivery and fill rates Realtime monitoring of business continuity risk to provide actionable insights like RM shortages delayed deliveries etc Access exposure to network disruptions such as geopolitical risks tradeoffs Supplier risk categorization based on performance and inventory segmentation followed by optimal purchasing pattern Traceability of Inbound network Understanding drivers of Lead Time Risk Mitigation and Resilience Clear to build Envisioning state of Inbound network Content slide for visibility Keep for now can remove later if not required Logistics Plants to TPM Logistics Supplier to plants Logistics Suppliers to Plants Clear to build Supply chains today are more global interconnecting multiple entities and flows... Bayesian and regression modelling to predict delivery risk and its impact on OTIF Risk prioritization of various risks for immediate business interventions Segmenting risks into warning alarming and emergency Track historical patterns of supplier behavior on Delivery Time defects compliance rate ontime delivery and fill rates Perform gap analysis to evaluate the current OTIF performance w.r.t. targets Variance analysis to assess the degree of deviation from baseline Assess tradeoff between different scenarios considering various constraints Recommending multiple alternatives to tackle the incoming risk Impact of suggested recommendations Evaluate Existing Risk and gap analysis Predict risk severity and occurrence Scenario Planning and recommendation Clear to build To analyse supplier reliability and uncertainity Trend Analysis Risk Prediction Sourcing recommendations Supplier uncertainty slide WIP Using regression to predict raw material shortage using historical delays lead time supplier factors demand POs Hierarchical time series forecasting to forecast demand and derive product buckets MTS Quantile regression to predict min and max number of products that can be manufactured on given inventory to calculate CTB To determine which orders to start early or right away and reprioritize orders to current onhand and shortages At what time and level to replenish inventory by estimating right level of inventory availability inhand To calculate clear to build component availability and ready to build Near realtime visibility of components availability to calculate CTB and plan orders accordingly to avoid falling short on an order Prioritization of sales orders based on available components inhand or make components available as per lead time Determine optimal purchasing pattern of raw materials and replenishment points Prediction Approach Outcomes Clear to build Clear to build projection Raw material shortage in Inventory Are the products available when I need them What are my Highrisk orders due to lack of component availability To determine the attributes for make order Clear to Build Orders Clear to Build Component Availability Ready to Build Ready to build quantity Top Shortage Components and Top Shortage Suppliers substitute indicators S imulation view to allocate onhand to competing make orders critical make orders to be expedited p rioritizing them d eprioritizing other make orders alternate BOMs dual sourcing TPM To identify orders that can be started early or right away reprioritize the orders to current onhand and critical orders to expedite component supplies Integration of future shortages of raw materials to proactively make them available onhand Proje cted shortages will lead to provide the potential CTB Visibility provides the endtoend truth about the current and future state of material availability and clear to build To recommend potential fulfilment date based on delay due to clear to build Optimized components and capacity usage to maximize CTB Simulation Approach Outcomes Clear to build To simulate arbitrary future states of supply chain to maximize clear to build What is the current state of material availability and clear to build Clear to build KPIs and Dates Clear to Build Status Yes All its components are completely available in on hand No On time Some components are not completely available in on hand right now No At risk The order is at risk due to lack of components CTB dateNeed by Date Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build you can start 10 units of the make order Clear to Build date Dec 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only Dec 14 Delay Due to Clear to Build 8 Dec 30 Dec 20 in workdays Ready to Build Quantity The quantity of the order that you could start now Ready to Build Order quantity Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Clear to build Clear to build KPIs What is the current state of material availability and clear to build Whyeasily forecasted products Howlow lt to orders produce prods in advance Forbulk low cost high qty low lt Glassware manufacturers MTS Whystd products sale forecast Howlowers risk of overmanufacturing high manufacturing cost and lt Forlarge qty upon order costs high require enggdesign MTO Whyspeed and reduced waste Howparts are preproduced assemble when orders received mtomts Forlow qty orders requires enggdesign no options to configure Monitor ATO Whymass customization Howsubassemblies mtsassemble final assembly when order received Forlow qty orders customizations enggdesign lab equipment manufacturers CTO Whycomplex prods Howorder based long lt design engg and manufacturing Forlow qty orders enggdesign customized ETO Clear to build Manufacturing Strategies that define sales in MedTech 03 Data Requirements We would consider following data sets as input to AIML exercise Suppliers Reference 04 Mockups Business Overview Business Analysis Recommendations Clear to build Story Flow Overview TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING TRACKING AND MONITORING INBOUND OTIF HISTORY Supplier Fill Rate 92 Supplier Count 26 Order count 11.34K Delay Risk 6 RECOMMENDATIONS Flow Recommend dates and check whether we are adhering to it Plan based on priority even 100 If incase of riskdelay how soon are we providing visibility to customer ResearchGraph db cosmos db httpsmedium.comtowardsdatasciencegettingstartedgraphdatabaseneo4jdf6ebc9ccb5b httpsql2gremlin.com httpsdkuppitz.github.iogremlincheatsheet101.html Gremlin Cheat Sheet 101 httpsdkuppitz.github.iogremlincheatsheet102.html Gremlin Cheat Sheet 102 httpstinkerpop.apache.orgdocscurrentreference httpswww.kelvinlawrence.netbookPracticalGremlin.html_introductionhttpswww.kelvinlawrence.netbookPracticalGremlin.html_introduction httpslearn.microsoft.comenusazurecosmosdbgremlinmodeling httpsitnext.iogettingstartedwithgraphdatabasesazurecosmosdbwithgremlinapiandpython80e57cbd1c5e Why graph Generally if there exists complex relations hierarchies or connections between various entities in the dataset storing data in the form of graphs is not only scalable but also simplifies various complex analytics GraphsVisualization network graph TOP MANUFACTURERS Products Advanced Molecular Nuclear Imaging Computed Tomography Diagnostic ECG Fluoroscopy Hospital Respiratory Care Imageguided therapy MRI Systems Solutions Patient Monitoring Solutions Radiation Oncology Radiography Xray Fluoroscopy Solutions Service Parts Sleep and Respiratory Care Ultrasound Ventilation Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers Clear to build BOM explosion and Raw material Segmentation What are the raw material categories Visibility into historical raw material patterns and contributions Supplier reliability scorecard Classification of raw materials to make them available before committing to build Estimating right level of inventory availability inhand Low visibility of the subtier suppliers and bottlenecks to deepdive into reason Unrealized sourcing opportunities Prioritization of sales orders based on available components inhand or make components available as per lead time At what time and level to replenish inventory Near realtime visibility of inbound network and explosion of BOM by FG and supplier dependency Supplier performance and ranking Segmentation and Optimal purchasing pattern of raw materials and replenishment point Challenges Approach Outcomes Clear to build Realtime visibility to better understand Inbound network Traceability Realtime visibility across global supply chain network and highlight bottlenecks To unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods Supplier reliability scorecard Prediction Identify material shortages and determine root cause of shortages Predict shortages of parts which can affect clear to build Optimal inventory of base parts classification and replenishment to make it available prior to build Simulation Integration of product shortages for sales order to arrive at potential CTB Visibility to risky sales orders and their CTB status Whatif scenarios on suppliers manufacturing and capacity capabilities Recommendation Synchronize simulation to the manufacturing schedule and recommend potential fulfilment Orderbased scheduling which can apply a ranking to prioritize the order process based on availability of raw materials and capacity. Clear to build High Level Solution Approach MTOATOCTOETO Planning To expedite Clear to Build and synchronize the manufacturing schedule Challenges Demand variations and supplier uncertainty lead to material unavailability at the right time at the right location which impact the manufacturing rate Ever evolving regulatory requirements supplier risk manufacturing complexity increase the lead time which impact the available parts in hand contribute to low CTB Misalignment in manufacturing plan priority Sales Order SO impact on time delivery Objective Traceability of orders have delay risks due to the lack of raw material availability Predict shortages Inventory classification before deciding prior to moving forward with build and recommend alternatives Recommend potential fulfilment date Optimize manufacturing schedule to fulfil OTIF and S ynchronize planning to the manufacturing schedule. Tactical operational How is BOM getting exploded to the finished goods for critical orders and raw material dependencies What are the options to source raw materials and its impact on manufacturing schedule Is there any capacity constraint Is there opportunity to deprioritize planned order How much A class B class C class inventory readily available onhand MTS Planning To expedite Clear to Build and synchronize the manufacturing schedule Challenges Upsurge in demand supplier dependency in upstream trigger supply demand mismatch Regulatory requirements manufacturing complexity increase the lead time which impact the available parts in hand contribute to low CTB Misalignment in manufacturing plan priority Sales Order SO impact on time delivery Objective Predict material shortages recommend alternatives Improve ready to build Recommend potential fulfilment date Optimize manufacturing schedule to fulfil OTIF Strategic questions How is demand surge impacting at supplier end OTIF 00 Research Types of Manufacturing Materials There are certain materials that can fulfill the requirements above and help manufacturers create highquality and costeffective medical devices. These include Metals Certain types of metals are ideal for medical devices that need to implanted inside the body. Surgical steel is the metal of choice for implants that are used to repair bone fractures. Titanium which is also a good choice for bone implants is used to make pacemaker casings although these can also be made with a titanium alloy. Plastics The malleability of plastics which has caused them to be a popular material for various everyday items makes them highly valuable in the medical device industry. A wide range of biocompatible plastics have been developed and theyre now used in countless applications. Polyurethane for instance is used to insulate the metal lead in pacemakers while polyvinyl chloride PVC is used to make blood tubing and blood bags. Polyetherimide PEI is the material of choice for surgical skin staples while polyetheretherketone PEEK is used to make rigid tubing for various purposes. PEI and PEEK are a favorite of medical device manufacturers since they can withstand the heat from autoclaving without losing their rigidity. The biocompatibility of plastics is determined by USP Class VI and ISO 10993. Medical device manufacturers who want to ensure that theyre using the best materials should look for plastics that pass the standards of these tests. PTFE FEP PVDF are widely used Fluoropolymer in medical industry. Extensive use in catheters syringes dialyzers parts of xray. Liquid silicone rubber Medical grade liquid silicone rubber is preferred by many manufacturers because of its thermosetting property. Liquid silicone rubber complies with USP Class VI and ISO 10993 standards and it can be sterilized using autoclaves as well as Ebeam and gamma radiation processes. It also stays stable and maintains its resiliency and flexibility even when exposed to high temperatures. Implants Metals surgical steel titanium Pacemaker case Metals titanium Pacemakers Plastics metal polyurethane polyvinyl chloride Polyetherimide polyetheretherketone Catheters syringes dialyzers xray machines plastics PTFE FEP PVDF Xray machines liquid silicon rubber Product Segmentation activation Low demand Tantalum Platinum Low Perishability Low Demand Medium Price Slow moving L iquid silicone rubber Medium Perishability Low Demand High Price Fast moving Drugs I mplantable devices High Perishability High Demand Low Price Bulk purchase Surgical steel Titanium Polyurethane polyvinyl chloride PVC Polyetherimide PEI Low Perishability Low Demand High Price Long term forecast based on historical demand Price based on discounts quantity Long term forecast based on reorder frequency replenishment count Ex Milk bread Products with lumpy erratic demand Price based on seasons quantity Forecast based on demand seasonality and quality Ex Electronics dispenser Different product groups will be optimized differently based on lifecycle demand consumption rate price discounts List of Important Procurement KPIs Number of Suppliers Helps track the dependency level on suppliers Compliance Rate Verifies if suppliers meet compliance requirements S upplier quality rating Evaluate the quality of suppliers S upplier Ability A measure of suppliers ability to meet demand Supplier Defect Rate Helps evaluate the quality of individual suppliers Purchase Order Cycle Time  Helps identify whom to address urgent orders to Vendor Rejection Rate and Cost Helps evaluate internal quality management strategies Lead Time Gauge the total time to fulfill an order List of Important Manufacturing KPIs Overall Equipment Effectiveness OEE    measures the percentage of time that a machine or production line produces good quality articles during the scheduled time.       OEE Availability x Performance x Quality Workinprocess WIP measures the value of raw materials or subassemblies that have entered the manufacturing process before obtaining the finished good.       WIP Manufacturing Lead Time x Production Flow Value Ontimeinfull OTIF    measures the number of orders which had been delivered with the right quality and the right quantity on time to the customer.        OTIF Number of perfect orders Total number of orders Lead Time LT  It is the time it takes to fulfill an order from when it is confirmed until the order is delivered in full. Cost per Unit CPU  helps a manufacturing system to optimize the cost of the products.       CPU Direct Material Costs Direct Labor Costs Manufacturing Overhead Total units produced Production Downtime is a period when the manufacturing process is on hold and no products are produced Inventory Turnover Ratio The higher the inventory turnover rate is the more efficiently the supply chain is built.        Inventory Turnover Ratio COGS Avg. Inventory Production Schedule Attainment  reflects how well production is planned and how efficiently the production workers meet their targets.        Production Schedule Attainment Actual Output Planned Output x 100 Objective Challenges Material shortages post SC planning as variations were not factored low clear to build attributes Reasons for these challenges Supplier uncertainty Inbound volatile demand Supplier OTIF external factors Inventory on hand poor quality long lead times high onhand inventory Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers lack of alternate suppliers defects manufacturing site locations with suppliers co manu Material shortages as variations were not factored low clear to build attributes and not synced with manufacturing plan Supplier uncertainty volatile demand P oor quality defects overmanufacturing waiting nonutilized talent transportation inventory motion and extra processing Defect long lead times Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers High risk potential from solesource suppliers and lack of subtier visibility Lack of visibility to BOM a global material dependency graph describing how material flows from raw to WIP to finished goods Manufacturers cannot react quickly and intelligently to changes without planning and scheduling tools Challenges Objective Improve clear to build Prod planning Inventory on Hand We can only clear to build something if all raw materials are available for it no material shortage. Availability is related to Supplier uncertainty Inbound Risk Resilience reliability. Predict material shortage using supplier uncertainty factors which will affect clear to build. Visualize your entire supply chain incl. all manufacturing sites on a world map to reveal weak spots such as supplier clusters in highrisk regions Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   purchase based on manufacturing strategies MTS MTO To deliver orderbased scheduling which can apply a ranking to prioritize the order process based on availability of resources and the materials required for the order Questions How much can I produce today given current inventory How much will I be able to produce in subsequent days as purchase orders sales orders and manufacturing runs continue What do I need to do in order to debottleneck manufacturing of a given finished good What am I clear to build How do I allocate limited raw materials to manufacturing orders To sales orders How do my allocation decisions impact revenue What do I need to do in order to debottleneck a given SKU My entire network How to get the materials that is needed connect manufacturing schedule material requirement order allocation How to prioritize the orders mtoeto new orders back orders Are the products available when I need them What will happen if I have long lead times 1. To synchronize purchasing and planning to the manufacturing schedule 2. To reduce unplanned variation of upstream and downstream material flows resulting in a mismatch of supply and demand 3. manufacturers continue to invest large amounts of money into new or upgraded ERP BI they still lack the ability to properly align the manufacturing plan from order input to order shipment 4. Advantage What if you knew you had all the parts that you need available to you prior to committing to your build and to create a manufacturing plan against resources available on hand 5. What if you could prioritize builds based on a priority Sales Order SO and see the effect on all your other deliveries 6. To have visibility on all your shortages at every stage before deciding prior to moving forward with your build 7. To understand which make orders have delay risks due to the lack of component availability 8. Expedite component supplies and Readjust planned manufacturing to expedite clear to build metrics of important orders CTB CTB visibility simulation and alerting capabilities across the entire materials management space Visibility preferred prioritization strategy for when multiple finished goods compete for the same raw

material. Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   Flow Companies Products Advanced Molecular Nuclear Imaging Computed Tomography Diagnostic ECG Fluoroscopy Hospital Respiratory Care Imageguided therapy MRI Systems Solutions Patient Monitoring Solutions Radiation Oncology Radiography Xray Fluoroscopy Solutions Service Parts Sleep and Respiratory Care Ultrasound Ventilation Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers MedTech Products and Companies Becton Dickinson Medtronic thermos fisher ResearchDates Clear to build KPIs Clear to Build No all components are not completely available in on hand Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build 10 you can start 10 units of the make order this consumes 10 units of A 20 units of B and 30 units of C 10 units ready to build 100 order quantity 100 Clear to Build date June 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only June 14 Delay Due to Clear to Build 8 June 30 June 20 in workdays Ready to Build Quantity 10 0.1 100 Make Order Value 4500 45 1000 Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Inbound Supplier uncertainty reliability risk quality lack of alternate suppliers long lead times OTIF product categories based on attributes purchase based on manufacturing strategies MTS MTO ATO CTO ETO   Inventory Replenishment cycle kind of items Planned receipts actual receipts materials planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance   Onhand balances and replenishment of items is stored in the plants system. The information stored will include item description location quantities onhand vendor info MinMaxs and more. Establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout. Establish a minimum and a maximum inventory level for each part.   BOM BOM up to date in order to prevent inventory inaccuracies and spare parts shortages and to better plan for preventative maintenance or servicing. SUPPLIER PRODUCTS QUALITY OF THE PRODUCTS   manufacturing Prioritize the orders mtoeto new orders back orders planning and scheduling based on demand BOM   How to get the materials that is needed connect manufacturing schedule material requirement order allocation Match manufacturing with demand Normal Additional Clear to build Nodes Manufacturing Strategies and KPIs Nodes Manufacturing strategies KPIs ResearchDates Clear to build KPIs Clear to Build No all components are not completely available in on hand Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build 10 you can start 10 units of the make order this consumes 10 units of A 20 units of B and 30 units of C 10 units ready to build 100 order quantity 100 Clear to Build date June 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only June 14 Delay Due to Clear to Build 8 June 30 June 20 in workdays Ready to Build Quantity 10 0.1 100 Make Order Value 4500 45 1000 Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Inbound Supplier uncertainty reliability risk quality lack of alternate suppliers long lead times OTIF product categories based on attributes purchase based on manufacturing strategies MTS MTO ATO CTO ETO   Inventory Replenishment cycle kind of items Planned receipts actual receipts materials planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance   Onhand balances and replenishment of items is stored in the plants system. The information stored will include item description location quantities onhand vendor info MinMaxs and more. Establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout. Establish a minimum and a maximum inventory level for each part.   BOM BOM up to date in order to prevent inventory inaccuracies and spare parts shortages and to better plan for preventative maintenance or servicing. SUPPLIER PRODUCTS QUALITY OF THE PRODUCTS   manufacturing Prioritize the orders mtoeto new orders back orders planning and scheduling based on demand BOM   How to get the materials that is needed connect manufacturing schedule material requirement order allocation Match manufacturing with demand Normal Additional M anufacturing strategies that define sales in the Medtech Whyeasily forecasted products Howlow lt to orders produce prods in advance Forbulk low cost high qty low lt Glassware manufacturers MTS Whystd products Howlowers risk of overmanufacturing high manufacturing cost and lt Forlarge qty upon order costs high require enggdesign MTO Whyspeed and reduced waste Howparts are preproduced assemble when orders received mtomts Forlow qty orders requires enggdesign no options to configure Monitor ATO Whymass customization Howsubassemblies mtsassemble final assembly when order received Forlow qty orders customizations enggdesign lab equipment manufacturers CTO Whycomplex prods Howorder based long lt design engg and manufacturing Forlow qty orders enggdesign customized ETO Siemens Solution Challenge Material shortages as variations were not factored low clear to build attributes and not synced with manufacturing plan Supplier uncertainty volatile demand P oor quality Defect long lead times Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers High risk potential from solesource suppliers and lack of subtier visibility Lack of visibility to BOM a global material dependency graph describing how material flows from raw to WIP to finished goods Manufacturers cannot react quickly and intelligently to changes without planning and scheduling tools No supplier alternative counterfeit Objective Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Visualize your entire supply chain incl. all manufacturing sites on a world map to reveal weak spots such as supplier clusters in highrisk regions Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   purchase based on manufacturing strategies MTS MTO To deliver orderbased scheduling which can apply a ranking to prioritize the order process based on availability of resources and the materials required for the order   For whom we are solving this problem MedTech Manufacturers   What all challenges are being experienced Material shortages even after SC planning as variations were not factored in planning low clear to build attributes Reasons for these challenges Supplier uncertainty Inbound volatile demand Supplier OTIF external factors Inventory on hand poor quality long lead times high onhand inventory Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers lack of alternate suppliers defects manufacturing site locations with suppliers co manu   Objective Improve clear to build Prod planning Inventory on Hand We can only clear to build something if all raw materials are available for it no material shortage. Availability is related to Supplier uncertaintyInbound Risk Resilience reliability. Predict material shortage using supplier uncertainty factors which will affect clear to build. Visualize your entire supply chain incl. all manufacturing sites on a world map to reveal weak spots such as supplier clusters in highrisk regions Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   Nodes to focus Procurement to manufacturing planning Inbound Ask To deliver Deck Solution along with Mockups   Flow Demand Inventory Check product availability If OutofStock Manufacture the Product manufacturing planning team Check Inventory availability of raw materials buy from Vendor if not available MedTech companiesBecton Dickinson Medtronic thermos fisher Make to order MedTech Configure to order MedTech Engineer to order MedTech   Inbound Supplier uncertainty reliability risk quality lack of alternate suppliers long lead times OTIF product categories based on attributes purchase based on manufacturing strategies MTS MTO ATO CTO ETO   Inventory Replenishment cycle kind of items Planned receipts actual receipts materials planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance   Onhand balances and replenishment of items is stored in the plants system. The information stored will include item description location quantities onhand vendor info MinMaxs and more. Establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout. Establish a minimum and a maximum inventory level for each part.   BOM BOM up to date in order to prevent inventory inaccuracies and spare parts shortages and to better plan for preventative maintenance or servicing. SUPPLIER PRODUCTS QUALITY OF THE PRODUCTS   manufacturing Prioritize the orders mtoeto new orders back orders planning and scheduling based on demand BOM   How to get the materials that is needed connect manufacturing schedule material requirement order allocation Match manufacturing with demand Normal Additional   Does Sap captures Prod schedule demand enters into the system so as and when demand comes in inventory should be available   Are the products available when I need them Fulfill New order Prioritize new order or back order   What will happen if I have long lead times Results in shortage Plan new inventory and manufacturing schedule   How is my planning Planning based on Lead time risks reliability other factors. Plan well and advance the inventory position and manufacturing schedule Prioritization of the orders if deviation what to do trigger   Approach Network Optimization PO uncertainty prediction Supplier uncertainty prediction Product shortage prediction Impact What are the recommendations actionsinsights Overall Tactical Operational How do I plan clear to build monitoring the deviation predicting recommending.   After making a list of these critical items you can break them into priority categories using the  ABC and XYZ analysis methods .   Research Spare parts business oilgas industry tracking monitoring factors gas turbines supplier reliability when to buy new product build   What is the problem why are we solving the problem Planned receipts therefore are  crucial to regulating the level of inventory in the warehouse . Planned receipts are also important for determining whether you have received materials promised by vendors or inhouse manufacturing. Without them the system cannot establish a link between orders and received material.   if goods receipts are planned Materials Planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance. Research MTO ETO CTO for medtech situations Inv policies Long lead time products   No seasonality Qure.ai engineer to order only change the algorithm Type of products Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers Research Item categories criticality Supplier categories Supplier uncertainty Clear to build Clear to build tells you whether you can start orders for make items. It depends on whether the materials required for these orders are available. An order is clear to build if all its components are onhand. start right away Prioritize based on onhand R isks due to the lack of component availability Expedite component supplies Readjust and resequence planned manufacturing to expedite clear to build metrics of important orders Clear to build for planned orders Check supplydemand view Clear to Build Status Yes All its components are completely available in on hand No On Time All its components are available when required for the order. Some components are not completely available in on hand right now. No At Risk The Clear to Build Date is later than the Need By Date. The order is at risk due to lack of components. Substitute components used indicators Manufacturers synchronize ComponentsRaw MaterialsSemiFinished goods to reduce shortage and working capital Traceability risks CTB visibility simulation and alerting capabilities across the entire materials management space Visibility preferred prioritization strategy for when multiple finished goods compete for the same raw material. How much can I produce today given current inventory How much will I be able to produce in subsequent days as purchase orders sales orders and manufacturing runs continue What do I need to do in order to debottleneck manufacturing of a given finished good Questions What am I clear to build How do I allocate limited raw materials to manufacturing orders To sales orders How do my allocation decisions impact revenue What do I need to do in order to debottleneck a given SKU My entire network ResearchDates Clear to build KPIs Clear to Build No all components are not completely available in on hand Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build 10 you can start 10 units of the make order this consumes 10 units of A 20 units of B and 30 units of C 10 units ready to build 100 order quantity 100 Clear to Build date June 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only June 14 Delay Due to Clear to Build 8 June 30 June 20 in workdays Ready to Build Quantity 10 0.1 100 Make Order Value 4500 45 1000 Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Research Clear to build simulations Identifying critical make orders that need to be expedited for clear to buildIn the SupplyDemand view filter for your key sales orders to identify whether there is any risk associated with replenishing themrev ship datedue date prioritize and deprioritize make orders and planned orders After making a list of these critical spare parts you can break them into priority categories using the  ABC and XYZ analysis methods . The goal here is to get a clear breakdown of the most frequently used parts then create a costeffective plan for replenishing them.  A good plan will help you avoid a stockout when a needed spare part is unavailable For each part Berger says establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout Calculate the Optimal Order Quantity maketostock MTS assemble to stock ATS maketoorder MTO configuretoorder CTO engineertoorder ETO Orde to cash cycle is long in medtech Why Information capture and no visibility Bulk orders Prod categories groups purpose Research 1. To synchronize purchasing and planning to the manufacturing schedule 2. To reduce unplanned variation of upstream and downstream material flows resulting in a mismatch of supply and demand 3. manufacturers continue to invest large amounts of money into new or upgraded ERP BI they still lack the ability to properly align the manufacturing plan from order input to order shipment 4. Advantage What if you knew you had all the parts that you need available to you prior to committing to your build and to create a manufacturing plan against resources available on hand 5. What if you could prioritize builds based on a priority Sales Order SO and see the effect on all your other deliveries 6. To have visibility on all your shortages at every stage before deciding prior to moving forward with your build 7. To understand which make orders have delay risks due to the lack of component availability 8. Expedite component supplies and Readjust planned manufacturing to expedite clear to build metrics of important orders Product Categorization based on Demand Supply Total Cost Analysis ABC Analysis Manufacturing Strategy MTS MTO ATO Suitable supply chain strategy Products Advanced Molecular Nuclear Imaging Computed Tomography Diagnostic ECG Fluoroscopy Hospital Respiratory Care Imageguided therapy MRI Systems Solutions Patient Monitoring Solutions Radiation Oncology Radiography Xray Fluoroscopy Solutions Service Parts Sleep and Respiratory Care Ultrasound Ventilation Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers Clear to build BOM explosion and Raw material Segmentation What are the raw material categories Activation of Inventory Policies Relevant inventory models could be followed by analyzing product specific eorder points review periods orderuptolevels Policies Cause Decide on the two major variables in an inventory control system order quantity ordering frequency Effect Analyze reorder pts order levels demand forecast to determine which inventory system to follow for each product group Smart Buying How to choose Inventory Policies What inventory models could be followed for different product groups by analyzing reorder points review periods orderuptolevels Cost Policies Smart Buying Operational Tactical Whether the plan can be accomplished or not For what products critical do we clear to build What are my critical raw materials How to r eadjust and resequence manufacturing plan and prioritize the orders based on availability manufacturing strategy mto ato eto sales orders back orders planned orders Order allocation Are we adhering to the recommendedplanned build date How soon are we providing visibility to the customer incase of risksdelays How much can be produced as per Production plan in planning horizon How much lastminute changes can be accommodated What will be the impact on production plan if PO is delayed How t o have visibility on all your shortages inter dependencies between orders at every stage before deciding prior to moving forward with your build Clear to build Tactical Operational risks in readjusting Clear to build This slide talks about the questions that we are going to answer from our capability Critical points Tactical Critical products which needs 100ctb priority so those raw materials must be critical I have orders I have the inventory. Plan based on priority availability alternate BOM 100 or 100 We have to plan and recommend for a build date and check whether we are adhering to it Incase of risks delays how soon are we providing that visibility to the customer to avoid penaltiesreputation issues Operational Min Max production levels Build projection Risk prediction Shortage prediction visibility on interdependencies between orders Links Oracle Rapid Planning Implementation and Users Guide What is a cleartobuild concept in inventory control How is it achieved in a manufacturing organization Quora 80 Clear to Build solution for HighTech manufactures Dassault Systmes YouTube 2022_04_Clear_to_Build_WP_FINAL__1_.pdf Planning Goods Receipts SAP Help Portal Receipt SCM Portal Demand Supply Chain Glossary Bill of Materials BOM Meaning Purpose and Types 2 What can a CleartoBuild Module mean for your company LinkedIn manufacturing issues and what can be done about them Cascadia Seller Solutions 6 Common Quality Issues in the Manufacturing Industry How to Solve the 5 Top Challenges of Manufacturing Projects Mdh logo 7 Things to Consider for Successful Spare Parts Management Sigma Thermal 9 Tips for Managing and Optimizing Spare Parts Inventory 3 Tips to Optimize Your Spare Parts Inventory Control System PDF Rapid manufacturing in the spare parts supply chain Alternative approaches to capacity deployment How to keep spare part inventories accurate for maintenance departments Plant Engineering httpswww.plm.automation.siemens.comglobalplindustriesmedicaldevicespharmaceuticalsmanufacturingplanningscheduling.html

MedTech Clear to Build V2.pptx

Optimize Cleartobuild Maximize the visibility and probability to meet the promise date December 2022 01 02 03 04 05 Problem statement Operational Elements Tactical Elements Data requirements Mockups Agenda 01 Problem statement Components unavailability leads to low fulfilment Clear to build Challenges Operational Lack of visibility to BOM explosion a global material flow dependency Components shortage due to supply uncertainties and Volatile demand Tactical High risk potential from single source suppliers and critical components Any deviation in planned production impacts ontime delivery Objective Visibility to unpack the BOMs describing how material flows and their dependencies To provide alerts to reveal highrisk supplier clusters and critical components R eadjust plan based on scenarios to recommend potential promise date To deliver order based scheduling by prioritizing the orders Outcomes Critical components and Supplier risk depending on products Scenario based scheduling to maximize the plan accomplishment and alerts on promise date being met Optimal Supplier path to achieve the highest CTB Projection of components shortages for planned orders to maximize the probability of meeting promise date Access to components subassemblies at the right time and in the right quantity is imperative to ensure adherence to the production plan and subsequently meeting the PDD Research indicates timely availability only to the extent of 40 in the MedTech Industry Solutions to improve this CTB percentage can significantly improve working capital inventory levels and adherence to PDDs Optimizing CTB based planning through structured approach CTB Engine OTIF deliveries Supplier uncertainty Components BOM Purchase Order Material availability Production plan How soon are we providing visibility in case of risksdelays Which are Critical Components How many FGs can be produced as per production plan What will be the impact if the PO is delayed or partially delivered What will be the impact if a critical component is unavailable For which components should safety stock be held What levels of safety stock to maintain How many orders can be fulfilled OTIF Inventory settings Safety stock Fulfilment Which are uncertain suppliers ELC intents to build an endtoend Supply chain application for descriptive and diagnostics visibility for volume and lead time Current scenario Whats the need CTB is estimated by SAP which is reactive considers only confirmed information with no simulation capability to capture planners inputs or planners scenario Due to lack of capability to use scenarios realization of event at last moment leads to production schedule cancellation Bottleneck identification in terms of critical components through exact BOM explosion Decision latency due to unscalable simulation process Realtime visibility across Inbound network with alerts and recommendations A CTB engine with users Whatif scenarios external inputs for future projection of production accomplishment User inputs capability for order prioritization strategy Supplier performance driven decision making based on business criticality Dynamic safety stock Absence of MultiHierarchy BOM explosion High time to insights identifying bottlenecks Limited preparedness for customer demand Minimum visibility Challenges Missing Alerts recommendations To build a CTB engine for descriptive and diagnostics visibility for component unavailability Operational questions Tactical questions Components unavailability leads to low fulfilment Clear to build Challenges Lack of visibility to BOM explosion a global material flow dependency High risk potential from solesource suppliers and critical components Components shortage due to supply uncertainties and Volatile demand Any deviation in planned production impacts ontime delivery Objective Visibility to unpack the BOMs describing how material flows and their dependencies To provide alerts to reveal highrisk supplier clusters and critical components R eadjust plan based on scenarios to recommend potential promise date To deliver order based scheduling by prioritizing the orders Outcomes Critical components and Supplier risk depending on products Scenario based scheduling to maximize the plan accomplishment and alerts on promise date being met Optimal Supplier path to achieve the highest CTB Projection of components shortages for planned orders to maximize the probability of meeting promise date Operational Tactical What are the critical parts or assemblies What are the top uncertain suppliers How soon are we providing visibility to the customer incase of risksdelays What are inter dependencies between same orders How to r eadjust Clear to build and prioritize the orders based on components availability How much can be produced as per Production plan in planning horizon Whether the plan can be accomplished or not Users scenarios can be accommodated What will be the impact on production plan if PO is delayed What is the status of priority order Clear to build Tactical Operational questions to be answered 0 1 Operational risk Alerts Projection Near realTime Monitoring Recommendations Visibility across BOM Multilevel BOM Highlight critical nodes based on supplier uncertainty component risk Alert on Clear to build status and plan adherence Quantile regression to predict min and max number of products that can be manufactured on given inventory to plan production Project component shortages based on historical trends or performance Identify critical components and categorize them Clear to build Solution Construct Architecture Simulation Plan adherence and lastminute changes To recommend potential fulfilment date for sales order Scenario planning based on Inventory Supply Capacity Demand priorities Constraints Plant Capacity Supplier uncertainty Safety stock replenishment cycle Plausible Scenarios for Optimized Production schedule Improved CTB Recommendation for Alternate suppliers and Scheduling Optimization What can we do to stay ahead Visibility for continuous assessment and monitoring of material flows and risk assessment Visibility across Multilevel BOM through graphbased analytics Dynamic Scheduling Amans pointer Prioritization or Deprioritization of FG basis the uncertainties Hassles to seamless production planning Order fulfillment compliance wrt production plan Material availability issues due to unforeseen deviations What does this mean for Production Planning functions Realtime visibility of component critical levels Maximize the probability of promise date to be met for high priority goods Improve planning and mitigating production delays Approach Challenges Impact Bridging the gaps in Operations planning to provision for demand fulfillment Discussion on this whether Kishore agreed for this Alerts Prediction Shortages for upcoming delivery What are my Highrisk orders how can I prioritize them Alert on Clear to build status and plan adherence Regression algorithms determines min and max number of products that can be produced ML models to predict component shortages RealTime Monitoring What are the critical components and risks associated with them Visualization of multilevel BOM their interdependency Nudges for Critical components which are based on Schedule What if scenarios for projected schedule impact on CTB Simulate scenarios for Yet to plan Planning stage orders Capture propagated impact of the production and arbitrary future states of the supply chain Clear to build Solution Levers Projection Simulation AIML algorithm to project shortages to meet promise date for planned orders Quantile regression to predict min and max number of products that can be manufactured on given inventory Optimization algorithm for future based CTB scenarios including yet to plan planning stage finished goods Graph Analytics Demonstrate multilevel interlinked BOM hierarchy through community detection algorithm Identify analyze critical component nodes based on inbound risk demand priority using centrality algorithm Unhide opportunities in decision making about supply uncertainty component risk to prioritize critical shortages Recommendations Component allocation for prioritized orders Enable supplier requirements based on risk categories Synchronize simulation to the manufacturing schedule and recommend potential fulfilment dates Prescriptive recommendation to meet promise date for planned orders depending on product type FG SMG Clear to build Solution Construct Leveraging analytics Clear to build Graph based Analysis of multilevel BOMs for E2E Visibility 0 2 Tactical risk Using regression to predict component shortage using historical delays lead time supplier factors demand POs Identify parts can be kept in stock or not and Hierarchical time series forecasting to forecast demand of stock parts Quantile regression to predict min and max number of products that can be manufactured on given inventory to calculate CTB Supply disruptions due to components that are not always available Volatile demand and low supplier attributes lead to components unavailability Suppliers may have their own supply chain risks which is leading to shortage at manufacturing unit Last minute changes to committed qty Anticipate potential shortages for planned orders Prescriptive recommendation to make components available for a long planning horizon Safety stock and replenishment of components that can be kept in inventory Existing Risk Approach Outcomes Clear to build Component Shortage Prediction to meet the promise date Clear to build Scenario based scheduling to recommend potential fulfilment Recommendation Plan Adherence Potential Fulfilment Prescriptive To readjust Plan Meeting promise date for planned orders Components allocation for parts Insights whether the fulfilment date is being met or not Last minute changes can be incorporated with the plan or not depending on the product type Enable alternate options to readjust CTB and prioritize orders To recommend the potential fulfillment date Potential fulfilment date Preventive actions to maximise the probability of meeting promise date for planner orders Component allocation for parts or assemblies when competing for the same finished good Last minute changes in plan Ensuring fulfilment date Clear to build Recommendation mechanism 03 Data Requirements Data Requirements 04 Mockups Clear to build What is Critical Component Parameters to be considered for material criticality Business Overview Business Analysis Recommendations Clear to build Story Flow Overview TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING TRACKING AND MONITORING INBOUND OTIF HISTORY Supplier Fill Rate 92 Supplier Count 26 Order count 11.34K Delay Risk 6 RECOMMENDATIONS 00 Research Data Requirement Recommendation mechanism Plan Adherence Insights whether the fulfilment date is being met or not Last minute changes can be incorporate with the plan or not Potential Fulfilment Enable alternate options to readjust CTB and prioritize orders To recommend the potential fulfillment date Prescriptive Preventive actions to maximise the probability of meeting promise date for planner orders Component allocation for parts or assemblies when competing for the same finished good Clear to build Alert Simulation AIML algorithm to project shortages to meet promise date for planned orders Quantile regression to predict min and max number of products that can be manufactured on given inventory Optimization algorithm for future based CTB scenarios including yet to plan planning stage finished goods RealTime Monitoring Demonstrate multilevel parentchild hierarchy through node chart Unhide opportunities in decision making about suppliers by Track trace transit LT Classification of components into priority categories Inbound risks Supplier uncertainty risk scores high risk regions etc to prioritize critical shortages by suppliers Recommendations Component allocation for prioritized orders Enable supplier requirements based on risk categories Synchronize simulation to the manufacturing schedule and recommend potential fulfilment dates Prescriptive recommendation to meet promise date for planned orders Clear to build Solution Construct Leveraging analytics C reate and query complex relationships between parts subassemblies finished goods s uppliers and manufacturing units. Structure of DB is comprised of nodes and e dges. An edge connects two nodes by describing relationship C entrality algorithms to rank nodes based on importance considering critica l components and supplier attributes With a 360degree view of BOM leverage meaningful contextual data connections Enable realtime decision making no matter how complex the BOM Extracted insights can provide realtime visibility and maximize the probability of promise date to be met BOM explosion Approach Outcomes Clear to build Graph based Analysis of multilevel BOMs to identify critical components and provide alerts C ombination of multiple  BOMs can provide a model for the analysis of parts common critical components and their suppliers A n aggregated graph can be used for various decisions suppliers search part purchasing decisions identifying bottleneck Determine supplier risk scores based on supplier attributes K Means Hierarchical clustering for raw material segmentation based on business inputs Derive supplier uncertainties associated with raw material categories based on supplier derived points Classification of component based on requirement lead time product usage BOMs into priority categories and choose which ones to clear to build Determine supplier risk scores associated with RM based on lead times geographic defect rate delivery delays subtier compliances fill rate delivery performance and quality to provide alternate sourcing Near realtime visibility of critical components and supplier dependency Component segmentation and optimal purchasing patterns based on clustering analysis Segmentation Approach Outcomes Clear to build Components classification and Supplier reliability What are the critical materials and risks associated with them BOM Visibility Identify influencing factors of lead time comparisons across raw materials Identify the opportunities to improve LT and assess the impact on the inbound network Assess the demand to capacity ratio and determine the maximum and optimum capacities and variance in demand can be absorbed Dynamic monitoring of Inbound database with entities nodes BOM explosion from RM to WIP to FG Identify bottlenecks of sourcing manufacturing and deepdive into reasons Identify tier1 and subtier suppliers and analyse the performance based on delivery time defects compliance rate ontime delivery and fill rates Realtime monitoring of business continuity risk to provide actionable insights like RM shortages delayed deliveries etc Access exposure to network disruptions such as geopolitical risks tradeoffs Supplier risk categorization based on performance and inventory segmentation followed by optimal purchasing pattern Traceability of Inbound network Understanding drivers of Lead Time Risk Mitigation and Resilience Clear to build Envisioning state of Inbound network Content slide for visibility Keep for now can remove later if not required Bayesian and regression modelling to predict delivery risk and its impact on OTIF Risk prioritization of various risks for immediate business interventions Segmenting risks into warning alarming and emergency Track historical patterns of supplier behavior on Delivery Time defects compliance rate ontime delivery and fill rates Perform gap analysis to evaluate the current OTIF performance w.r.t. targets Variance analysis to assess the degree of deviation from baseline Assess tradeoff between different scenarios considering various constraints Recommending multiple alternatives to tackle the incoming risk Impact of suggested recommendations Evaluate Existing Risk and gap analysis Predict risk severity and occurrence Scenario Planning and recommendation Clear to build To analyse supplier reliability and uncertainity Trend Analysis Risk Prediction Sourcing recommendations Supplier uncertainty slide WIP To determine the attributes for make order Clear to Build Orders Clear to Build Component Availability Ready to Build Ready to build quantity Top Shortage Components and Top Shortage Suppliers substitute indicators S imulation view to allocate onhand to competing make orders critical make orders to be expedited p rioritizing them d eprioritizing other make orders alternate BOMs dual sourcing TPM Reprioritize the orders to current onhand and critical orders to expedite component supplies Integration of shortages of components to proactively inform customer about potential fulfilment Proje cted shortages will provide potential order fulfilment potential CTB Visibility provides the endtoend truth about the current and future state of material availability and improved clear to build Recommend potential fulfilment date based on component delays Optimized capacity usage Simulation Approach Outcomes Clear to build To simulate alternatives of supply chain entities to readjust clear to build What is the current state of material availability and clear to build Operational Tactical For what products critical do we clear to build What are my critical raw materials How to r eadjust and resequence manufacturing plan and prioritize the orders based on availability manufacturing strategy mto ato eto sales orders back orders planned orders Order allocation Are we adhering to the recommendedplanned build date How soon are we providing visibility to the customer incase of risksdelays How much can be produced as per Production plan in planning horizon How much lastminute changes can be accommodated How much will I be able to produce in subsequent days as purchase orders sales orders and manufacturing runs continue What will happen if I have long lead times supplier risks than expected How t o have visibility on all your shortages inter dependencies between orders at every stage before deciding prior to moving forward with your build Clear to build Tactical Operational risks in readjusting Clear to build This slide talks about the questions that we are going to answer from our capability Critical points Tactical Critical products which needs 100ctb priority so those raw materials must be critical I have orders I have the inventory. Plan based on priority availability alternate BOM 100 or 100 We have to plan and recommend for a build date and check whether we are adhering to it Incase of risks delays how soon are we providing that visibility to the customer to avoid penaltiesreputation issues Operational Min Max production levels Build projection Risk prediction Shortage prediction visibility on interdependencies between orders Flow Recommend dates and check whether we are adhering to it Plan based on priority even 100 If incase of riskdelay how soon are we providing visibility to customer ResearchGraph db cosmos db httpsmedium.comtowardsdatasciencegettingstartedgraphdatabaseneo4jdf6ebc9ccb5b httpsql2gremlin.com httpsdkuppitz.github.iogremlincheatsheet101.html Gremlin Cheat Sheet 101 httpsdkuppitz.github.iogremlincheatsheet102.html Gremlin Cheat Sheet 102 httpstinkerpop.apache.orgdocscurrentreference httpswww.kelvinlawrence.netbookPracticalGremlin.html_introductionhttpswww.kelvinlawrence.netbookPracticalGremlin.html_introduction httpslearn.microsoft.comenusazurecosmosdbgremlinmodeling httpsitnext.iogettingstartedwithgraphdatabasesazurecosmosdbwithgremlinapiandpython80e57cbd1c5e httpslearn.microsoft.comenusazurecosmosdbgremlinsupplychaintraceabilitysolution httpswww.openbom.comblogopenbomgraphsnetworksandbillofmaterialspart3 httpsmaxdemarzi.com20171117billofmaterialsinneo4j httpsd3graphgallery.comgraphnetwork_basic.html Why graph Generally if there exists complex relations hierarchies or connections between various entities in the dataset storing data in the form of graphs is not only scalable but also simplifies various complex analytics GraphsVisualization network graph Cosmos db apis with sql or azure table storage Gremline for graphs Clear to build A graph model of multilevel BOM explosion TOP MANUFACTURERS Products Advanced Molecular Nuclear Imaging Computed Tomography Diagnostic ECG Fluoroscopy Hospital Respiratory Care Imageguided therapy MRI Systems Solutions Patient Monitoring Solutions Radiation Oncology Radiography Xray Fluoroscopy Solutions Service Parts Sleep and Respiratory Care Ultrasound Ventilation Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers Clear to build BOM explosion and Raw material Segmentation What are the raw material categories Visibility into historical raw material patterns and contributions Supplier reliability scorecard Classification of raw materials to make them available before committing to build Estimating right level of inventory availability inhand Low visibility of the subtier suppliers and bottlenecks to deepdive into reason Unrealized sourcing opportunities Prioritization of sales orders based on available components inhand or make components available as per lead time At what time and level to replenish inventory Near realtime visibility of inbound network and explosion of BOM by FG and supplier dependency Supplier performance and ranking Segmentation and Optimal purchasing pattern of raw materials and replenishment point Challenges Approach Outcomes Clear to build Realtime visibility to better understand Inbound network Traceability Realtime visibility across global supply chain network and highlight bottlenecks To unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods Supplier reliability scorecard Prediction Identify material shortages and determine root cause of shortages Predict shortages of parts which can affect clear to build Optimal inventory of base parts classification and replenishment to make it available prior to build Simulation Integration of product shortages for sales order to arrive at potential CTB Visibility to risky sales orders and their CTB status Whatif scenarios on suppliers manufacturing and capacity capabilities Recommendation Synchronize simulation to the manufacturing schedule and recommend potential fulfilment Orderbased scheduling which can apply a ranking to prioritize the order process based on availability of raw materials and capacity. Clear to build High Level Solution Approach MTOATOCTOETO Planning To expedite Clear to Build and synchronize the manufacturing schedule Challenges Demand variations and supplier uncertainty lead to material unavailability at the right time at the right location which impact the manufacturing rate Ever evolving regulatory requirements supplier risk manufacturing complexity increase the lead time which impact the available parts in hand contribute to low CTB Misalignment in manufacturing plan priority Sales Order SO impact on time delivery Objective Traceability of orders have delay risks due to the lack of raw material availability Predict shortages Inventory classification before deciding prior to moving forward with build and recommend alternatives Recommend potential fulfilment date Optimize manufacturing schedule to fulfil OTIF and S ynchronize planning to the manufacturing schedule. Tactical operational How is BOM getting exploded to the finished goods for critical orders and raw material dependencies What are the options to source raw materials and its impact on manufacturing schedule Is there any capacity constraint Is there opportunity to deprioritize planned order How much A class B class C class inventory readily available onhand MTS Planning To expedite Clear to Build and synchronize the manufacturing schedule Challenges Upsurge in demand supplier dependency in upstream trigger supply demand mismatch Regulatory requirements manufacturing complexity increase the lead time which impact the available parts in hand contribute to low CTB Misalignment in manufacturing plan priority Sales Order SO impact on time delivery Objective Predict material shortages recommend alternatives Improve ready to build Recommend potential fulfilment date Optimize manufacturing schedule to fulfil OTIF Strategic questions How is demand surge impacting at supplier end OTIF Whyeasily forecasted products Howlow lt to orders produce prods in advance Forbulk low cost high qty low lt Glassware manufacturers MTS Whystd products sale forecast Howlowers risk of overmanufacturing high manufacturing cost and lt Forlarge qty upon order costs high require enggdesign MTO Whyspeed and reduced waste Howparts are preproduced assemble when orders received mtomts Forlow qty orders requires enggdesign no options to configure Monitor ATO Whymass customization Howsubassemblies mtsassemble final assembly when order received Forlow qty orders customizations enggdesign lab equipment manufacturers CTO Whycomplex prods Howorder based long lt design engg and manufacturing Forlow qty orders enggdesign customized ETO Clear to build Manufacturing Strategies that define sales in MedTech Clear to build KPIs and Dates Clear to Build Status Yes All its components are completely available in on hand No On time Some components are not completely available in on hand right now No At risk The order is at risk due to lack of components CTB date Need by Date Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build you can start 10 units of the make order Clear to Build date Dec 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only Dec 14 Delay Due to Clear to Build 8 Dec 30 Dec 20 in workdays Ready to Build Quantity The quantity of the order that you could start now Ready to Build Order quantity Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Clear to build Clear to build KPIs What is the current state of material availability and clear to build Critical components Surgical steel Titanium Bulk purchase PTFE FEP PVDF Plastics Make to Order Titanium Steel Polyurethane Polyvinyl chloride Polyetherimide Liquid silicon rubber Make to Stock Plastics Fluoropolymer Make to stock demand forecast Ex Dialyzers Syringes Plastics Make to order Multiple BOMs Ex Pacemakers Xray machines patient monitors Plastics Critical components used for implants knee implants braces Ex Surgical steel titanium Metals Easily forecastable Ex lab equipment Catheters test tubes Plastics Different raw material categories based on usage BOM priority lead time consumption rate lifecycle etc Clear to build BOM explosion and Raw material Segmentation What are the raw material categories Should name the categories Logistics Plants to TPM Logistics Supplier to plants Logistics Suppliers to Plants Clear to build Supply chains today are more global interconnecting multiple entities and flows... Types of Manufacturing Materials There are certain materials that can fulfill the requirements above and help manufacturers create highquality and costeffective medical devices. These include Metals Certain types of metals are ideal for medical devices that need to implanted inside the body. Surgical steel is the metal of choice for implants that are used to repair bone fractures. Titanium which is also a good choice for bone implants is used to make pacemaker casings although these can also be made with a titanium alloy. Plastics The malleability of plastics which has caused them to be a popular material for various everyday items makes them highly valuable in the medical device industry. A wide range of biocompatible plastics have been developed and theyre now used in countless applications. Polyurethane for instance is used to insulate the metal lead in pacemakers while polyvinyl chloride PVC is used to make blood tubing and blood bags. Polyetherimide PEI is the material of choice for surgical skin staples while polyetheretherketone PEEK is used to make rigid tubing for various purposes. PEI and PEEK are a favorite of medical device manufacturers since they can withstand the heat from autoclaving without losing their rigidity. The biocompatibility of plastics is determined by USP Class VI and ISO 10993. Medical device manufacturers who want to ensure that theyre using the best materials should look for plastics that pass the standards of these tests. PTFE FEP PVDF are widely used Fluoropolymer in medical industry. Extensive use in catheters syringes dialyzers parts of xray. Liquid silicone rubber Medical grade liquid silicone rubber is preferred by many manufacturers because of its thermosetting property. Liquid silicone rubber complies with USP Class VI and ISO 10993 standards and it can be sterilized using autoclaves as well as Ebeam and gamma radiation processes. It also stays stable and maintains its resiliency and flexibility even when exposed to high temperatures. Implants Metals surgical steel titanium Pacemaker case Metals titanium Pacemakers Plastics metal polyurethane polyvinyl chloride Polyetherimide polyetheretherketone Catheters syringes dialyzers xray machines plastics PTFE FEP PVDF Xray machines liquid silicon rubber Product Segmentation activation Low demand Tantalum Platinum Low Perishability Low Demand Medium Price Slow moving L iquid silicone rubber Medium Perishability Low Demand High Price Fast moving Drugs I mplantable devices High Perishability High Demand Low Price Bulk purchase Surgical steel Titanium Polyurethane polyvinyl chloride PVC Polyetherimide PEI Low Perishability Low Demand High Price Long term forecast based on historical demand Price based on discounts quantity Long term forecast based on reorder frequency replenishment count Ex Milk bread Products with lumpy erratic demand Price based on seasons quantity Forecast based on demand seasonality and quality Ex Electronics dispenser Different product groups will be optimized differently based on lifecycle demand consumption rate price discounts List of Important Procurement KPIs Number of Suppliers Helps track the dependency level on suppliers Compliance Rate Verifies if suppliers meet compliance requirements S upplier quality rating Evaluate the quality of suppliers S upplier Ability A measure of suppliers ability to meet demand Supplier Defect Rate Helps evaluate the quality of individual suppliers Purchase Order Cycle Time  Helps identify whom to address urgent orders to Vendor Rejection Rate and Cost Helps evaluate internal quality management strategies Lead Time Gauge the total time to fulfill an order List of Important Manufacturing KPIs Overall Equipment Effectiveness OEE    measures the percentage of time that a machine or production line produces good quality articles during the scheduled time.       OEE Availability x Performance x Quality Workinprocess WIP measures the value of raw materials or subassemblies that have entered the manufacturing process before obtaining the finished good.       WIP Manufacturing Lead Time x Production Flow Value Ontimeinfull OTIF    measures the number of orders which had been delivered with the right quality and the right quantity on time to the customer.        OTIF Number of perfect orders Total number of orders Lead Time LT  It is the time it takes to fulfill an order from when it is confirmed until the order is delivered in full. Cost per Unit CPU  helps a manufacturing system to optimize the cost of the products.       CPU Direct Material Costs Direct Labor Costs Manufacturing Overhead Total units produced Production Downtime is a period when the manufacturing process is on hold and no products are produced Inventory Turnover Ratio The higher the inventory turnover rate is the more efficiently the supply chain is built.        Inventory Turnover Ratio COGS Avg. Inventory Production Schedule Attainment  reflects how well production is planned and how efficiently the production workers meet their targets.        Production Schedule Attainment Actual Output Planned Output x 100 Objective Challenges Material shortages post SC planning as variations were not factored low clear to build attributes Reasons for these challen

es Supplier uncertainty Inbound volatile demand Supplier OTIF external factors Inventory on hand poor quality long lead times high onhand inventory Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers lack of alternate suppliers defects manufacturing site locations with suppliers co manu Material shortages as variations were not factored low clear to build attributes and not synced with manufacturing plan Supplier uncertainty volatile demand P oor quality defects overmanufacturing waiting nonutilized talent transportation inventory motion and extra processing Defect long lead times Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers High risk potential from solesource suppliers and lack of subtier visibility Lack of visibility to BOM a global material dependency graph describing how material flows from raw to WIP to finished goods Manufacturers cannot react quickly and intelligently to changes without planning and scheduling tools Challenges Objective Improve clear to build Prod planning Inventory on Hand We can only clear to build something if all raw materials are available for it no material shortage. Availability is related to Supplier uncertainty Inbound Risk Resilience reliability. Predict material shortage using supplier uncertainty factors which will affect clear to build. Visualize your entire supply chain incl. all manufacturing sites on a world map to reveal weak spots such as supplier clusters in highrisk regions Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   purchase based on manufacturing strategies MTS MTO To deliver orderbased scheduling which can apply a ranking to prioritize the order process based on availability of resources and the materials required for the order Questions How much can I produce today given current inventory How much will I be able to produce in subsequent days as purchase orders sales orders and manufacturing runs continue What do I need to do in order to debottleneck manufacturing of a given finished good What am I clear to build How do I allocate limited raw materials to manufacturing orders To sales orders How do my allocation decisions impact revenue What do I need to do in order to debottleneck a given SKU My entire network How to get the materials that is needed connect manufacturing schedule material requirement order allocation How to prioritize the orders mtoeto new orders back orders Are the products available when I need them What will happen if I have long lead times 1. To synchronize purchasing and planning to the manufacturing schedule 2. To reduce unplanned variation of upstream and downstream material flows resulting in a mismatch of supply and demand 3. manufacturers continue to invest large amounts of money into new or upgraded ERP BI they still lack the ability to properly align the manufacturing plan from order input to order shipment 4. Advantage What if you knew you had all the parts that you need available to you prior to committing to your build and to create a manufacturing plan against resources available on hand 5. What if you could prioritize builds based on a priority Sales Order SO and see the effect on all your other deliveries 6. To have visibility on all your shortages at every stage before deciding prior to moving forward with your build 7. To understand which make orders have delay risks due to the lack of component availability 8. Expedite component supplies and Readjust planned manufacturing to expedite clear to build metrics of important orders CTB CTB visibility simulation and alerting capabilities across the entire materials management space Visibility preferred prioritization strategy for when multiple finished goods compete for the same raw material. Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   Flow Companies Products Advanced Molecular Nuclear Imaging Computed Tomography Diagnostic ECG Fluoroscopy Hospital Respiratory Care Imageguided therapy MRI Systems Solutions Patient Monitoring Solutions Radiation Oncology Radiography Xray Fluoroscopy Solutions Service Parts Sleep and Respiratory Care Ultrasound Ventilation Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers MedTech Products and Companies Becton Dickinson Medtronic thermos fisher ResearchDates Clear to build KPIs Clear to Build No all components are not completely available in on hand Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build 10 you can start 10 units of the make order this consumes 10 units of A 20 units of B and 30 units of C 10 units ready to build 100 order quantity 100 Clear to Build date June 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only June 14 Delay Due to Clear to Build 8 June 30 June 20 in workdays Ready to Build Quantity 10 0.1 100 Make Order Value 4500 45 1000 Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Inbound Supplier uncertainty reliability risk quality lack of alternate suppliers long lead times OTIF product categories based on attributes purchase based on manufacturing strategies MTS MTO ATO CTO ETO   Inventory Replenishment cycle kind of items Planned receipts actual receipts materials planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance   Onhand balances and replenishment of items is stored in the plants system. The information stored will include item description location quantities onhand vendor info MinMaxs and more. Establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout. Establish a minimum and a maximum inventory level for each part.   BOM BOM up to date in order to prevent inventory inaccuracies and spare parts shortages and to better plan for preventative maintenance or servicing. SUPPLIER PRODUCTS QUALITY OF THE PRODUCTS   manufacturing Prioritize the orders mtoeto new orders back orders planning and scheduling based on demand BOM   How to get the materials that is needed connect manufacturing schedule material requirement order allocation Match manufacturing with demand Normal Additional Clear to build Nodes Manufacturing Strategies and KPIs Nodes Manufacturing strategies KPIs ResearchDates Clear to build KPIs Clear to Build No all components are not completely available in on hand Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build 10 you can start 10 units of the make order this consumes 10 units of A 20 units of B and 30 units of C 10 units ready to build 100 order quantity 100 Clear to Build date June 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only June 14 Delay Due to Clear to Build 8 June 30 June 20 in workdays Ready to Build Quantity 10 0.1 100 Make Order Value 4500 45 1000 Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Inbound Supplier uncertainty reliability risk quality lack of alternate suppliers long lead times OTIF product categories based on attributes purchase based on manufacturing strategies MTS MTO ATO CTO ETO   Inventory Replenishment cycle kind of items Planned receipts actual receipts materials planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance   Onhand balances and replenishment of items is stored in the plants system. The information stored will include item description location quantities onhand vendor info MinMaxs and more. Establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout. Establish a minimum and a maximum inventory level for each part.   BOM BOM up to date in order to prevent inventory inaccuracies and spare parts shortages and to better plan for preventative maintenance or servicing. SUPPLIER PRODUCTS QUALITY OF THE PRODUCTS   manufacturing Prioritize the orders mtoeto new orders back orders planning and scheduling based on demand BOM   How to get the materials that is needed connect manufacturing schedule material requirement order allocation Match manufacturing with demand Normal Additional M anufacturing strategies that define sales in the Medtech Whyeasily forecasted products Howlow lt to orders produce prods in advance Forbulk low cost high qty low lt Glassware manufacturers MTS Whystd products Howlowers risk of overmanufacturing high manufacturing cost and lt Forlarge qty upon order costs high require enggdesign MTO Whyspeed and reduced waste Howparts are preproduced assemble when orders received mtomts Forlow qty orders requires enggdesign no options to configure Monitor ATO Whymass customization Howsubassemblies mtsassemble final assembly when order received Forlow qty orders customizations enggdesign lab equipment manufacturers CTO Whycomplex prods Howorder based long lt design engg and manufacturing Forlow qty orders enggdesign customized ETO Siemens Solution Challenge Material shortages as variations were not factored low clear to build attributes and not synced with manufacturing plan Supplier uncertainty volatile demand P oor quality Defect long lead times Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers High risk potential from solesource suppliers and lack of subtier visibility Lack of visibility to BOM a global material dependency graph describing how material flows from raw to WIP to finished goods Manufacturers cannot react quickly and intelligently to changes without planning and scheduling tools No supplier alternative counterfeit Objective Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Visualize your entire supply chain incl. all manufacturing sites on a world map to reveal weak spots such as supplier clusters in highrisk regions Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   purchase based on manufacturing strategies MTS MTO To deliver orderbased scheduling which can apply a ranking to prioritize the order process based on availability of resources and the materials required for the order   For whom we are solving this problem MedTech Manufacturers   What all challenges are being experienced Material shortages even after SC planning as variations were not factored in planning low clear to build attributes Reasons for these challenges Supplier uncertainty Inbound volatile demand Supplier OTIF external factors Inventory on hand poor quality long lead times high onhand inventory Medtech supply chains are highly globalized but manufacturers rarely have visibility beyond tier1 suppliers lack of alternate suppliers defects manufacturing site locations with suppliers co manu   Objective Improve clear to build Prod planning Inventory on Hand We can only clear to build something if all raw materials are available for it no material shortage. Availability is related to Supplier uncertaintyInbound Risk Resilience reliability. Predict material shortage using supplier uncertainty factors which will affect clear to build. Visualize your entire supply chain incl. all manufacturing sites on a world map to reveal weak spots such as supplier clusters in highrisk regions Visibility A tool to unpack the BOMs to create a global material dependency graph describing how material flows from raw to WIP to finished goods. Leveraging this model it can use inventory projections to determine the clear to build status for each finished good. Layered on top it can define their preferred prioritization strategy for when multiple finished goods compete for the same raw material. Simulation Visibility provides the endtoend truth about the current state of material availability and clear to build. Simulation enables users to simulate future states of the supply chain such as whatif scenarios on suppliers and delivery capabilities manufacturing and capacity capabilities or end customer demand plans and forecasts.   Nodes to focus Procurement to manufacturing planning Inbound Ask To deliver Deck Solution along with Mockups   Flow Demand Inventory Check product availability If OutofStock Manufacture the Product manufacturing planning team Check Inventory availability of raw materials buy from Vendor if not available MedTech companiesBecton Dickinson Medtronic thermos fisher Make to order MedTech Configure to order MedTech Engineer to order MedTech   Inbound Supplier uncertainty reliability risk quality lack of alternate suppliers long lead times OTIF product categories based on attributes purchase based on manufacturing strategies MTS MTO ATO CTO ETO   Inventory Replenishment cycle kind of items Planned receipts actual receipts materials planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance   Onhand balances and replenishment of items is stored in the plants system. The information stored will include item description location quantities onhand vendor info MinMaxs and more. Establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout. Establish a minimum and a maximum inventory level for each part.   BOM BOM up to date in order to prevent inventory inaccuracies and spare parts shortages and to better plan for preventative maintenance or servicing. SUPPLIER PRODUCTS QUALITY OF THE PRODUCTS   manufacturing Prioritize the orders mtoeto new orders back orders planning and scheduling based on demand BOM   How to get the materials that is needed connect manufacturing schedule material requirement order allocation Match manufacturing with demand Normal Additional   Does Sap captures Prod schedule demand enters into the system so as and when demand comes in inventory should be available   Are the products available when I need them Fulfill New order Prioritize new order or back order   What will happen if I have long lead times Results in shortage Plan new inventory and manufacturing schedule   How is my planning Planning based on Lead time risks reliability other factors. Plan well and advance the inventory position and manufacturing schedule Prioritization of the orders if deviation what to do trigger   Approach Network Optimization PO uncertainty prediction Supplier uncertainty prediction Product shortage prediction Impact What are the recommendations actionsinsights Overall Tactical Operational How do I plan clear to build monitoring the deviation predicting recommending.   After making a list of these critical items you can break them into priority categories using the  ABC and XYZ analysis methods .   Research Spare parts business oilgas industry tracking monitoring factors gas turbines supplier reliability when to buy new product build   What is the problem why are we solving the problem Planned receipts therefore are  crucial to regulating the level of inventory in the warehouse . Planned receipts are also important for determining whether you have received materials promised by vendors or inhouse manufacturing. Without them the system cannot establish a link between orders and received material.   if goods receipts are planned Materials Planning can monitor the stocks of ordered or manufactured materials and achieve an optimal inventory balance. Research MTO ETO CTO for medtech situations Inv policies Long lead time products   No seasonality Qure.ai engineer to order only change the algorithm Type of products Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers Research Item categories criticality Supplier categories Supplier uncertainty Clear to build Clear to build tells you whether you can start orders for make items. It depends on whether the materials required for these orders are available. An order is clear to build if all its components are onhand. start right away Prioritize based on onhand R isks due to the lack of component availability Expedite component supplies Readjust and resequence planned manufacturing to expedite clear to build metrics of important orders Clear to build for planned orders Check supplydemand view Clear to Build Status Yes All its components are completely available in on hand No On Time All its components are available when required for the order. Some components are not completely available in on hand right now. No At Risk The Clear to Build Date is later than the Need By Date. The order is at risk due to lack of components. Substitute components used indicators Manufacturers synchronize ComponentsRaw MaterialsSemiFinished goods to reduce shortage and working capital Traceability risks CTB visibility simulation and alerting capabilities across the entire materials management space Visibility preferred prioritization strategy for when multiple finished goods compete for the same raw material. How much can I produce today given current inventory How much will I be able to produce in subsequent days as purchase orders sales orders and manufacturing runs continue What do I need to do in order to debottleneck manufacturing of a given finished good Questions What am I clear to build How do I allocate limited raw materials to manufacturing orders To sales orders How do my allocation decisions impact revenue What do I need to do in order to debottleneck a given SKU My entire network ResearchDates Clear to build KPIs Clear to Build No all components are not completely available in on hand Clear to Build Component Availability 0 no components are completely available in on hand Ready to Build 10 you can start 10 units of the make order this consumes 10 units of A 20 units of B and 30 units of C 10 units ready to build 100 order quantity 100 Clear to Build date June 30 component B is the latest to arrive in on hand Clear to Build Date Purchased Components only June 14 Delay Due to Clear to Build 8 June 30 June 20 in workdays Ready to Build Quantity 10 0.1 100 Make Order Value 4500 45 1000 Substitute Components Used Indicator No KPIs Clear to Build Orders Clear to Build Component Availability Ready to Build Top Shortage Components Top Shortage Suppliers Research Clear to build simulations Identifying critical make orders that need to be expedited for clear to buildIn the SupplyDemand view filter for your key sales orders to identify whether there is any risk associated with replenishing themrev ship datedue date prioritize and deprioritize make orders and planned orders After making a list of these critical spare parts you can break them into priority categories using the  ABC and XYZ analysis methods . The goal here is to get a clear breakdown of the most frequently used parts then create a costeffective plan for replenishing them.  A good plan will help you avoid a stockout when a needed spare part is unavailable For each part Berger says establish the maximum level of stock you can accommodate as well as a reorder point the point at which you need to order more parts to prevent a stockout Calculate the Optimal Order Quantity maketostock MTS assemble to stock ATS maketoorder MTO configuretoorder CTO engineertoorder ETO Orde to cash cycle is long in medtech Why Information capture and no visibility Bulk orders Prod categories groups purpose Research 1. To synchronize purchasing and planning to the manufacturing schedule 2. To reduce unplanned variation of upstream and downstream material flows resulting in a mismatch of supply and demand 3. manufacturers continue to invest large amounts of money into new or upgraded ERP BI they still lack the ability to properly align the manufacturing plan from order input to order shipment 4. Advantage What if you knew you had all the parts that you need available to you prior to committing to your build and to create a manufacturing plan against resources available on hand 5. What if you could prioritize builds based on a priority Sales Order SO and see the effect on all your other deliveries 6. To have visibility on all your shortages at every stage before deciding prior to moving forward with your build 7. To understand which make orders have delay risks due to the lack of component availability 8. Expedite component supplies and Readjust planned manufacturing to expedite clear to build metrics of important orders Product Categorization based on Demand Supply Total Cost Analysis ABC Analysis Manufacturing Strategy MTS MTO ATO Suitable supply chain strategy Activation of Inventory Policies Relevant inventory models could be followed by analyzing product specific eorder points review periods orderuptolevels Cause Decide on the two major variables in an inventory control system order quantity ordering frequency Effect Analyze reorder pts order levels demand forecast to determine which inventory system to follow for each product group Smart Buying How to choose Inventory Policies What inventory models could be followed for different product groups by analyzing reorder points review periods orderuptolevels Cost Policies Smart Buying Products Advanced Molecular Nuclear Imaging Computed Tomography Diagnostic ECG Fluoroscopy Hospital Respiratory Care Imageguided therapy MRI Systems Solutions Patient Monitoring Solutions Radiation Oncology Radiography Xray Fluoroscopy Solutions Service Parts Sleep and Respiratory Care Ultrasound Ventilation Needles syringes mri xray ultrasound dentures braces knee implants artificial joints hearing aids pacemakers Clear to build BOM explosion and Raw material Segmentation What are the raw material categories Operational Tactical Whether the plan can be accomplished or not For what products critical do we clear to build What are my critical raw materials How to r eadjust and resequence manufacturing plan and prioritize the orders based on availability manufacturing strategy mto ato eto sales orders back orders planned orders Order allocation Are we adhering to the recommendedplanned build date How soon are we providing visibility to the customer incase of risksdelays How much can be produced as per Production plan in planning horizon How much lastminute changes can be accommodated What will be the impact on production plan if PO is delayed How t o have visibility on all your shortages inter dependencies between orders at every stage before deciding prior to moving forward with your build Clear to build Tactical Operational risks in readjusting Clear to build This slide talks about the questions that we are going to answer from our capability Critical points Tactical Critical products which needs 100ctb priority so those raw materials must be critical I have orders I have the inventory. Plan based on priority availability alternate BOM 100 or 100 We have to plan and recommend for a build date and check whether we are adhering to it Incase of risks delays how soon are we providing that visibility to the customer to avoid penaltiesreputation issues Operational Min Max production levels Build projection Risk prediction Shortage prediction visibility on interdependencies between orders Links Oracle Rapid Planning Implementation and Users Guide What is a cleartobuild concept in inventory control How is it achieved in a manufacturing organization Quora 80 Clear to Build solution for HighTech manufactures Dassault Systmes YouTube 2022_04_Clear_to_Build_WP_FINAL__1_.pdf Planning Goods Receipts SAP Help Portal Receipt SCM Portal Demand Supply Chain Glossary Bill of Materials BOM Meaning Purpose and Types 2 What can a CleartoBuild Module mean for your company LinkedIn manufacturing issues and what can be done about them Cascadia Seller Solutions 6 Common Quality Issues in the Manufacturing Industry How to Solve the 5 Top Challenges of Manufacturing Projects Mdh logo 7 Things to Consider for Successful Spare Parts Management Sigma Thermal 9 Tips for Managing and Optimizing Spare Parts Inventory 3 Tips to Optimize Your Spare Parts Inventory Control System PDF Rapid manufacturing in the spare parts supply chain Alternative approaches to capacity deployment How to keep spare part inventories accurate for maintenance departments Plant Engineering httpswww.plm.automation.siemens.comglobalplindustriesmedicaldevicespharmaceuticalsmanufacturingplanningscheduling.html

MedTech SC Deck - 2022.pptx

MedTech Supply Chain Capability June 2022 01 MedTech industry Changing dynamics Changing dynamics in MedTech industry Enable digitization Transforming from Doing digital to Being digital to provide connected solutions and achieve operational excellence Advance patient centricity Transitioning to patient centric ecosystem to empower and engage patients throughout their health journey Improve flexibility and sustainability Engaging in stresstesting operating models and increasing transparency to strengthen resilience and make the operations future ready Drive value amongst customers Evolving from a traditional supplier role to a valuedriven strategic business partner to their customers MedTech industrys Supply chain challenges Solutions to overcome them key to achieve success 01 02 03 04 02 Fractal Supply Chain Solution areas Fractal has enabled transformation initiatives and bottomline improvements for its clients across the supply chain 1.3Bn reduction in working capital over 4 years 2Bn Identified new growth opportunities 3Mn Savings with reduced off spec 15 Reduction in capex through longterm capacity planning 1Bn savings through e2e inventory optimization 25Mn reduction in transportation costs 52Mn savings through alternative formulations 1Mn savings per material due to optimized purchase EOQ Control Tower Single source of truth which would provide the right data at the right time to become more strategy focused Ability to easily track trace and audit medical devicesaccessories from supplier sourcing till patient delivery A ctionable Insights Benchmarks Risk identification mitigation early warning alerts Risk and Resilience Predict risk of delayed supply and their impact Enable MedTech with endtoend supply chain visibility to cover for delayed shipments due to 3PL 40 of total goods shipped Mitigation strategies or alternatives Demand prediction and service levels Advanced analytics to enable a better sense of demand and service levels Optimized allocation decisions for improved service levels and reduce Failure to Serve FTS Optimize segments in supply chain such as network design cost to serve Minimize inefficiencies Achieve shorter leadtime through optimizing the sourcing requirement Identify trends in lost devices or equipment due to damagelost in warehouse including product recalls Fractal has deep Supply chain expertise for MedTech Art of Possible 02 01 03 04 2021 Fractal Analytics Inc. All rights reserved Confidential enabling decisions across value chain through AIML interventions Control Tower 1 . Endtoend Supply chain visibility Real time metrics tracking across the Medical devices continuum of care Prevention Diagnosis Monitoring Treatment and Care Environment metrics such as sensor drift temperature etc. during device manufacturing Early warning alerts and cross functional nudges in case of critical dip in devices quality metrics 2 . Digital enablement Digital accelerates cross functional decision making homecare personalized healthcare and remote interactions with multiple stakeholders such as payers Build analytics engine and scalable technology plan for a successful digital adoption Inventory optimization Demand prediction by u sing AIML techniques by using the relevant MedTech input factors Product Device sales price patent expiry efficacy safety etc. Market Formulary Therapeutic area spending etc. External GDP growth Healthcare spending Disease prevalence etc. Manage optimized levels and prioritize inventory utilization based on MedTech variables such as Product shelf life Unique suppliers Product type highlow velocity Systematic alerts for expired and recalled inventory 2. Service levels Quantify service level risks owing to Inventory availability warehousing and logistics disruptions shortage at each Supply Chain node delays in POs Minimize inefficiencies Optimizing Distribution by T racking Multimodal sh ipment across multiple levels D evice components SKUs Purchase orders etc. around key metrics such as OTIF Fill rates Understanding distribution impact of productSKU fragmentation across regions owing to differing regulations approvals 2. Optimizing Production by A. Predictive maintenance to proactively predict a device failure and thus intervene prior to break down B. Production Digital twins to enable simulation across all manufacturing levels Risk and Resilience  Supplier analytics scorecard using key inputs such as performance spend and cycle times financial vulnerability of Tier 123 suppliers ESG performance etc. Build a MedTech resilience framework through the lens of 1. Regulatory qualified suppliers 2. Process for ex. safety procedures 3. Product post device launch performance 4. Structural manufacturing location supplier location etc. Dualalternate sourcing for raw materials redeploy from other networks Tracking Counterfeit risks at any node of the supply chain 01 02 03 04 MedTech Supply chain focus Fractals assessment of current need state Fractal research Analysis based on the challenges cited in MedTech firms annual reports investor presentations INTERNAL SLIDE 03 Fractal Selected Impact examples Transformed SOP process with Supply Chain Planning Control tower Business complexity PLAN SOURCE MAKE DELIVER 11 Product platforms varied recipe and suppliers 25 Plants across North America with copack and coman complexity 13 Distribution centres 42 Customer distribution centres Business needs Need for flexible views Comparison of data from different perspectives View historic vs. future trends View data as per specific needs for a user country vs. function vs. business level Need for next best actions Actionable Insights Benchmarks Risk identification mitigation Early warning alerts Create a single source of truth which would provide the right data at the right time to become more strategy focused Limited trust in data as everyone had their own interpretation multiple tools were needed to get e2e picture Inbound Supplier risk prediction and traceability Collaborative platform for inbound raw material visibility Early warning signals on risks Recommendations to mitigate risks Real Time Visibility Limited visibility of supply at risk Slow tradeoffs analysis for mitigation strategies Limited impact identification on Supply demand reconciliation SOURCE MAKE DELIVER 11 Product platforms varied recipe and suppliers 25 Plants across North America with copack and coman complexity 13 Distribution centres 42 Customer distribution centres Improved service levels by 8 reduced penalties through On time delivery risk predictions Solution features Recommend ideal pickup window based on MABD Recommend alerts based on risk score like change in LRDT Recalibrate appointment times Optimize Load Ready Date Time schedule to maximize on time deliveries Business problem 24Mn loss in penalties due to shipments delay for a top category 15 shipments were not on time Over allocation of warehouse docks leading to frequent reschedules DC processes not completed on time Carrier delays Carrier decides customer appointment time schedules pickups OPTIMAL PICKUP WINDOW PER LOAD risk scores using a Bayes Network model Solution approach 04 Fractal Selected Solutions Examples Supply Chain Overall Status Solution Example Control Tower Monthly Average Total Demand CompliancetoSchedule Daily Order Compliance Customer Service Levels Network Inventory Meals Beverages 40M units CSnacks 25M units Volume 65M Revenue 900M 12 YoY Inventory Storage Cost 0.8 unit 10M DemandSupply Mismatch High inventory for Soup Broth Alerts Bias 7 Production Coman Top 5 customers by revenue 37M Executive cockpit view of Control Tower Solution Example Risk and Resilience13 Solution Example Risk and Resilience23 Solution Example Risk and Resilience 33 Solution Example Improved Service levels OOS visibility across Store network OOS visibility across Store network Recommendations on DOS to reduce OOS

MedTech Supply chain focus – assessment & leads.pptx

MedTech Supply chain focus Fractals assessment of current need state INTERNAL SLIDE Fractal research Analysis based on the challenges cited in MedTech firms annual reports investor presentations and client conversations

Optimal Buying Pattern.pptx

Optimal Buying Pattern September 2022 01 02 03 04 Problem statement Solution construct Data requirements Mockups Agenda 01 Problem statement Optimizing buying to better map demand control inventory flow Challenges Products are suboptimally ordered with limited visibility across supplier dynamics Choosing incorrect supplier leads to higher costs lead times leading to reduced serviceability Little emphasis is given on choosing what quantity to buy at what time resulting in inventory build up or unavoidable stock outs. Objective To optimize buying patterns for retailers by determining dynamic AI learned rules . R educing total operational costs while ensuring serviceability. Balancing between avoiding stockouts and inventory build up. Outcomes Segmenting products suppliers to better understand portfolio. Choosing which products to buy whom to buy from when to buy and at what margins. D etermining the optimal time to buy from suppliers by considering constraints Inventory demand lead time maximizing service within external and internal controls Smart Buying 02 High level Solution approach Improve buying patterns Smart Procurement Intelligent buying decisions Selection Segmentation Placement Segment the products to better map supply with demand Demand Sensing based on historical data and current events Maintaining balance between overstocking and understocking while minimizing total cost for each product segment By using suppliers catalogue get the availability of each product price ranges unit costs and discounts etc Analyze the list of alternate suppliers available to order from at different price ranges Provide alternate backup suppliers in case of disruptions Determine an optimal reorder point Take in to account the i nventory on hand demand lead time and season to determine the optimal price Provide optimal safety stock levels to improve order cycle service levels with uncertain lead times Segment and analyze products at retail stores to arrive at optimal order quantities Identify potential suppliers and choose them to arrive at optimal order allocation Generate a calendar for Optimal scheduling of orders from list of selected suppliers Smart Buying Segmentation Classification of products to better map supply with demand Product segmentation based on demand variability consumption rate service levels and reorder frequency Optimal ordering policy to be framed for each product group Demand Price elasticity Forecast demand based on demand seasonality lifecycle Demand sensing layer to take current events into account Creating price demandPrice 1Demand elasticity models to get price bounds Price Min Price Max Selection Placement Select the optimal suppliers and place orders with right quantity at right time Objective is to Min Cost lead time Maximize Margin quality supplier Reputation Use relevant InventoryDemand constraints Recommendations Prescriptive recommendations on optimal inventory Safety stock ROP sourcing MOQ EOQ lead time controls Buying patterns recommendationsJIT anticipation buffer strategic buying Multiple switching rules based on order quantities and suppliersMOQ Dynamic buying trends assessment for products to predict optimal patterns Smart Buying Overall analytical solution Construct to smart buying Goals Demand Sensing Price Elasticity Segmentation Multiobjective Optimization Recommendation Classification of products to better map supply with demand Extract Nyears of data. Check for seasonality demand variability Arrive at the products and categories that qualifies for Demand Forecasting . Add Demand Sensing to generate accurate forecasts Use Quantile Regression for MinMax Price Range prediction Establish a Causal Network such that Price fTime Demand Seasonality Multi Objective optimization by Mixed Integer Linear Programming. Assign weights to each objective function The model is reiterated with different weights till minimum threshold reached to arrive at optimal values Decide on Objectives Min Cost Max Profit Max Quality Min Lead time Max Reputation Decide on Constraints Inventory Supplier Price Min Demand Time Period Recommendations on optimal inventory buying patterns switching rules based on supplier MOQ and the demand Choose which products to buy using segmentation and extract insights Smart Buying Data Preprocessing to analyze and derive features for the model K Means Hierarchical clustering for SKU segmentation based on business inputs FSN XYZ Median based analysis to group and select the products to analyze Classification of products based on different supply and demand indicators Order quantity Demand Reorder frequency Replenishment count Product lifecycle discounts Grouping like behaving Suppliers since they exhibit different behaviors based on the nature of the business model. Product segments and selection based on clustering analysis Explore results and validate output with business on the products segments Segmentation to better understand portfolio Segmentation Approach Outcomes Smart Buying Product Segmentation activation Low demand Low Perishability Low Demand Medium Price Slow moving Medium Perishability Low Demand High Price Perishables Fast moving High Perishability High Demand Low Price Bulk purchase Low Perishability Low Demand High Price Long term forecast based on historical demand Price based on discounts quantity Long term forecast based on reorder frequency replenishment count Ex Milk bread Products with lumpy erratic demand Price based on seasons quantity Forecast based on demand seasonality and quality Ex Electronics dispenser Different product groups will be optimized differently based on lifecycle demand consumption rate price discounts Smart Buying Supplier activation mapping Different supplier groups discounting will be activated at different points in time and different conditions 01 Fixed Price discounts Variable Price discounts Volume based discounts Time based discounts VolumeTime based discounts Fixed price throughout a time zone for a given product from Independent suppliers Best for unique artisanal orders with a tat of 16months Different price during different time zone for a given product from Importers Best for seasonal orders with a tat of 16months Suppliers with discount grid based on the order quantity from Wholesalers Best for bulk orders with a tat of 3weeks2months Suppliers with discount grid based on the time of order from Manufacturers Importers and wholesalers Best for seasonal demand products with a tat of 16months Suppliers with discount grid based on volume and time of the order from Manufacturers Best for custom and bulk orders with a tat of 1week2months Smart Buying Approach Business requirement Creating long term forecasts on sales historyPOS data Making forecasts more robust for short term dynamic inventory planning by adding current events from external data sources Provide a range of prices available at Product X Vendor level to the retailers Long term forecast models based on different demand patterns Demand sensing layer on demand signal changes to execute shortterm forecast adjustments Causal model to identify the network impacting the price and quantile regression model to predict the price range Sensing demand and Price elasticity using analytics Outcomes Improved short term forecast accuracy and demand responsive supply chains Optimal inventory and min overall costs by deploying demand sensing model Benefits including high profit margins decreased inventory levels and cost leads to better order performance Smart Buying Predictive models to better sense and forecast demand Demand patterns Seasonality trends demand variability Business decisions Changes in promotions price storage External factors Weather holidays inflation congestion AIML Accurate forecasts for retail planning Forecast per product months day and store or fulfillment channel Longterm forecast horizon Demand Engineering Inventory POS data for a minimum of N years along with current external d ata m acroeconomic i ndicators Check for seasonality demand and classify products into lumpy smooth intermittent erratic by CV and ADI D emand Sensing Product level statisticalMLensemble forecast models C ompare forecast with actual demand to calculate the bias and error. E xecute shortterm forecast adjustments based on sensed demand patterns Outcomes Objective is to generate demand forecast which acts as a constraint for the optimization layer Generate long term forecast for 12 18 months quantity for each product or category 1 2 3 Smart Buying Causal impact mapping to simulate optimal price ranges Establish causality and generate a causal network to identify the factors impacting price Quantile Regression Outcomes Predicted Minimum and Maximum selling prices for a product at a given time period Price will act as a constraint as well as a criterion to select or reject a supplier Causal Modelling Estimate price distribution using MC sampling and the establish causal network Using quantile regression predict the minimum and maximum price of a product at a given time by having 50 for min price and 95 for max price range Product Features Purchase Frequency Time Historical demand Features impacting Price Seasonality Smart Buying Optimal Buying through Optimization algorithms Quality Checks Network disruptions External Factors Inputs Multi Objective Optimization engine to incorporate weighted function for all the objectives and constraints Evaluation Criteria to include Cost Profit margin Perishability Lead Time Delivery delay Supplier Reputation Additional business heuristics to make account for inventory levels constraints and other external factors Constraints Port Congestions Buying Patterns Optimization Product Availability Price Inventory Position Inventory Demand Min Demand Supplier Activation Storage Price Time Window Quality Cost Supplier Reputation Climatic changes Macroeconomic Indicators Smart Buying Multi Objective Optimization Mathematical programming model considering the objectives constraints and business requirements Obj 1 Max Margin Min Cost Obj 2 Maximize Quality Obj 3 Minimize Lead Time Delay Obj 4 Maximize Supplier Reputation Input Parameters Demand Inventory Supplier Information Cost Prices Time Window Constraints Inventory on hand Safety stock MOQ Supplier activation Product choice activation Minimum d emand M axMin price range Storage capacity Inputs Constraints Generate various sets of weights using an optimal frontier. Weights ranges to be permuted on relative importance and criticality of achievement of each objective. Final Weights to be optimized using Rank Order Centroid Ratio Pairwise comparison satisfying business objectives. Mixed Integer Linear Programming Maximize Minimize W 1 Obj 1 W 2 Obj 2 ........ W 4 Obj 4 weighted sum of the given objectives Multiobjective allocation and scheduling optimization Smart Buying Dynamic recommendation mechanism Prescriptive Recommendations Safety Stock EOQ ROP Enhance E2E visibility on key KPIs for r ecommending optimal inventory and sourcing controls across various product categories Inventory Policies Demand Determining optimal buying policies such as JIT Bulk orders for each product group based on demand Inventory and discounts Switching Rules Supplier MOQ Order Quantity The objective is to arrive at rules for changing the order quantity by switching to a lower or higher value of Supplier MOQ depending on demand Inventory on hand Smart Buying S afety Stock Objective Minimize costs holding ordering Generating EOQ for each time period given constraints on Demand Inventory Storage Calculated initial safety stock based on daily usage and lead time Safety Stock simulation to arrive at optimal level that balance between service levels and minimizes cost Prescriptive Recommendations EOQ To provide prescriptive recommendations on sourcing controls and optimal inventory across various product categories Reorder Points One of the main inventory sourcing control method lies in reorder points Given safety stock the exact time to reorder can be calculated Reorder Point Analyze demand EOQ Forecast Demand Supplier Constraints Time Constraints Cost Constraints Cost Service balance Controls Smart Buying Prescriptive Recommendations Initial Safety Stock Max Daily Usage x Max Lead Timeavg daily usage avg lead time Lower initial value by 25 each time to reach the optimal value that minimizes total cost while hitting the service level target Inclusion of safety stock as constraint improves order cycle service levels Reasons to Carry Safety Stock Unforeseen Supply Variation Customer Satisfaction Inaccurate Demand Estimation Determined by inventory related costs such as ordering costs carrying costs stockout costs Standard EOQ is not applicable Volatile Demand Stock Outs Varying ordering cost unit Discounts Offered Triggers the purchase of a predetermined amount of replenishment inventory Reorder point varies across product groups due to different usage rates lead time Reorder Point Safety Stock Average Daily Sale or Forecast Average Lead Time EOQ Safety Stock Reorder Point Controls Smart Buying Activation of Inventory Policies Relevant inventory models could be followed by analyzing product specific eorder points review periods orderuptolevels Policies Cause Decide on the two major variables in an inventory control system order quantity ordering frequency Effect Analyze reorder pts order levels demand forecast to determine which inventory system to follow for each product group Smart Buying How to choose Inventory Policies What inventory models could be followed for different product groups by analyzing reorder points review periods orderuptolevels Cost Policies Smart Buying Switching Rule Set Mechanism S witching rules reduce average inventory overall cost stockout events CV of next month forecasted sales vs CV of P1M historical sales Average demand forecast for the next 4 weeks vs average rolling forecast for the entire year Demand forecast for the upcoming 4th week vs. actual usage for the previous year Rolling 3 months inventory vs previous R3M Switching Switch between suppliers and schedule providing different MOQ based on demand and inventory policies dynamically Smart Buying 03 Data Requirements We would consider following data sets as input to AIML exercise Suppliers We would consider following data as input to AIML exercise Product We would consider following data as input to AIML exercise Re tailers We would consider following data as input to AIML exercise External 04 Mockups Revenue 100 Gross Margin 20 Gross Margin 33 Business Overview Business Analysis Recommendation Revenue GM CostSpend What to buy product segment How much to buy demand forecast At what rate should I buy price Whom to buy supplier Inventory Sourcing controls Inventory policy Switching rules Smart Buying Story Flow Overview Smart Buying Story Flow Overview Business overview with bookmarks Brand 1 demand has increased by 9 Prod 1 current stock has decreased by 6 Supplier 1 Prod 1 discount increased by 2 Prod 1 demand increase and Price decreased by 5 7 9 7 3 2 Smart Buying Story Flow Overview Business overview with bookmarks Brand 1 demand has increased by 9 Prod 1 current stock has decreased by 6 Supplier 1 Prod 1 discount increased by 2 Prod 1 demand increase and Price decreased by 5 7 9 7 3 2 What to buy Category Brand Product xaxis with quant variables something which is needed Im not having enuf of it. KPIs with Time Product level Quadrant demand variability and supplyhigh demand low supply low demand high supply etc choose 3 items from 3 quadrants Replenishment Count Consumption rate Historical Demand Perishability Score Inventory On Handss Historical patterns Smart Buying Story Flow Product segmentation past For those 3 items historical demand purchase freq historical. Whom did I order from kpis which supplier we got them from how many suppliers I got from dive into those supplierall historical supplier kpis box or violin for all supplier kpis Price minmax vs time vs supplier category vs brand vs product qty and variability consumption rate perishability demand High Demand Low Supply Low Demand High Supply Smart Buying Story Flow Demand Patterns High Demand High Supply SKU 1 SKU 6 SKU 2 past For those 3 items historical demand purchase freq historical. Whom did I order from kpis which supplier we got them from how many suppliers I got from dive into those supplierall historical supplier kpis box or violin for all supplier kpis Price minmax vs time vs supplier category vs brand vs product qty and variability consumption rate perishability demand High Demand Low Supply Low Demand High Supply Smart Buying Story Flow Demand Patterns High Demand High Supply SKU 1 SKU 6 SKU 2 past For those 3 items historical demand purchase freq historical. Whom did I order from kpis which supplier we got them from how many suppliers I got from dive into those supplierall historical supplier kpis box or violin for all supplier kpis Price minmax vs time vs supplier category vs brand vs product qty and variability consumption rate perishability demand High Demand Low Supply Low Demand High Supply Smart Buying Story Flow Demand Patterns High Demand High Supply SKU 1 SKU 6 SKU 2 Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price Elasticity Week 4 Week 15 Q1 FY 2122 FY 2021 SKU 1 SKU 6 SKU 2 Avg Demand 30 17 Q2 Before After Avg Demand 70 Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price Elasticity Week 4 Week 15 Q1 FY 2122 FY 2021 SKU 1 SKU 6 SKU 2 Avg Demand 35 5 Q2 Before After Avg Demand 35 Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price Elasticity Week 4 Week 15 Q1 FY 2122 FY 2021 SKU 1 SKU 6 SKU 2 Avg Demand 60 12 Q2 Before After Avg Demand 70 Whom at what price 1 st opt basewithout doing anything Given 3 items these are all the suppliers this much yu shd buy at this price 2 nd opt what if analysis on supplier and price Change lead time score discount etc Come up with optimized output Then show comparison cost profit lead time reputation obj 15 Margin Optimized 25 Quality Optimized 20 Lead Time Optimized 10 Reputation Optimized Smart Buying Story Flow Optimization SKU 1 SKU 6 SKU 2 13 Cost Optimized Default Optimized Whom at what price 1 st opt basewithout doing anything Given 3 items these are all the suppliers this much yu shd buy at this price 2 nd opt what if analysis on supplier and price Change lead time score discount etc Come up with optimized output Then show comparison cost profit lead time reputation obj 15 Margin Optimized 25 Quality Optimized 20 Lead Time Optimized 10 Reputation Optimized Smart Buying Story Flow Optimization SKU 1 SKU 6 SKU 2 13 Cost Optimized Default Optimized Whom at what price 1 st opt basewithout doing anything Given 3 items these are all the suppliers this much yu shd buy at this price 2 nd opt what if analysis on supplier and price Change lead time score discount etc Come up with optimized output Then show comparison cost profit lead time reputation obj 14 Margin Optimized 22 Quality Optimized 18 Lead Time Optimized 8 Reputation Optimized Smart Buying Story Flow Optimization SKU 1 SKU 6 SKU 2 12 Cost Optimized Default Optimized Whom at what price 1 st opt basewithout doing anything Given 3 items these are all the suppliers this much yu shd buy at this price 2 nd opt what if analysis on supplier and price Change lead time score discount etc Come up with optimized output Then show comparison cost profit lead time reputation obj 11 Margin Optimized 21 Quality Optimized 15 Lead Time Optimized 6 Reputation Optimized Smart Buying Story Flow Optimization SKU 1 SKU 6 SKU 2 10 Cost Optimized Default Optimized Message Switching rules Ss minmax inv moq supplier eoq rop Switch to different values of the MOQ depending on the value of the demand rop doubt Jit for perishables with sudden drop in forecasted demand as compared to Previous year Switch to LowerHigher Safety Stock with next 4 weeks Demand forecast continuously lessergreater than average yearly forecast Switch from Continuous to Fixed Order Policy with higher demand fluctuation Switching between SuppliersMOQ Current Forecast Smart Buying Story Flow Recommendations Switching between SuppliersMOQ Forecast Ahead Switching between SuppliersMOQ Current Forecast Switching between SuppliersMOQ Forecast Ahead Message Switching rules Ss minmax inv moq supplier eoq rop Switch to different values of the MOQ depending on the value of the demand rop doubt Jit for perishables with sudden drop in forecasted demand as compared to Previous year Switch to LowerHigher Safety Stock with next 4 weeks Demand forecast continuously lessergreater than average yearly forecast Switch from Continuous to Fixed Order Policy with higher demand fluctuation Switching between SuppliersMOQ Current Forecast Smart Buying Story Flow Recommendations Switching between SuppliersMOQ Forecast Ahead Switching between SuppliersMOQ Current Forecast Switching between SuppliersMOQ Forecast Ahead Story Flow Smart Buying Rough Summary costrevenue gross margin gm 3 sections business overview analysis and decisionsrecommendations 1 slide Business overview cost gm revenue service level stockouts period graph productsupplier level only buying side 1 slide Business overviewlinksrev 1 slide cost 1 slide gm 1 slide productsupplier service levels stockout only high level Business analysis Product product segment wise demand forecastsensing product wise price range product wise what and how much 1 slide Supplier supplier catalog score discounts price alternate suppliers whom to buy right quantity right time Optimization objective and constraints 23 slides optimal reorder point optimal safety stock the i nventory on hand demand lead time and season to determine the optimal price Recommendations On inventory eoq rop ss and sourcing moq leadtime 1 slide On inventory policies which inventory type should I have by seeing my reorder points demand order frequency 1 slide On switching rule if else based on demand 1 slide 45 slides O verview Segment price model prod what and how much 1 Supplier opt whom to buy Opt right qty right time R ecommendation product segmentation category vs brand vs product picking few products  3 items Item A OVERVIEW WHAT HOW MUCH WHEN WHOM AT WHAT PRICE Mile stone 1 Historial behaviours High level category vs brand vs product demand supplier whom do i order from Supplier equation which all suppliers supplier analysis   Supplier risk ld time discount late delivery order qty price reputation defects availability otif procurement cycle violin chart of information minmax supplier price min max vs time vs supplier SEGMENTATION product segmentation category vs brand vs product picking few products  3 items DEMAND category vs brand vs product qty and variability consumption rate perishability demand outlook 3 items  twist 1 good 1 med 1 bad days in hand 100 qty 20 qty in future how demand is looking forecast forecast INTERVAL Mile stone 2 Demadn price elasticity DEMAND AND PRICE demand forecast demand price elasticity 1st simulation what price should i buy simulation of demand and price Mile stone 3 supplier   OPTIMIZATION optimizer base iten 1 s1 10 100 2nd simulator what if analysis on suppliers and price simulate supplier analysis   Supplier risk ld time discount late delivery order qty price reputation defects availability otif procurement cycle violin chart of information minmax optimizer new version simulation new output comparison how base vs new ouput is giving you value Item 1 old 100 95 cost lead time availability how solution will give them value RECOMMENDATION suggest suppliers look at the current vs incoming recommending 3 items switching rules ss min max inve rop eoq supplier message THE END Smart Buying Story Flow Overview Business overview with bookmarks What to buy How much to buy Whom to buy At what Price to buy Recommendations Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price Elasticity SKU 1 SKU 6 SKU 2 Min Price 2 Max Price 5 Avg Demand 60 Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price Elasticity SKU 1 SKU 6 SKU 2 Min Price 2 Max Price 6 Avg Demand 65 Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price Elasticity SKU 1 SKU 6 SKU 2 Min Price 2 Max Price 6 Avg Demand 50 Future How much and then demand forecast values demand sensing inventory on hand demand so I need to order something Good medium bad needhave I need this much how much I have What price should I buy them from demand price equation Smart Buying Story Flow Demand Forecast and Price past For those 3 items historical demand purchase freq historical. Whom did I order from kpis which supplier we got them from how many suppliers I got from dive into those supplierall historical supplier kpis box or violin for all supplier kpis Price minmax vs time vs supplier category vs brand vs product qty and variability consumption rate perishability demand Lead timeprice across Suppliers Smart Buying Story Flow Demand Behavior Smart Buying Story Flow Overview To show overall rev gm gm supplier wise product reve and order qty product wise suppliers rev and order qty product wise uniq suppliers brand wise order qty period graph for all three main kpis product wise stocksales and service levels Smart Buying Story Flow Product Segmentation and Pricing Segment demand consumption rate service level reorder freq Demand forecast and sensing layer product wise Price range product wise Replenishment Count Consumption rate Historical Demand Forecast Perishability Score Inventory On Handss Cost Gross Profit Smart Buying Story Flow Product Segmentation and Pricing Product segmentation based on demand variability consumption rate service levels and reorder frequency Optimal ordering policy to be framed for each product group Smart Buying Story Flow Supplier Supplier group Qty dis line unit price line period Product single select bookmarks MOQ by product Price by product Discountby Product Order Quantity Smart Buying Story Flow Supplier Supplier group Qty dis line unit price line period Product single select bookmarks Smart Buying Story Flow Recommendations Week 4 Week 15 Switch to different values of the MOQ depending on the value of the demand rop doubt Jit for perishables with sudden drop in forecasted demand as compared to Previous year Switch to LowerHigher Safety Stock with next 4 weeks Demand forecast continuously lessergreater than average yearly forecast Switch from Continuous to Fixed Order Policy with higher demand fluctuation Switching between SuppliersMOQ Switching between SuppliersMOQ Switching for InventorySS Switching for InventorySS Smart Buying 120M Products 120M Order Qty Story Flow Overview Product wise unique suppliers To show overall rev gm gm supplier wise product reve and order qty product wise suppliers rev and order qty product wise uniq suppliers brand wise order qty period graph for all three main kpis product wise stocksales and service levels Story Flow Product Segmentation and Pricing Smart Buying SKU Demand against time Price Range vs Demand Time Replenishment Count Consumption rate Historical Demand Forecast Perishability Score Inventory On Handss Cost Gross Profit Product vs Quantity Week 4 Week 15 Actual vs Forecasted Demand Segment demand consumption rate service level reorder freq Demand forecast and sensing layer product wise Price range product wise Procurement cycle vs Product Replenishment Count Consumption rate Historical Demand Forecast Perishability Score Inventory On Handss Cost Gross Profit Story Flow Supplier Whom to buy Smart Buying Week 4 Week 15 MOQ by product Price by product Discounts by Product Order Quantity Order Qty vs Discount Supplier group Qty dis line unit price line period Product single select bookmarks Order Qty vs Unit cost Story Flow Optimization Smart Buying Min Demand MOQSafety Stock Storage Capacity External factors Week 4 Week 15 Min Price Max Price ProfitCost Objective Functions as per priority Story Flow Recommendations Smart Buying Current demand forecast vs avg forecast Switching rule 1 Current demand forecast vs. actual usage for the previous year Switching rule 3 Demand forecast for the 4th week vs. actual usage for the previous year Switching rule 4 demand forecast for the 4 th week vs avg forecast Switching 2 Switch to different values of the MOQ depending on the value of the demand rop doubt Jit for perishables Switch to lowerhigher ss demand higherlower than avg yrly Recommendation Product Category All Region All Brand All Product Type All SKU All Switching rules Simulation Output Product A Forecast Actuals Less Volatile Low Demand Switching rules Simulation Output Inventory Current R3M Vs Previous R3M 10 Forecast Actuals Product A Service levels All Not using this coz this needs iteration on moq values ss to obtain the values that give us the lowest cost and achieve the target service level opt Optimization Product Category All Region All Brand All Product Type All SKU All Week 4 Week 15 Min Price Max Price Min Demand MOQSafety Stock Storage Capacity ProfitCost Objective Functions as per priority External factors Optimisation 2020 Fractal Analytics Inc. All rights reserved Confidential Supplier selection optimized oqs Mark All Order Analysis Unmark All enter enter enter enter enter enter enter Low volatility sku recommendations PRODUCT ANALYSIS Product Category All Region All Brand All Product Type All SKU All Quantity Ordered Across Suppliers moq unit price disc SKU Demand against time Supplier SKU 1 Week 4 Week 15 Forecast Replenishment Count Historical Demand Demand Volatility Perishability Score Inventory On Hand Inv Cost Holding Carrying Gross Profit Choose metric Analysis Across SKUs SKU Min and Max Price Range against time Supplier SKU 1 Forecast Replenishment Count Historical Demand Demand Volatility Perishability Score Inventory On Hand Inv Cost Holding Carrying Gross Profit Week 4 SUPPLIER ANALYSIS whom to buy Product Category All Region All Brand All Product Type All SKU All Week 4 Week 15 MOQ by product Price by product Discounts by Product Order Quantity Choose metric SKU Demand for S1 Analysis Across Suppliers Supplier S1 Story Flow BAProduct Segment whattobuy Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Product Product Product Story Flow BAProduct Segment whattobuy Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch 12 Number of Suppliers FSN Analysis Forecast Replenishment Count Historical Demand Demand Volatility Price Perishability Score Choose metric Recommendation variation wd servic

levelpolicy types etc Overview V endor All Region All Brand All Product Type All Time SKU All Service Levels Story Flow Summary RevGM Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Product Supplier Story Flow Summary Cost Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Product Supplier Story Flow BA ProductSupplierRevCost Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Quantity Quantity Story Flow BA demnd f howmuchtobuy Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Demand Volume Perishability Demand Volatility High Medium Low Story Flow BApricewhat rate Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Story Flow BAsupplierwhom to buy Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Story Flow BAsupplierwhom to buy Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Story Flow Optimization Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Story Flow Recommendations InvSourcing Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Story Flow Recommendations Inv policy Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Story Flow Recommendations Switching rules Smart Buying FY Quarter Month Supplier Product Product Group Brand Branch Overview Smart Buying Add suppliertime filter Service level drops can be seen in the months of Jan and September SKU Demand against time Product Segmentation Smart Buying Future Forecast Quantity Ordered Demand Volatility Replenish Count FSN Analysis SKU3025226 High Forecast Error with ve bias Product Segmentation Forecast Step SKU 3024796 SKU with High Volatility Product Segmentation Forecast Step Switching rules Fixed Quantity mock up Switch to Supplier providing lower MOQ in case of Increasing Inventory Built up Order in smaller multiples of MOQ at higher discount rates with demand forecast exceeding actuals Objective Business Need With volatile demand ordering from one supplier with one large MOQ or MOQ multiplescan lead to overstocking Input Suppliers MOQ price unit cost Stockout events Order Quantity Approach Switch between suppliers providing different MOQs Statistical methods Simulation to compare metrics used in rules. Impact S witching rules reduce average inventory overall cost stockout events Smart Buying 1 2 Switching rules Simulation Output Why Inventory Current R3M Vs Previous R3M 10 Forecast Actuals Product A KPIs Used to trigger Switching Rules Switching Rule Switching rules Fixed Supplier mock up Switch to Higher Safety Stock with Demand forecast continuously exceeding Actuals Objective Business Need V ariations in demand patterns demand volume and supplier cost across product groups and time periods Input Supplier Simulated Safety Stock Values Cost Demand Volatility and Volume Time Approach Switch to lower or higher values of SS depending on demandinventory rules. Switch between Periodic and Continuous Review basis demand volatility groups SS requirement increased techlabor costs due to continuous reviewing Switch between JITBulk policy as per Demand Volume and Cost Impact S witching rules reduce average inventory overall cost stockout events Smart Buying 1 Switch to JIT policy for low demand high cost products Why KPIs Used to trigger Switching Rules Switching Rule Switch from Continuous to Periodic review for less volatile products 2 3 Switching rules Simulation Output Product A Forecast Actuals Less Volatile Low Demand SKU 3013419 SKU with Low Volatility Product Segmentation Forecast Step SKU 3013419 SKU with Low Volatility Optimizing buying planning efficiency to better map demand control inventory flow To optimize buying patters for retailers by determining dynamic AI learned rules in a way that reduces total operational costs while ensuring serviceability. Balancing between avoiding stockouts and inventory build up. Products are suboptimally mapped to purchase orders with limited visibility across supplier dynamics Choosing incorrect supplier could lead to higher costs and lead times leading to reduced margins serviceability Objective Challenges Desired outcomes Smart procurement Segmenting products to better understand Item portfolio. Choosing which products to buy whom to buy from when to buy and at what margins. Identify potential suppliers to procure from and choosing the optimal supplier. The buyer selects the right supplier and orders an appropriate quantity. D etermining the optimal time to buy from suppliers by taking in account of the constraints such as Inventory positions demand lead time to maximize service within constraints of external and internal controls Little emphasis is given on choosing what quantity to buy at what time resulting in either inventory build up or unavoidable stock outs . Align in to phi framework the 3 boxer Prescriptive Recommendations Smart Buying EOQ Overall Cost Minimization given Constraints EOQ Minimizes Total Costs Total Cost Holding Costs Ordering Cost Safety Stock Reasons to Carry Safety Stock Unforeseen Supply Variation Customer Satisfaction Inaccurate demand Estimation Initial Safety Stock Max Daly Usage x Max Lead Timeavg daily usage avg lead time Safety Stock Simulation to arrive at optimal level that maintains high service levels while minimizing cost Reorder Point Safety Stock Average Daily Sale or Forecast Average Lead Time ROP triggers the purchase of a predetermined amount of replenishment inventory Reorder point varies across product groups due to different usage rates lead time Reorder Point Benefits of having Inventory Policies Smart Buying Demand Cost Cost Demand Switching Rules Smart Buying Business Goals Metrics to trigger Switching Approach Switching Rules Outcome Switch between suppliers providing different MOQ based on demand Anticipated Inventory Built Up check Rolling 3 months inventory vs previous R3M Unexpected Demand Check Demand forecast for the upcoming 4th week vs. actual usage for the previous year Demand Volatility Check CV of next month forecasted sales vs CV of P1M historical sales Unexpected Demand Check Average demand forecast for the next 4 weeks vs average rolling forecast for the entire year Switch to Just in Time Policy for perishable products with sudden drop in forecasted demand as compared to Previous year Switch to Supplier providing lowerhigher MOQ in case of IncreasingDecreasing Inventory built up Switch from Continuous to Fixed Order Policy with higher femand fluctuation Switch to LowerHigher Safety Stock with next 4 weeks Demand forecast continuously lessergreater than average yearly forecast S witching rules reduce average inventory overall cost stockout events Switch between Inventory Policies Safety Stock values given a supplier Inventory Policies Smart Buying Need Decide on the two major variables in an inventory control system order quantity ordering frequency Analyze reorder pts order levels demand forecast to determine which inventory system to follow for each product group Approach Benefits Business is to decide what when how much to order and to follow an inventory strategy on how to manage inventory cost Determine control decisions to maximize service level customer satisfaction while minimizing cost Proper inventory control policies reduces the cost associated with the inventory carrying costs ordering costs shortage costs When we decide on the inventory control policies we are striking a balance between cost and service Objective To keep stock of a product and replenish the stock as and when necessary Policies Supplier Selection Smart Buying How to select supplier groups Supplier Selection Smart Buying U nderstanding supplier strengths and differences will improve our sourcing process and helps in choosing the best one based on cost schedule and product needs Quality standards will be based on the research of individual suppliers How to select supplier groups Switching rules Smart Buying W hen to switch to a lower or higher MOQ also the optimal values of the MOQs that achieve the target service level and are the lowest cost Challenges I f the demand forecast is very high we want to order a higher MOQ whereas if the demand forecast is very low then we would switch to a lower MOQ High Medium Low demand If the weeks forecast is less than the average the model will use the smaller MOQ and if it is greater than the average it will switch to the higher MOQ Approach Benefits To choose optimal one from different values of the MOQs Since the demand for the product is highly volatile having one large MOQ throughout can lead to overstocking and holding too much inventory at certain times of the year R educe the average inventory held and reduce the overall cost A good balance between the costs and the service level is obtained Inventory Policies Smart Buying What inventory models could be followed for different product groups by analyzing reorder points review periods orderuptolevels Demand Inputs Constraints Inventory Cost Order quantity Order frequency Predicted safety stock Service level Review period Purchase orders Lead time Fixed Order Quantity How Only specific quantityEOQ of item can be ordered WhoWhen Costs are stable Benefits Efficient order size safety stock during lead time period minimum storage space and discounts good for fast moving and highcost products automatic ROPs Fixed Period Ordering How Product ordered at certain time WhoWhen Demand fluctuates Benefits Order quantity is different to compensate for demand orders can be combined Just in Time Ordering How Item o rdered only when needed WhoWhen Demand and costs are stable Benefits Reduced inventory costs Demand Cost MOQ Proper inventory control policies and procedures reduces the costs associated with the inventory When we decide on the values of inventory control policies we are striking a balance between cost and service Benefits of inventory control policies Activation of Inventory Policies Smart Buying Relevant inventory models could be followed by analyzing product specific eorder points review periods orderuptolevels Policies Cause Decide on the two major variables in an inventory control system order quantity ordering frequency Effect Analyze reorder pts order levels demand forecast to determine which inventory system to follow for each product group Leveraging AIML to Forecast Demand Smart Buying Demand patterns Seasonality trends demand variability Business decisions Changes in promotions price storage External factors Weather holidays inflation congestion AIML Accurate forecasts for retail planning Forecast per product months day and store or fulfillment channel Forecast horizon from 1218 months Data Requirements Inventory Transactional data POS for a minimum of 3 years Master Product data Product Geo Channel with price External Data Macroeconomic Weather indicators Feature Engineering Univariate b ivariate analysis d erived variables of last x months quantity Check for seasonality demand variability Classify products by demand into lumpy smooth intermittent erratic basis CV and ADI Forecasting model Aggregate Data f or each productcategory Different Product Level StatisticalMLensemble models for each demand type Smooth Lumpy Erratic Intermittent Model at hierarchy level if product level model is not accurate Demand Sensing Devise an algorithm between the past and the future C ompare forecast with actual demand to calculate the bias and error E xecute shortterm forecast adjustments based on sensed demand patterns Model Output The Objective is to generate demand forecast to provide as a constraint for the optimization layer Generate long term forecast for 12 18 months quantity for each product or category 1 2 3 4 5 S afety Stock Objective Minimize costs holding ordering Generating EOQ for each time period given constraints on Demand Inventory Storage Calculated initial safety stock based on daily usage and lead time Safety Stock simulation to arrive at optimal level that balance between service levels and minimizes cost Prescriptive Recommendations EOQ Optimal safety stock Initial Safety Stock Max Daly Usage x Max Lead Timeavg daily usage avg lead time Lower initial value by 25 each time to reach the optimal value that minimizes total cost while hitting the service level target Inclusion of safety stock as constraint improves order cycle service levels EOQ Determined by inventory related costs such as ordering costs carrying costs stockout costs Standard EOQ is not applicable Volatile Demand Stock Outs Varying ordering cost unit Discounts Offered To provide Prescriptive recommendations on sourcing controls and optimal inventory across various product categories Smart Buying Reorder Points One of the main inventory sourcing control method lies in reorder points Given safety stock the exact time to reorder can be calculated Reasons to Carry Safety Stock Unforeseen Supply Variation Customer Satisfaction Inaccurate Demand Estimation ROP ROP triggers the purchase of a predetermined amount of replenishment inventory Reorder point varies across product groups due to different usage rates lead time Reorder Point Safety Stock Average Daily Sale or Forecast Average Lead Time Product Segmentation Products exhibit various patterns in terms of Order quantity Volatility of order quantity Demand Consumption rate Replenishment count Reorder frequency Gross Margin Discounts Product lifecycle Classification of product based on different supply and demand indicators required to frame optimal ordering policy for each selected product group Data Preprocessing Univariate analysis outlier treatment missing value treatment transformations derived features last X months ordered quantity forecast for next 10 weeks scaling for the model K Means Hierarchical clustering for SKU segmentation and select the products to analyze based on business inputs FSN XYZ Median based analysis to group and select the products to analyze based on business inputs Product segments and selection based on clustering analysis Validate output with business on the products segmentation Explore results and iterate at different time frames Quarterly Half yearly Yearly Smart Buying Inventory Policies Smart Buying Inventory policies and control decisions are concerned with maximizing the service level customer satisfaction and minimizing cost Components carrying costs ordering costs shortage costs One is to decide when to order and how much to order and then to create a systematic tracking of inventory Inventory policy is affected by demand lead time number of products service level minimizing costs Benefits proper inventory control policies and procedures reduces the cost associated with the inventory Parameters which inventory items are important How much to order When to order Cost associated with managing inventories which inventory strategytype to likely pursue Two major variables in an inventory control system order quantity ordering frequency Inventory control systems 1 . cont holds the order size constant and lets the frequency of ordering fluctuate according to demand the stock position is monitored continuously when the stock position drops to the reorder point a fixed quantity q is ordered Excellent for high cost items needed close attention Adv efficient order size safety stock needed for the lead time period more attention for fast moving items 2 . periodicholds the frequency of ordering constant and lets the order size fluctuate according to demand stock position is reviewed at regular intervals when thr is one supplier and items are expensive it doesnot have an eoq since the quantity varies according to demand monitors inventory at regular basis as weekly monthly and yearly depends need alrger safety stock than continuous review systems When you decide on the values of inventory control policies you are striking a balance between cost and service The primary concept of inventory revolves around keeping stock of a product and replenishing the stock as necessary. There are three main principles of inventory management reorder points order up to levels review periods Modify for across prod grps Solution Approach Prescriptive model to recommend When to buy at what rate to improve margins Steps we can take to build this model We require input parameters of Realtime cost demand We need to integrate supplier data and market price and Demand Supply demand from customers production demand For Low DemandHighCost items we can introduce JIT concept if feasible For High DemandLowCost items we can have advance buffer stock . When to Buy at What rate For High DemandHigh Cost we need to have a model where we need decision When to Buy at What rate We can take average of historical forecasted cost demand along with market price to identify idle cost and current demand . Buying decision is utmost important factor in strategic supply chain practice . Hence we need a model which can tell us when to buy which material at what cost . Due to the diversity is material type we need to adopt multiple approach to execute this model Segmentation Price Elasticity Selection Placement Recommendations Smart Buying Goal of this slide is to show the recommendations on what inventory models could be followed for different product groups Show various policies here like continuous periodic hybrid Modify this slide to show various recommendations that were suggested in various product groups the content of the slide is good just rearranging and showing some visual graphs could help Graphs could be nice to explain here Take help from below slides too Dependent demand MOQ Production fill rate Compliance to schedule PurchaseProduction Orders Predicted Target days of supply Predicted Safety Stock Inputs Constraints Evaluating safety stock by simulating inventory policies Tool simulates inventory consumption over 13 weeks considering policies s S Inventory Policy S Max Stock Rolling average N weeks demand forecast Predicted safety stock s Safety stock Predicted safety stock qty Replenishment qty S Ending inventory in multiples of MOQ r Q Inventory Policy Q Order Qty MOQ or in multiples of MOQ r replenishment point Predicted target days of supply Replenishment qty r Q Ending inventory 1 2 Output Revised Safety Stock Quantity Revised Target Days of Supply Beginning Ending inventory Days of Supply Target Stock Simulation Optimization Remove this slide later Multiple reasons why we hold inventory for different reasons HORIZON OPPORTUNITY Remove this slide later Switching rules Determine Optimal Safety Stock Optimal Quantity Switch to Lower Safety Stock with Increasing Inventory Built up Switch to Higher Safety Stock with Falling Shipment Switch to Lower Safety Stock with Demand forecast continuously exceeding Actuals Objective Business Need With volatile demand one large OQSS can lead to overstocking. Input Range of Safety Stock MOQ values Cost Stockout events Optimal Quantity values from different weights in Optimization Layer Approach Switch to lower or higher values of OQMOQ depending on demandinventory rules. Statistical methods to compare metrics used in rules. Impact S witching rules reduce average inventory overall cost stockout events Switch to a Different Optimal Quantity solution as soon as one criteria is met Smart Buying Data Requirements Modify this slide to multiple slides to show data sources needed by different entities like supplier product demand price etc Use below slides as an example Solution Approach . 1Find the optimal safety stock value using the safety stock calculated and then lower the value by 25 each time to reach the optimal value that minimizes total cost while hitting the service level target. 2 Once the safety stock is set iterate the model with different OQ values to reach the optimal value that minimizes cost and obtains the target service level Objective minimize holding carrying cost  Subject to target safety stock levels target service levels The ordering policy states that if the inventory position in 4 weeks with the current inventory level is smaller than the safety stock then we place an order for the gross requirement which also covers the safety stock as a multiple of the MOQ. Segmentation Price Elasticity Selection Placement Recommendations Smart Buying Modify this slide to show various recommendations that could follow on the inventory controls as highlighted in point 1 and 2and what rules to apply. Graphs could be nice to explain here Goal of this slide is to show the recommendations on SS ROP EOQ etc that could come out of the exercise. Modify this slide to show various recommendations that could follow on the inventory controls as highlighted in point 1 and 2and what rules to apply. Graphs could be nice to explain here take content from below slides too. Just one slide should be enough EOQ assumptions Demand is known constant no safety stock is required Lead time is known constant No quantity discounts are available Ordering or setup costs are constant All demand is satisfied no shortages The order quantity arrives in a single shipment Remove this slide later EOQ Total annual cost annual ordering cost annual holding costs Remove this slide later Quantity Discount Model Same as the EOQ model except Unit price depends upon the quantity ordered The total cost equation becomes Remove this slide later Prescriptive Recommendations . 1Find the optimal safety stock value using the safety stock calculated and then lower the value by 25 each time to reach the optimal value that minimizes total cost while hitting the service level target. 2 Once the safety stock is set iterate the model with different OQ values to reach the optimal value that minimizes cost and obtains the target service level Objective minimize holding carrying cost  Subject to target safety stock levels target service levels The ordering policy states that if the inventory position in 4 weeks with the current inventory level is smaller than the safety stock then we place an order for the gross requirement which also covers the safety stock as a multiple of the MOQ. Segmentation Price Elasticity Selection Placement Recommendations Modify this slide to show various recommendations that could follow on the inventory controls as highlighted in point 1 and 2and what rules to apply. Graphs could be nice to explain here Goal of this slide is to show the recommendations on SS ROP EOQ etc that could come out of the exercise. Modify this slide to show various recommendations that could follow on the inventory controls as highlighted in point 1 and 2and what rules to apply. Graphs could be nice to explain here take content from below slides too. Just one slide should be enough Data Extraction D emand Forecast Model steps Model Validation Forecast Output Master Data Product and GeoChannel Transactional Data historical Sales Inventory Position Month level transactional data for all products Minimum availability of 3 years Aggregate data for each product segment Store 1. Product level model Statistical models Time series regression ML models for smooth and erratic demand 1.a Croston method for lumpyintermittent demand Classification to predict whether demand occurs or not ensembleneural networks regression methods to predict demand Qualified for model 2. Higher product level hierarchy modelcategory level is run and assigned to the respective product group Merging models for 1 2 for all the products Distribute the data for training and testing and Validate it Fine tuning the model Ensemble approach to use the best fit model Generate long term Forecast output for 12 18 months sale quantity for each scope product category. No Yes Data cleaning outlier detection and preparing data in the required format Feature Engineering for Demand Sensing Data exploratory Analysis Using Market data adding features such as listed price external inventory Use external data holidays macroeconomic weather indicators Missing data Outlier treatment Trend analysis correlation analysis Data clean up Data Extraction Exploratory Analysis Feature engineering Model Management Model Validation Demand Sensing and Output Demand Forecasting Sensing Check for seasonality cyclicity stationarity demand variability Classify demand into Lumpy Smooth intermittent erratic basis CV and ADI Incorporate Demand Sensing Apply bias to recent predictions. Devise an algorithm between the past and the future .Apply the same and compare with real output. E xecute shortterm forecast adjustments based on sensed demand pattern changes Smart Buying Segmentation Demand Price Selection Placement Recommendations Update this slide to show the demand forecasting and sensing in detail Use the below slide as an inspiration Optimal buying Patterns Ask To optimize the Ordering quantity for the retailers by determining optimal buying patterns in a way that reduces total cost holding carrying costs and at the same time meets demand keeping sufficient inventory avoiding stockouts Need Forecast the Demand to achieve the required quantity To determine how much quantity to buy from which Supplier by identifying potential suppliers and choosing supplier. T he buyer selects the right supplier and orders an appropriate quantity. There is also a need to determine the optimal time to buy from suppliers by taking in account of the constraints such as Inventory on hand Inventory positions demand lead time Demand Sensing What kind of Products to consider Determine the products to be included in the analysis exFSN analysis consumption rate quantity average stay reorder frequency demand etc Filter slow moving products FSN analysis and XYZ analysis Price Model How much quantity should I buy Consider demand variabilityXYZ analysis seasonality stationarity product lifecycle etc Multivariate analysis like Decision Tree Random Forest Based on the historical data forecast the demand of the products for next 1218 months Demand Sensing Add data of current events to make forecasts robust for short term dynamic inventory planning. Improved short term forecast accuracy and demand responsive supply chains Optimization At what price should I sell Price depends on season time historical demand elasticity purchase frequency Predict the minimum and maximum price of a product to be sold for at a given time Using quantile regression model considering quantile 50 for Price min and 95 for Price max predict the price based on the historical data Choose Cluster Products How much to buy From whom to buy When to buy Using multi objective optimization algorithms to determine the optimal quantity to buy from whom to buy and when to buy to obtain optimal buying patterns Multi Objective optimization will incorporate weighted function of all different evaluation criteria including Cost Profit Priority Lead time Perishability Constraints Forecasted Demand Min and Max Price Inventory Levels Solution Construct Endtoend 360 view A twolevel supply chain is considered consists of one retailer and a collection of suppliers that operate within a finite planning horizon including multiple periods and a model is formulated that simultaneously determines both supplier selection and inventory allocation problems in the supply chain Doubt Objective How to optimize the ordering policy in a way that reduces total cost holding carrying costs and at the same time meets demand keeping sufficient inventory avoiding stockouts Objective Arrive at optimum eoq optimum parameters EOQ strategy minimizes the total cost of ordering and carrying cycle stocks. follows basic philosophy of when and how much to order. EOQ attempts to determine the amount of inventory to purchase with each order to minimize total inventory cost Use to determine reorder levelsafety stockreserve stock Basic EOQ Assumptions i Demand is known constant and independent ii Demand during the lead time is the same as the normal demand iii Lead time is known and constant iv Receipt of inventory is instantaneous and complete v Purchase price of the item is constant. vi Quantity discounts are not applied to the model vii Stock outs completely avoidable viii The Process continues infinitely ix There are no quantity constraints on order quantity or storage capacity x Only variable costs are Setup and holding Challenges and Opportunities Why we dont go for Optimal order quantities They may not have a known uniform demand Some suppliers have minimum order quantity that are beyond the demand. How to optimize the ordering policy in a way that reduces total cost holding carrying costs and at the same time meets demand keeping sufficient inventory to avoid stockouts A standard EOQ model may not applicable to our project because the real demand and product does not satisfy the assumptions  Challenges demand is not constant with a lot of volatility seasonal changes unexpected spikes 2 lead time is constant and known 3 stockouts can occur but we want to avoid it 4 items are ordered in lots and 5 unit item cost is not constant and incremental discounts are offered. Instead we order in incremental multiples of the MOQ.  The best MOQ value will be determined as a result of the simulation by doing a sensitivity analysis which will give us the minimum total cost and no stockouts.   simulated the demand for different values of safety stock to reach the optimal value. Requirements and Impact If demand or lead time is uncertain safety stock can be added to improve ordercycle service levels There is a requirement to maintain balance between overstocking and understocking. The optimal order quantity will maintain a balance between the ordering cost and the holding cost. The model will provide the optimal MOQ to use while reordering. It will also incorporate a switching rule that automatically switches the MOQ to a higher or lower value depending on the demand forecast and determines the order quantity OQ If we identify seasonality in the demand then we can use the switching rule to switch between peak season and nonpeak season to reduce the holding cost Questions When to buy How much to buy From whom to buy How many times I should place an orderHow often Replenishment cycle find optimal rep cycle answers how much to order and when to order    Challenges Approach Impact Approach . Challenges . Impact Decreases Inventory costs Minimizes outofstock Improves Profit Margin Savings on bulk purchases . Products to consider . Step 1 Products Examples Cotton Balls Boxes Plastic cups packaging items etc. Demand forecasting Model Architecture Step by step approach for demand forecasting model . Step 2 Forecasting Data Extraction Showroom market segment modelling steps Model Validation Forecast Output Master Data Product and GeoChannel Transactional Data historical Sales Inventory Position Month level transactional data for all products Aggregate data for each product segment Store 1. product  level model Time series regression ML models Qualified for model 2. Higher product hierarchy level model is run and assigned to the respective product group or category Merging models for 1 2 for all the products Product and retailmarket level model files are saved for output Distribute the data for training and testing and Validate it Fine tunning the model Ensemble approach to use the best fit model Generate Forecast output for future weeks sale quantity for each scope product category. No Yes Data cleaning outlier detection and preparing data in the required format Feature Engineering Data exploratory Analysis Using Market data adding features such as listed price external inventory demographic Missing data Outlier treatment Trend analysis correlation analysis Data clean up Data Extraction Exploratory Analysis Feature engineering Model Management Model Validation and Output Check for seasonality cyclicity and stationarity 12 Month Future forecast Input data should have minimum 3 years of monthly data Can consider external data too like demographics holidays Determining Order quantit

Lotforlot Order exactly what is needed Fixedorder quantity Specifies the number of units to order whenever an order is placed Minmax system Places a replenishment order when the onhand inventory falls below the predetermined minimum level. Order n periods Order quantity is determined by total demand for the item for the next n periods Model for determining Order quantity Lotforlot fixedorder quantity minmax systems order n periods periodic review systems EOQ models quantity discount models and singleperiod models can be used to determine order quantities. Ordering decisions can be improved by analyzing total costs of an inventory policy. Total costs include ordering cost holding cost and material cost. Economic Order Quantity EOQ The optimal volume and frequency of orders required to satisfy a given level of demand while minimizing the cost per order how much to order and when to order how often An EOQ strategy minimizes the total cost of ordering and carrying cycle stocks. how much product needs to be purchased while keeping costs down. EOQ    2 D S H ddemand sordering cost hholding cost An optimizing method used for determining order quantity and reorder points Part of continuous review system which tracks onhand inventory each time a withdrawal is made Quantity Discount Model Modifies the EOQ process to consider cases where quantity discounts are available A bad side of EOQ is that it assumes that the demand will be the same over time. Also the calculation assumes that ordering prices and costs remain constant.  Quantity Discount Model Same as the EOQ model except Unit price depends upon the quantity ordered The total cost equation becomes Data Required Product name Stockkeeping unit SKU Brand Variables such as size retail price product category lot number location and expiration date. Vendor and vendor SKU Wholesale cost Minimum reorder amount Economic order quantity EOQ Case quantity amount Inventory on hand Reorder lead time Data Required Unit cost holdingcarrying cost kept constant Carrying costs include housing handling insurance shrinkage record keeping cost of invested capital and related operational costs. Ordering cost depending on material ordered item cost qty ordered Ordering inventory costs money in terms of purchasing shipping handling receiving accounting and related operational expense. Demand Pattern from DD forecast Purchase PriceMOQ price slab Unit price offered by the suppliers for the incremental quantities Historical inventory trend HIT data Data table of different inventory levels of the product such as available inventory production usage safety stock and the dollar values for each of the stock keeping units SKUs Lead time for replenishment and target service levels Demand Forecasting demand depends on time and the unit selling price Optimal Buying Patterns retailperishable max availabilitydiscounts min wastagespend keep in mind discounts opinv mgmtsafetystockwhentoorder retailfinished goods fast moving noneed slow movingdo focusbulk purchasesdiscounts Obj functionservice level opt cost max price sales lead time moq discounts What shd be our main obj func Constraintsdiscount lifecycle time space Distinguish categorize between fast moving slow moving and filter on slow Step 2 Demand Demand Forecasting How many times shd I place an order For whom I shd place The quantity to be placed Replenishment cycle find optimal rep cycle product lifecycle Ip to eoq     Identify products Assumptions eoq order all at once demand during lt Demand forecast method Identifying the optimal buying patterns cont review policy fixed order policy   Obj function service level opt cost max price sales lead time moq discounts What shd be our main obj func Constraintsdiscount lifecycle time space moq Average daily unit sales  x  average lead time in days  safety stock in units   reorder point in units Step 2 Demand Solution Construct Price Elasticity Price depends on season time discounts supplier packaging needs quantity quality etc. Using regression Bayesian model predict the price based on the historical data Predict the minimum and maximum price to be sold for a given time of a product Consider seasonality competition demand pattern festivals etc. Demand Forecasting Based on the historical data forecast the demand of the product for next 1218 months Consider seasonality cyclicity stationarity etc. Multivariate analysis like Decision Tree Random Forecast etc. Optimal Buying Recommendation Using optimization algorithm identify when to buy and in what quantity and from whom to buy so that to minimize cost maximize revenue maximize the availability and minimize perishability Constraints Freshness supplier delays discounts MOQ lead time demand Price etc.. Overall Solution Construct Type of Suppliers Todo Overall solution construct 1 slide price model 1 slide Optimization high level approach 1 slide chains of optimizers N Each optimizer in the chain 1 slide eachN slides Order Batching Optimization batch the orders objconstraintsdata Scheduling Optimization objconstraintsdata Min Cost Optimizationmin cost max rev objconstraintsdata Demo pbi When to buy How much to buy From whom to buy How many times I should place an orderHow often Whenscheduling Price based Optimizer AssignmentOrder Qtysupplier mapping SchedulingSupplier Monthwise Questions Assignment objconstraintsdata how much qty we will be getting from different suppliers Scheduling Optimization objconstraintsmoqdata When to buy From whom to buy Min Cost Optimization min cost max rev objconstraintsdata How much to buy demand forecasting How many times I should place an orderHow often Solution Construct Optimization Scheduling Optimizer To know when to buy Assignment Optimizer Allocate orders to achieve how much quantity to buy from which Supplier by identifying potential suppliers and choosing supplier. Here the buyer selects the right supplier and order an appropriate quantity Consider the buyers viewpoint and maximize only the buyers profit Single source Ranking Multi source Single objective allocation problem Factors money quality reliability and service Objective Minimize the total costpurchasing cost the ordering cost the transportation costs Constraints Moq price discounts lead time Horizon 1218months Min Cost Optimization . . wip Overall Solution Construct Flow Segmentation Extract insights from different Product Groups Extract 3yrs data Check for seasonality demand variability Arrive at of SKUs Categories that qualify for Demand Forecasting Add Demand Sensing Layer Generate Forecast Use Quantile Regression for Min and Max Price Range prediction Establish a Causal Network such that Price fTime Demand Seasonality Generate Optimal ordering Quantity to buy from Optimal Supplier at a given period at optimal price range Use Multi Objective optimization Assign weights to each obj Function Multi Integer Linear Programing The model is reiterated with different weights till minimum threshold reached Create Optimization Model Optimization layer Decide on Constraints Inventory Supplier Price Min Demand Time Period Decide on Objective Functions Cost Profit Quality Lead time Reputation Business Goals Basic Analysis Advanced Analytics Data Science Demand Sensing and Price Elasticity Model layer Recommendations on optimal inventory sourcing buying patterns and switching rules based on MOQ Smart Buying EDA Feature Engineering Univariate Bivariate analysis Outlier missing Value correlation variance etc Derive variables of last x months quantity Check for seasonality cyclicity stationarity demand variability Classify products by demand into l umpy smooth intermittent erratic basis CV and ADI Demand Sensing Devise an algorithm between the past and the future C ompare forecast with actual demand to calculate the bias and error E xecute shortterm forecast adjustments based on sensed demand patterns Model Output The Objective is to generate demand forecast to provide as a constraint for the optimization layer Generate long term forecast for 12 18 months quantity for each product or category Leveraging AIML to Forecast Demand Data Requirements Inventory Transactional data POS for a minimum of 3 years Master Product data Product Geo Channel External Data Macroeconomic Weather indicators Market Data Listed Price External Inventory Model Forecast Management Aggregate Data f or each productcategory Different Product Level StatisticalMLensemble models for each demand type Smooth Lumpy Erratic Intermittent Model at hierarchycategory level if product level model is not accurate Smart Buying 1 2 3 4 5 Product Segmentation Outcomes Smart Buying Examples Cotton Balls Boxes Plastic cups packaging items etc. The type of Product Need to be considered is.. Product Overview Show this slides as various product groups thatc ame out of segemnation exercise . Add 3 dimensions demand typevolume and speedfastslow moving price perishability PRODUCT GROUPS Product Overview Supplier groups Show this slides as various supplier groups that could be in the selection phase Show icons for each type of supplier and the challenges Prescriptive Recommendations Smart Buying EOQ Overall Cost Minimization given Constraints EOQ Minimizes Total Costs Total Cost Holding Costs Ordering Cost Safety Stock and ROP Reasons to Carry Safety Stock Unforeseen Supply Variation Customer Satisfaction Inaccurate demand Estimation Initial Safety Stock Max Daly Usage x Max Lead Timeavg daily usage avg lead time Safety Stock Simulation to arrive at optimal level that maintains high service levels while minimizing cost ROP Determined using SS Inventory Policies EOQ based Continuous Review On time inventory control High Cost Techlabor requirement to monitor inventory Periodic Review No Ontime inventory control Low Cost S election of inventory policy is associated with inventory cost Inv Policy Rules Reorder Points Objective Minimize costs holding ordering Generating EOQ for each time period given constraints on Demand Inventory Storage Calculated initial safety stock based on daily usage and lead time Safety Stock simulation to arrive at optimal level that balance between service levels and minimizing cost Different inventory policy recommendation across product groups Inventory policy is affected by demand lead time service level costs One of the main inventory sourcing control method lies in reorder points Given safety stock the exact time to reorder can be calculated Prescriptive Recommendations S afety Stock EOQ Optimal safety stock Lower initial value by 25 each time to reach the optimal value that minimizes total cost while hitting the service level target Inclusion of safety stock as constraint improves order cycle service levels EOQ Model Determined by Inventory related costs such as ordering costs carrying costs stockout costs Assumptions Uniform and known demand Fixed item cost Fixed ordering cost Constant lead time To provide Prescriptive recommendations on sourcing controls and optimal inventory across various product categories Smart Buying Inventory Policy rules Continuous review policy Constant order quantity Periodic review policy Constant order frequency First in first Out Highly Perishable JIT low demand Bulk discounts Product Segmentation Outcomes Smart Buying Slow moving Bulk purchase Low demand Perishables Fast moving Identify which product groups to analyze based on lifecycle demand consumption rate price discounts Optimal Buying through Optimization algorithms Quality Checks Network disruptions External Factors Inputs Multi Objective Optimization engine to incorporate weighted function for all the objectives and constraints Evaluation Criteria to include Cost Profit margin Perishability Lead Time Delivery delay Supplier Reputation Additional business heuristics to make account for inventory levels constraints and other external factors Constraints Port Congestions Buying Patterns Optimization Product Availability Price Inventory Position Inventory Demand Min Demand Supplier Activation Storage Price Time Window Quality Cost Supplier Reputation Climatic changes Macroeconomic Indicators Smart Buying Multi Objective Optimization Mathematical programming model considering the objectives constraints and business requirements Obj 1 Max Margin Min Cost Obj 2 Maximize Quality Obj 3 Minimize Lead Time Delay Obj 4 Maximize Supplier Reputation Input Parameters Demand Inventory Supplier Information Cost Prices Time Window Constraints Inventory on hand Safety stock MOQ Supplier activation Product choice activation Minimum d emand M axMin price range Storage capacity Inputs Constraints Generate various sets of weights using an optimal frontier. Weights ranges to be permuted on relative importance and criticality of achievement of each objective. Final Weights to be optimized using Rank Order Centroid Ratio Pairwise comparison satisfying business objectives. Mixed Integer Linear Programming Maximize Minimize W 1 Obj 1 W 2 Obj 2 ........ W 4 Obj 4 weighted sum of the given objectives Multiobjective allocation and scheduling optimization Smart Buying Smart Buying Story Flow Optimization Min Demand MOQSafety Stock Storage Capacity External factors Week 4 Week 15 Min Price Max Price Objective Functions as per priority Doubt Min Price 2 Max Price 7 Purchase Order Placement SKU Demand Default Vs Clustered DCs Product Distribution Default Distribution Default Each DC acts as individual entity Clustered DCs Analysis Across DCs Analysis Across SKUs SKU Demand against time Distribution center DC1 Distribution center DC1 SKU SKU 1 13 Cost Optimized 13 25 25 time Optimized 20 Container efficiency Optimized 20 Delivery Time Week 4 Week 15 DC 2 DC 3 DC 1 DC 2 DC 3 DC 1 Purchase Order Placement Demand Across DCs Default Vs Clustered DCs Product Distribution Default Distribution Default Each DC acts as individual entity Clustered DCs Analysis Across DCs Analysis Across SKUs SKU Demand against time Distribution center SKU 1 13 Cost Optimized 13 25 25 time Optimized 20 Container efficiency Optimized 20 Delivery Time SKU1 SKU 2 SKU 3 Distribution center DC1 SKU SKU 1 SKU1 SKU 2 SKU 3 Week 4 Week 15 RISK ASSESSMENT RECOMMENDATIONS SUPPLY NETWORK SCENARIO PLANNING TRACK AND MONITOR SCENARIO PLANNING Total Landing Cost 35464 PO at Risk 23 PO at Risk 345 Updated Landing Cost 28000 Updated PO at Risk 16 Updated PO at Risk 201 CURRENT LANDING COST UPDATED LANDING COST Source Rail Destination Source Roadways Destination Procurement Cost 13204 Transportation Cost 135 Other Misc. Cost 145 Procurement Cost 13204 Transportation Cost 780 Other Misc. Cost 168 Cost 620 Time 6 days SCENARIO PLANNING RECOMMENDATIONS RECOMMENDATIONS TRACK AND MONITOR RECOMMENDED MODE OF TRANSPORT RISK ASSESSMENT LANDED COST BENEFITS IMPACT ON PLANTS NETWORK PLANNING RECOMMENDATIONS 55 99 25 5 A CTUALS SCENARIO PLANNING RECOMMENDATIONS RECOMMENDATIONS Total Product s at Risk 100 Products for which warning issued 6 0 Products for which Alarm issued 25 Critical Products 15 GENERIC PRODUCTS RECOMMENDATIONS AND IMPACT Date All Product Type All supplier All DIGITAL RECORDS LANDED COST BENEFITS FOR PROPRITORY PRODUCTS Warn Alarm Emergency Safe FORECAST OVERVIEW SCENARIO PLANNING RECOMMENDATIONS RECOMMENDATIONS TRACK AND MONITOR OPTIMIZED SPEND AND RISKS RISK ASSESSMENT NEGOTIATION IMPACT ON SPEND Warn Alarm Emergency Safe Optimized spend 116M Predicted risk 22 Average risk 51 Optimized discount 8.8 RECOMMENDED SUPPLIER CHANGES REDUCED OVERALL RISK POST SUPPLIER CHANGE RECOMMENDATIONS Predicted delivery risk 2 Supplier performance 95 Cluster 2 Lead time variability 8 RISK OVERVIEW RISK ASSESSMENT SCENARIO PLANNING RECOMMENDATION IMPACT Supplier Fill Rate Delivery Risk SCENARIO PLANNING TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING RECOMMENDATIONS RECOMMENDED APPOINTMENT DATE RECOMMENDED CARRIER RECOMMENDATIONS TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING RECOMMENDATIONS

P&G_Spare_parts_forecasting V1.pptx

Spare Parts Smart Planning Fractal Analytics February 2023 01 02 03 04 05 06 07 08 Reason to Believe Case Study Fractal.ai Fractal PG Our Expertise Our Understanding of the Need Our Solution Approach Implementation Methodology Commercials Engagement Model Content 01 Case study Automated e2e forecasting inventory optimization for spare parts Client ask Result Forecast the consumption of Spare parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution 2Mn savings across 30 of Spare parts accessories globally Huge potential savings with scale Efficient utilization of spare parts with AIML based forecasting inventory optimization resulted in 2Mn Cost savings Solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process AIML driven methodology eventually leading to a fully automated procurement system 1 2 3 4 Differential evolution optimization Performing various statistical analysis and hypothesis Differential evolution optimization for SKU Positioning into quadrants to understand Past behaviors and global behaviors Decide modelling technique for Forecasting demand DL CNN LSTM ML Random forest Statistical A SRIMAX GARCH Replenishment parameters at SKU level Min Max ROP safety stock EOQ Dynamic recommendations on minmax inventory norms Estimating and highlighting emerging trends and disconnects. Simulation of inventory norms w.r.t service levels supply demand parameters 02 Fractal.ai Our aspiration Powering every human decision in the enterprise Founded in 2000 60 Fortune 500 clients 22 00 Employees 100 Countries 17 Global offices Kuala Lumpur Sweden Growth by focusing on clients people innovation Global Recognitions in overall strategy 1 Industry leader in delivering Customer Analytics in client engagement model in future viabilityinvesting in future to stay relevant in people practicesnurturing and retaining talent 2 in current offering and market presence Customer Consumer in Forrester Parlance Research Year 2019 03 Fractal and PG The Fractal you know ACHIEVEMENTS AND ACCOLADES AT A GLANCE PG TEAMS THAT WORK WITH US DA CFTI Strategic Revenue Management SKU Portfolio Optimization STC Analytics global solutions Analytics and Insights AI Embedded teams with all major SBUs regions and customer teams for deep dive analysis on key business questions Product Supply TOTaL application iPlanning Europe Command Centre Lane Management AMA Control Tower SPO BI DevOps Create data marts develop and support backend data architecture DA Dev Ops CFTI SPO BI SMO IT AMA SOI PSKU Korea Data Platform Pollux Database etc.  CMK Connected Room Solutions Services KBD Drainers Drivers Ecommerce Digital analytics Central IBO team Other PG teams   Next Generation Services NGS RD MyPG Services Continued push for better and more useful analytics 60 of the PG portfolio is investigative and predictive analytics in addition to a dozen cloud implementations at PG. Delivering 25 efficiency and productivity Through scaleup of services automation and process standardization including 10 hard savings annually. Engagements 350 Fractal practitioners committed 99.5 OND 21 Net Promoter Score Quality of Service 110 70 2 280 60 250 200 2020 E2E Problem Solving Application development DS Advanced Analytics Continually evolving client conversations work profile team 350 2021 Data Science partnership New MSA renewal for next 5 years Scale with people Embedded consultants to region business units Cross Functional Skillset across AI Engineering and Design Regular Consulting training Focus on relevance value Processes Data partnerships TPA Data security setup Performance measurement Process Governance Technology Data Engineering to give you scale Integration with PG set up Alignment on business consumption Sophisticated analytics using AIML Partnership Engaged across business units of AI CMK GBS PS Current focus on driving synergies with rest of the organization Key elements of our successful partnership 04 Our Expertise Improving spare part tracking visibility through data driven decision making AI modeling to strike an optimal balance. Ensuring effective inventory levels reduced risk optimized budget spending and fewer unplanned downtimes. 01 We understand spare parts ecosystem across CPG manufacturing Provided reliable resilient and scalable supply chain solution to multiple CPG and manufacturing clients which has transformed their business operation to grow both vertically and horizontally 02 We have extensively worked on AIML across technology Integrating technology design and AI for large scale analytics deployment and drive adoption enabled by functional SMEs has promoted Fractal as one of strategic channel partner across industries 03 Extensive experience in delivery rapid prototyping through agile process Fractal has followed Agile delivery approach for multiple client engagements and has delivered effectively 04 Supply Chain Thought leadership extensive domain expertise Thorough understanding of PG Supply processes through successfully delivering engagements like CSL CPM CPFR DnD Data engine work Fractal is uniquely positioned to deliver this engagement Unwanted contents filtered out.Takshila kindly review 05 Our Understanding of the Need Background PG is a leading consumer good manufacturer with Specialty plants in numerous locations. PG is looking forward to a strategic partner who can help them with an AIML based predictive models which can improve decisions over the spare part replenishment strategy Current GapsPain Points Several slowmoving MRO parts are rarely utilized or consumed but the organization must hold in stock to meet contractual obligations business continuity or capture commercial opportunities Due to lack of visibility of the future demand often these slowmoving items are not consumed but has to scrap once they are obsolete and this causes significant strain on cash resource and financial performances for the organization Proposed Approach Fractal proposes two stages to build a reliable forecasting solution to be aligned with PG Predicting stage AI ML enabled forecasting model predicting accurate planned and unplanned demand for various SKUs Optimization stage Forecast inputs integrated with an inventory optimizer to propose optimized and balanced inventory Expected Benefits Ensuring effective inventory levels reduced risk and minimum downtimes. Huge potential savings with reduced scrappage and obsolescence Our Understanding of the Problem Completed .Reviewed Solution Scope Solution Design Engagement is limited to only one regionSBU and model will be developed based on 10 material SKUs within defined categories volumecostDTMTBF spread across portfolio Model should propose the future consumption for a Spare parts and also determine whether the consumption would be plannedunplanned UI layer will be using Power BI to illustrate the model output and recommendation 06 Our Solution Approach Planning Model Planning consumption model to classify the spare part consumption status Weighted methods to determine Forecastability Score using consumption time criticality parameters of spare parts Use heuristics ADICV2 to segmentclassify spare parts Forecasting Model AIMLdriven spare parts forecasting process by fitting the right forecasting models based on consumption segmentation Tournament selection method to determine best forecast for each spare part Tuning models to achieve the balance between model accuracy and business fitment Disaggregation Model Disaggregation of forecasted consumption into planned vs unplanned Use forecasting output and external variables to simulate scenarios. Examples machine failures downtime maintenance activities etc. to determine planned and unplanned Optimization Engine Optimization to balance criticality vs availability to find balance of inventory control parameters such as safety stock EOQROP minmax etc. Depending on nature of component and segments provide prescriptive recommendations on Inventory policies Multi step approach to Spare parts smart planning execution Smart Planning Phase 1 Phase 2 Two boxes Planning Model Forecastability score based on parameters generated from Consumption data only. Segmentation based on ADI and CVs 2. Forecasting Model Forecast of the consumption based on segments and forecastability score. Tournament selection Hyper parameter tuning Heuristics to disaggregate planned vs unplanned. No Simulation Optimization Will be phase 2 Keep the box Planning Model Planning consumption model to classify the spare part consumption status Weighted methods to determine Forecastability Score using consumption time criticality parameters of spare parts Use heuristics ADICV2 to segmentclassify spare parts Forecasting Model AIMLdriven spare parts forecasting process by fitting the right forecasting models based on consumption segmentation Tournament selection method to determine best forecast for each spare part Tuning models to achieve the balance between model accuracy and business fitment Disaggregation Model Disaggregation of forecasted consumption into planned vs unplanned Use forecasting output and external variables to simulate scenarios. Examples machine failures downtime maintenance activities etc. to determine planned and unplanned Optimization Engine Optimization to balance criticality vs availability to find balance of inventory control parameters such as safety stock EOQROP minmax etc. Depending on nature of component and segments provide prescriptive recommendations on Inventory policies Multi step approach to Spare parts smart planning execution Smart Planning Phase 1 Phase 2 Two boxes Planning Model Forecastability score based on parameters generated from Consumption data only. Segmentation based on ADI and CVs 2. Forecasting Model Forecast of the consumption based on segments and forecastability score. Tournament selection Hyper parameter tuning Heuristics to disaggregate planned vs unplanned. No Simulation Optimization Will be phase 2 Keep the box FORECASTING PLANNING DISAGGREGATION OPTIMIZATION Using Differential evolution to calculate Forecastability score of spare parts Build appropriate Forecasting models Stats ML DL based on different segmentation Ensemble of different models and Tuning to meet desired accuracy and bias Lower and Upper bound forecasted consumption to generate probabilistic forecasts Multi Objective optimization to find an optimal balance of the inventory control parameters Simulate scenarios such as failures downtime using forecast and external variables Disaggregation of forecasted consumption into planned vs unplanned Segmentation of different spare parts using consumption supply criticality parameters Recommendations on Inventory Policies for each segments Prescriptive recommendations on safety stock EOQ ROP MinMax for planned and unplanned consumption Smart Planning Analytical solution construct to spare parts smart planning Planning Consumption Ease of Forecasting Classification of spare parts based on Forecastability Score ML and Rulebased Supply Variability Material Usage Time and Cost based Criticality Assessment Differential Evolution to estimate weights to arrive at the final Forecastability score Augment Forecastability Score with ML Clustering methods Kmeansmedian based to arrive at Segments Diagnostics Statistical analysis to deep dive into temporal nature of the part wrt past patterns consumption usage manufacturing lt mtbf criticality etc Exploratory data analysis and feature engineering to capture global vs local patterns Multi objective decision criteria to balance criticality predictability and stability Segmentation Grouping like behaving spare parts based on the nature of the spare part and its dynamics Positioning Spare parts into segment s to enable appropriate forecasting process through enablement of smart forecasting method selection Explore results and validate output with business heuristics on the spare part segments ConsumptionSupply Ratios Mean Time Between FailureMTBF and cost Smart Planning Two boxes Ease of forecasting Forecastability score based on parameters generated from Consumption data only. 2. Segmentation Segmentation based on ADI and CVs No Simulation Optimization Will be phase 2 Planning Consumption Segmentation Smart Planning Different spare part groups will be optimized differently based on Forecastability score supply consumption rate time and cost criticality CAUSE Decide on the major variables in spare parts system consumption supply time cost criticality lifecycle BENEFIT Analyze forecastability criticality to determine which forecasting models to follow for each segment Low Consumption High Reliability ConsumptionSupply 1 High mtbf High Cost Nature Low Consumption Low Criticality ConsumptionSupply 1 High mtbf Low Cost Nature Low Consumption High Criticality Consumption Supply 1 Low mtbf High Cost Nature Low Consumption Low Reliability Consumption Supply 1 Low mtbf Low Cost Nature High Consumption High Reliability Consumption Supply 1 High mtbf High Cost Nature High Consumption Low Criticality Consumption Supply 1 High mtbf Low Cost Nature High Consumption High Criticality Consumption Supply 1 Low mtbf High Cost Nature High Consumption Low Reliability Consumption Supply 1 Low mtbf Low Cost Nature Mean Time Between FailureMTBF Illustrative Predictive models to better forecast spare parts consumption Smart Planning Forecast Consumption Identify appropriate forecasting model for each segment based on the consumption supply and criticality parameters Ensemble different models to determine best forecast and bias C ompare forecast with actual consumption to calculate the bias and error. Forecast Calibration Segmentpart level statisticalMLensemble forecast models Conformal Prediction to generate upperlower bounds with x Confidence Outcomes Objective is to generate spare parts consumption forecast which acts as an input for the disaggregation layer Generate short long term forecast upto N weeks consumption for each spare part 1 2 3 Consumption patterns Seasonality trends consumption variability Business decisions Changes in tactical organization policies price quality tolerance Internal External factors Weather inflation geopolitics Failures downtime trade policies AIML Accurate forecasts for spare parts consumption and planning Forecast per spare part months weeks day and plant or the endproduct Shortlong term forecast horizon Predictive models to better forecast spare parts consumption Smart Planning AIML model that best fits for each region category spare parts class via model tournament selection Medium Forecastibility Score Start End of WeekMonthQuarterYear flags Sinecosine features Cumulative and last Demand volume feature Low Forecastibility Score CNNs LSTMs Predictive models to better forecast spare parts consumption Smart Planning AIML model that best fits for each region category spare parts class via model tournament selection Medium Forecastibility Score Start End of WeekMonthQuarterYear flags Sinecosine features Cumulative and last Demand volume feature Low Forecastibility Score CNNs LSTMs Output Planned Planned Disaggregation into Planned vs Unplanned Approach Simulate scenarios Using forecasted consumption along with external variables to simulate scenarios using appropriate disaggregation methods such as bottom up top down to get planned vs unplanned consumption Example Demand spikes downtime failures manufacturing deviations raw material availability etc Disaggregation Disaggregation of forecasted consumption into planned vs unplanned Planned and unplanned consumption will be based on simulated scenarios forecasted consumption and external factors Outcomes Visibility of planned vs unplanned consumption from the forecast helps to plan inventory accordingly For efficient inventory planning the total consumption planned vs unplanned is used to calculate the right inventory parameters Smart Planning Scenario Planning to Disaggregate consumption into Planned vs Unplanned Smart Planning Internal Factors M achine downtime Planned maintenance Ca pacity Utilization Manufacturing deviations Machines failures C omponent subassemblies Continuous Scenario Monitoring Measure the impact of simulation on KPIs such as downtime failures external demand Monitor Scenarios with high degree of impact on consumption External Factors N etwork disruptions C hanges in macroeconomic indicators C limatic disruptions G eo political conditions Validate estimates Use latest information available to simulate the consumption and validate if current estimates hold good Impact of process variability Generate fresh estimates based on simulation parameters and enable scenario planning Scenario Planning Simulating Impact of InternalExternal Factors on Spare parts consumption forecast Simulate scenarios on both internal and external factors Disintegrate forecasted consumption into planned and unplanned consumption based on the internal and external factors Suggestrecommend optimal inventory and improved resource allocationincreasing shifts etc. for plants with low degree of impact Prescriptive Recommendations on inventory control parameters Quality Supply disruptions Int Ext Factors Inputs Multi Objective Optimization engine to incorporate weighted function for all the objectives and constraints Evaluation Criteria to find balance between inventory control parameters such as safety stock reorder point min max inventory EOQ on minimizing cost maximizing availability Additional business heuristics to make account for inventory levels constraints and other external factors Constraints Smart Planning Optimization SOP Downtime Failures Maintenance Climatic changes Macroeconomic Indicators Smart Planning Phase 2 MultiObjective Optimization to balance the criticality vs availability Smart Planning Multi Objective Optimization Mathematical programming model considering the objectives constraints and business requirements Obj 1 Minimize Cost Obj 2 Maximize Availability Input Parameters Forecast consumption Supply Time Window Cost Constraints SOP Downtime Failures Maintenance Inputs Constraints Generate various sets of weights using an optimal frontier. Weights ranges to be permuted on relative importance and criticality of achievement of each objective. Final Weights to be optimized using Rank Order Centroid Ratio Pairwise comparison satisfying business objectives. Mixed Integer Linear Programming Maximize Minimize W 1 Obj 1 W 2 Obj 2 ........ W 4 Obj 4 weighted sum of the given objectives Smart Planning Consumption Supply 1 Consumption Supply 1 Continuous Review RQ Segment Low Consumption Low Reliability When High Probability of failure. Quantity Q will depend on consumption and supply Continuous Review Two Bin Segment Low Consumption High Criticality When High Probability of failure. Two bins for components that can be resupplied quickly Set Par Level Segment Low Consumption Low Criticality When Low probability of failure and hence can be periodically reviewed. Order only when the minimum quantity is reached Periodic Review ST Segment Name Low Consumption High Reliability When Low probability of failure. The component will be reordered after a time interval T up to quantity Min Max Policy Segment Name High Consumption Low Reliability When High Probability of failure. Safety net to be maintained min quantity and order upto max quantity Min Max Policy Segment Name High Consumption High Criticality When High Probability of failure. Safety net to be maintained min quantity and order upto max quantity Periodic Review s S Segment Name High Consumption Low Criticality When Low probability of failure. A minimum quantity of s is maintained. Periodic Review ST Segment Name High Consumption High Reliability When Low probability of failure. The component will be reordered after a time interval T up to quantity Recommendations for identified business segments Illustrative 07 Our Implementation Methodology End State Vision for the overall Prediction model Logical View Data Ingestion Collection Connections Scheduler Data Sources Publish Activities Orchestration Triggers Configuration Persistence Layer Data Processing Transformation Master Data Machine Learning Reports PowerBI Users Govern Monitor DevOps Data Security Diagnostics Logging Audit Authentication Authorization Data Harmonization Data Aggregation Data Processing Consumption Model Reference Data Consumptions Data Production Data Spare Parts Production Data Master Data Publish Publish Spare Parts Consumption Finncial Production Parts Planning Financial Optimization Model Consumption Data Quality Validation End State Vision for the overall Prediction model Technical View PG Intranet Flat files Core Data Lake Service Endpoint Azure Storage Firewall Allow IP of DD API Whitelisted traffic Ingest Azure Data Factory Blob Storage PaaS Storage RAW Semi Processed Processed Quality Checked Processing Databricks Machine Learning Consume PowerBI Govern Monitor DevOps AppInsights Monitor Cost Management Diagnostics Key Vault RBAC AAD BLOB Sensor SharePoint Connector API Connector Harmonization Standardization Data Cleansing Quality Transformation Predictive Forecasting Databricks MLOps Reference Architecture Slides on governance Pardhasaradhy and pranav to update DevOps Reference Architecture Start Feature Request Feedback Bug Report etc Plan CodeDev Operate Test Deploy Build Kanban Scrum Sprint Microservice Architecture Git Branching and Workflow Code Quality and Security Cloud Architecture Data pipelines and Transformation Artifacts Packages Container Images Unit Test Functional Test Code Security Scanning Container Scanning Performance Testing Monitor Data Engineering Continuous Release Change and Release management Cloud Infra IaC Configuration Management Performance Management Error Tracking Capacity Monitoring Troubleshooting Incident and Problem Management Infra Monitoring Data Pipeline monitoring Measure ITIL Service Management Monitoring and Observability SLO SLA Dev Sprint velocity Bugs and Issues Log Monitoring CD DevQAProd App Performance Feedback Security and Monitoring Databricks CICD Develop and Test Clone repo Run unit and lint testing Publish Result Build Wheel files Push build files to Jfrog Artifactory Raise a PR for test Dev Pipeline Clone Repo Download artifacts Deploy artifacts and DB jobs Run E2E test Publish test Create a Release Candidate Promote RC to Artifactory Raise PR for release Test Pipeline Pipeline Definition from central DevOps Repo Code commit and PR to Dev branch Jfrog scans artifacts vulnerable packages can be blocked Code Quality and Coverage report Run E2E test on job and data 08 Commercials Engagement Model Scope Deliverables Outcome Updated the ScopeDeliverable Engagement Model Takshila to cross check RFP Response Guide Please give reference to which slides We will adopt an agile methodology to deliver incremental value every 2 weeks Milestone based delivery Discovery Design Thinking Data Model Development AI Modelling with Optimization UIUX Interface UAT Signoff with cutover Product Team Platform Team Business team using support from PG platform business teams across the project timeline Build unit testing of models and optimizer Model cross validations tuning and accuracy mapping with business defined criteria Hypercare with operational support Data bricks clusters compute workload and resources setup DataLake setups for managing raw and processed data Node scaling depending on workloads Collaborate with business team to validate and review model findings and recommendations Continuous feedback loop with models to improve and enhance Only If production required Commercials Takshila Indranil Pardhasaradhy to update Assumptions Engineering Design All software licenseinstallation will be provided by ABC and required folders and accesses to foldersone drive folder as required to automate the pipeline Model ML Ops Cloud infrastructure for deployment will be provided by ABC Access to data sources will be enabled via VPNVDI Data mapping and matching sessionsdocumentation will be provided Pardhasaradhy Pranav to update Assumptions 13 General Defined scope on business objective problem statement requirements data sources and key business questions will be aligned at the start of the project PG will review and provide notice of approval or rejection for the services and deliverables delivered by the Fractal within a reasonable timeframe OR 10 business days PG will provide access to Fractals personnel systems and documents as reasonably deemed necessary for Fractal to perform the services. All access to PG systems shall be subject to the PG Enterprise Security Standards. PG will work with Fractal to control and manage scope schedules resources and follow project change control processes as required PG will provide access to software hardware including PG network cloud infra within PG ecosystem The solution in scope of this proposal are as listed in the scope slide in this presentation 63. Any changes to scope post the discovery phase will lead to a reassessment with Fractal submitting a revised proposal. Project execution will happen remotely from Fractal offices Study complexity will be aligned before kickoff of any new study Request for additional Jmp scripts for the studies will be treated as a new request and will be considered as out of scope for this engagement Dashboard Setup to be done only after at least 2 weeks of data has been recorded Data Requirements Dynamic recommendation mechanism Prescriptive Recommendations Prescriptive recommendations on sourcing controls for different components Derive optimal inventory on different scenarios for planned and unplanned consumption Right control optimizer will give output Optimal inventory from optimizer Optimal Inventory Derive optimal inventory on different scenarios for planned and unplanned consumption To determine the optimal balance between the inventory control parameters such as safety stock reorder point min max inventory levels EOQ Inventory Policies Derive relevant inventory models based on the nature of the business segments and different scenarios it operates upon Example The business segments which has high failures need to be monitored continuously for a potential replacement Smart Planning Recommendations for identified business segments S afety Stock Objective Minimize Holding and order cost Generating EOQ for each component on the planned and unplanned scenarios given constraints demand inventory downtime failures EOQ To provide prescriptive recommendations on optimal inventory and sourcing controls for planned and unplanned consumption Reorder Points Reorder Point Analyze consumption EOQ Time Constraints Cost Constraints Criticality vs availability balance Calculate the initial safety stock for each component on the upper and lower bound of the forecasting model Determine optimal safety stock based on my planned and unplanned consumption Determine the reordering point based on my safety stock calculated for planned and unplanned consumption Once the level of inventory is reached the automatic action of replenishing the inventory can be triggered Smart Planning Planning Consumption Forecastability Smart Planning Step 1 Planning consumption Forecastibility score Parameters Scoring Algorithm Perform EDA Outlier treatment Missing data SMOTE Encoding Data augmentation Normality Test Random walk test Stationarity test Volatility test Differencing Normal transformation Average demand Interval Coefficient of variation Demand pattern movement Controllability Dynamicity Predictability Reliability Stability Provides the easy of forecasting Lower the score difficult to Forecast D ifferential evolution Activation of Inventory Policies Relevant inventory models could be followed by analyzing product specific eorder points review periods orderuptolevels Policies Cause Decide on the two major variables in an inventory control system order quantity ordering frequency Effect Analyze reorder pts order levels demand forecast to determine which inventory system to follow for each product group Smart Buying Smart Planning Step 2 Forecasting Actual technical methods Predictive models to better sense and forecast parts consumption An ensemble model which combines multiple models that are the best fit for each material class are used to predict demand. Forecasting methods Stats Model Machine Learning Deep Learning High Forecastability Score Mean and Variance Model Statistical models Auto correlation flag Heteroscedasticity flag Seasonality flag White Noise flag Med Forecastibility Score ML methods Low Forecastibility Score CNNs LSTMs Lag features Mean Encoding Weighted Mean encoding Start End of WeekMonthQuarterYear flags Sinecosine features Age of Spare part Cumulative and last Demand volume feature WIP Mean Time Between FailureMTBF S afety Stock Objective Minimize costs holding ordering Generating EOQ for each time period given constraints on Demand Inventory Storage Calculated initial safety stock based on daily usage and lead time Safety Stock simulation to arrive at optimal level that balance between service levels and minimizes cost Prescriptive Recommendations EOQ To provide prescriptive recommendations on sourcing controls and optimal inventory across various product categories Reorder Points One of the main inventory sourcing control method lies in reorder points Given safety stock the exact time to reorder can be calculated Reorder Point Analyze demand EOQ Forecast Demand Supplier Constraints Time Constraints Cost Constraints Cost Service balance Controls Smart Planning Step 4 Recommendations Inv policies BF High SF Low BF High SF High BF High SF Low BF High SF Low Run optimization on data. Specify variables to be used in optimization establishing the lower upper bounds of each variable Multiple run Runs on overall data random subset selection of SKUs with and without replacement Apply Ztest with 95 confidence interval to calculate final threshold value on above run sample outputs for each variable Run final optimization with the optimal derived coefficients from previous step of each variable. Output is a 2X2 Matrix depicting the quadrant each SKU belongs to. Placement of SKUs into quadrants will help decide the modelling technique to be adopted for that quadrant. Maximize the total SKUs that can be forecasted SKU Positioning OBJECTIVE Business Footprint Statistical Footprint Low High Low High 2. Classification of Spare Parts Business Footprint Impacting factors Controllability Predictability Statistical Footprint Impacting factors Reliability Stability Dynamicity Demand Forecasting Inventory Optmization Forecastibility Score Classification of spare Planning Model Data from various ERPs are structured and aggregated into a unified repository Perform the required EDA though different stages like feature engineering hypothesis testing transformation Estimate the forecast ability score for each SKU through Differential evaluation determining whether the required SKU is forecastable or not Forecasting Model Derive the business and technical footprint score for each SKU based on their application and statistical inference respectively. Classify the SKUs and position them in the quadrants based on derived scores disaggregation Model AIMLdriven demand forecasting process built in by identifying the appropriate forecasting model based on the nature and state of quadrant Perform the iterative runs through hyper parameter tuning to meet the desired accuracy and bias Recommendations Recommend the optimized inventory for Safety stock reordering point and max upto level based on the given inventory norms cost and desired service level Prediction of NPI due to excess stock assessment on cost Multi step approach to improve the replenishment decision and business performance Smart Planning In Model Out Model Planning Consumption model What will be consumed Different

al evolutionranges weights Variables to used Forecastability score segmentationclassification ADI vs CV smooth erratic lumpy intermittent No Forecasting parts model How much to forecast Spare part Forecasting Optimization layer use planned unplanned from step 3 How much tolerance to allow Inventory control parameters which is depending on nature of component if rQ vs sS safety stock rop Min max policy and Plannedunplanned disaggregation how much of your forecasted consumption in step 2 is of plan vs unplanned Reference Slide Data Preprocessing to analyze and derive features for the model K Means Hierarchical clustering for SKU segmentation based on business inputs FSN XYZ Median based analysis to group and select the products to analyze Classification of products based on different supply and demand indicators Order quantity Demand Reorder frequency Replenishment count Product lifecycle discounts Grouping like behaving Suppliers since they exhibit different behaviors based on the nature of the business model. Product segments and selection based on clustering analysis Explore results and validate output with business on the products segments Planning Consumption Segmentation Approach Outcomes Smart Planning Step 1 Planning consumption Forecastibility score MultiObjective Optimization to balance the criticality vs availability Smart Planning Quality Supply disruptions External Factors Inputs Multi Objective Optimization engine to incorporate weighted function for all the objectives and constraints Evaluation Criteria to include Availability Criticality Cost Lead Time Supply Additional business heuristics to make account for inventory levels constraints and other external factors Constraints Demand patterns Smart Planning Optimization Consumption Supply Inventory Position Demand Spikes Downtime Failures Cost Usage Time Window Quality Cost Supplier Reputation Climatic changes Macroeconomic Indicators Step 3 disaggregation Optimization Smart Planning Multi Objective Optimization Mathematical programming model considering the objectives constraints and business requirements Obj 1 Max Margin Min Cost Obj 2 Maximize Quality Obj 3 Minimize Lead Time Delay Obj 4 Maximize Supplier Reputation Input Parameters Demand Inventory Supplier Information Cost Prices Time Window Constraints Inventory on hand Safety stock MOQ Supplier activation Product choice activation Minimum d emand M axMin price range Storage capacity Inputs Constraints Generate various sets of weights using an optimal frontier. Weights ranges to be permuted on relative importance and criticality of achievement of each objective. Final Weights to be optimized using Rank Order Centroid Ratio Pairwise comparison satisfying business objectives. Mixed Integer Linear Programming Maximize Minimize W 1 Obj 1 W 2 Obj 2 ........ W 4 Obj 4 weighted sum of the given objectives Step 3 disaggregation Optimization MultiObjective Optimization to balance the criticality vs availability 3. Forecast demand for various classes of materials with right techniques An ensemble model which combines multiple models that are the best fit for each material class are used to predict demand. Forecasting methods Moving Average Exponential smoothening SyentesosBolan Teunter Syentesos Babi Crostons forecasting Estimates next periods value by aggregating previous period demands Adjusts previous period demand with current period demand Forecast strategy for intermittent demand Exponential smoothening based on average size Average interval between demands is calculated Corrects for bias Updates demand probability Method is unbiased Achieves high flexibility by using different smoothing constant for demand size and demand probability Demand Forecasting Inventory Optmization Forecastibility Score Classification of spare Product Segmentation activation Low demand Low Perishability Low Demand Medium Price Slow moving Medium Perishability Low Demand High Price Perishables Fast moving High Perishability High Demand Low Price Bulk purchase Low Perishability Low Demand High Price Long term forecast based on historical demand Price based on discounts quantity Long term forecast based on reorder frequency replenishment count Ex Milk bread Products with lumpy erratic demand Price based on seasons quantity Forecast based on demand seasonality and quality Ex Electronics dispenser Different product groups will be optimized differently based on lifecycle demand consumption rate price discounts Smart Buying Supplier activation mapping Different supplier groups discounting will be activated at different points in time and different conditions 01 Fixed Price discounts Variable Price discounts Volume based discounts Time based discounts VolumeTime based discounts Fixed price throughout a time zone for a given product from Independent suppliers Best for unique artisanal orders with a tat of 16months Different price during different time zone for a given product from Importers Best for seasonal orders with a tat of 16months Suppliers with discount grid based on the order quantity from Wholesalers Best for bulk orders with a tat of 3weeks2months Suppliers with discount grid based on the time of order from Manufacturers Importers and wholesalers Best for seasonal demand products with a tat of 16months Suppliers with discount grid based on volume and time of the order from Manufacturers Best for custom and bulk orders with a tat of 1week2months Smart Buying Approach Business requirement Creating long term forecasts on sales historyPOS data Making forecasts more robust for short term dynamic inventory planning by adding current events from external data sources Provide a range of prices available at Product X Vendor level to the retailers Long term forecast models based on different demand patterns Demand sensing layer on demand signal changes to execute shortterm forecast adjustments Causal model to identify the network impacting the price and quantile regression model to predict the price range Sensing demand and Price elasticity using analytics Outcomes Improved short term forecast accuracy and demand responsive supply chains Optimal inventory and min overall costs by deploying demand sensing model Benefits including high profit margins decreased inventory levels and cost leads to better order performance Smart Buying Predictive models to better sense and forecast parts consumption Demand patterns Seasonality trends demand variability Business decisions Changes in promotions price storage External factors Weather holidays inflation congestion AIML Accurate forecasts for retail planning Forecast per product months day and store or fulfillment channel Longterm forecast horizon Demand Engineering Inventory POS data for a minimum of N years along with current external d ata m acroeconomic i ndicators Check for seasonality demand and classify products into lumpy smooth intermittent erratic by CV and ADI D emand Sensing Product level statisticalMLensemble forecast models C ompare forecast with actual demand to calculate the bias and error. E xecute shortterm forecast adjustments based on sensed demand patterns Outcomes Objective is to generate demand forecast which acts as a constraint for the optimization layer Generate long term forecast for 12 18 months quantity for each product or category 1 2 3 Smart Buying Causal impact mapping to simulate optimal price ranges Establish causality and generate a causal network to identify the factors impacting price Quantile Regression Outcomes Predicted Minimum and Maximum selling prices for a product at a given time period Price will act as a constraint as well as a criterion to select or reject a supplier Causal Modelling Estimate price distribution using MC sampling and the establish causal network Using quantile regression predict the minimum and maximum price of a product at a given time by having 50 for min price and 95 for max price range Product Features Purchase Frequency Time Historical demand Features impacting Price Seasonality Smart Buying Optimal Buying through Optimization algorithms Quality Checks Network disruptions External Factors Inputs Multi Objective Optimization engine to incorporate weighted function for all the objectives and constraints Evaluation Criteria to include Cost Profit margin Perishability Lead Time Delivery delay Supplier Reputation Additional business heuristics to make account for inventory levels constraints and other external factors Constraints Port Congestions Buying Patterns Optimization Product Availability Price Inventory Position Inventory Demand Min Demand Supplier Activation Storage Price Time Window Quality Cost Supplier Reputation Climatic changes Macroeconomic Indicators Smart Buying Multi Objective Optimization Mathematical programming model considering the objectives constraints and business requirements Obj 1 Max Margin Min Cost Obj 2 Maximize Quality Obj 3 Minimize Lead Time Delay Obj 4 Maximize Supplier Reputation Input Parameters Demand Inventory Supplier Information Cost Prices Time Window Constraints Inventory on hand Safety stock MOQ Supplier activation Product choice activation Minimum d emand M axMin price range Storage capacity Inputs Constraints Generate various sets of weights using an optimal frontier. Weights ranges to be permuted on relative importance and criticality of achievement of each objective. Final Weights to be optimized using Rank Order Centroid Ratio Pairwise comparison satisfying business objectives. Mixed Integer Linear Programming Maximize Minimize W 1 Obj 1 W 2 Obj 2 ........ W 4 Obj 4 weighted sum of the given objectives Multiobjective allocation and scheduling optimization Smart Buying Dynamic recommendation mechanism Prescriptive Recommendations Safety Stock EOQ ROP Enhance E2E visibility on key KPIs for r ecommending optimal inventory and sourcing controls across various product categories Inventory Policies Demand Determining optimal buying policies such as JIT Bulk orders for each product group based on demand Inventory and discounts Switching Rules Supplier MOQ Order Quantity The objective is to arrive at rules for changing the order quantity by switching to a lower or higher value of Supplier MOQ depending on demand Inventory on hand Smart Buying S afety Stock Objective Minimize costs holding ordering Generating EOQ for each time period given constraints on Demand Inventory Storage Calculated initial safety stock based on daily usage and lead time Safety Stock simulation to arrive at optimal level that balance between service levels and minimizes cost Prescriptive Recommendations EOQ To provide prescriptive recommendations on sourcing controls and optimal inventory across various product categories Reorder Points One of the main inventory sourcing control method lies in reorder points Given safety stock the exact time to reorder can be calculated Reorder Point Analyze demand EOQ Forecast Demand Supplier Constraints Time Constraints Cost Constraints Cost Service balance Controls Smart Buying 1. Identify forecastability and its degree to select appropriate method to forecast reliably PARAMETERS SCORING ALGORITHM Perform EDA Outlier treatment Missing data SMOTE Encoding Data augmentation Normality Test Random walk test Stationarity test Volatility test Differencing Normal transformation Average demand Interval Coefficient of variation Demand pattern movement Controllability Dynamicity Predictability Reliability Stability Provides the easy of forecasting Lower the score difficult to Forecast D ifferential evolution BF High SF Low BF High SF High BF High SF Low BF High SF Low Run optimization on data. Specify variables to be used in optimization establishing the lower upper bounds of each variable Multiple run Runs on overall data random subset selection of SKUs with and without replacement Apply Ztest with 95 confidence interval to calculate final threshold value on above run sample outputs for each variable Run final optimization with the optimal derived coefficients from previous step of each variable. Output is a 2X2 Matrix depicting the quadrant each SKU belongs to. Placement of SKUs into quadrants will help decide the modelling technique to be adopted for that quadrant. SKU POSITIONING OBJECTIVE Maximize the total SKUs that can be forecasted Business Footprint Statistical Footprint Low High Low High 2. Classification of Spare Parts Business Footprint Impacting factors Controllability Predictability Statistical Footprint Impacting factors Reliability Stability Dynamicity 3. Forecast demand for various classes of materials with right techniques An ensemble model which combines multiple models that are the best fit for each material class are used to predict demand. Forecasting methods Moving Average Exponential smoothening SyentesosBolan Teunter Syentesos Babi Crostons forecasting Estimates next periods value by aggregating previous period demands Adjusts previous period demand with current period demand Forecast strategy for intermittent demand Exponential smoothening based on average size Average interval between demands is calculated Corrects for bias Updates demand probability Method is unbiased Achieves high flexibility by using different smoothing constant for demand size and demand probability 4. Inventory Optimization OUTPUTS Segmenting demand streams based on value and variability A B and C categories based on value X Y and Z categories based on variance Product segments are represented as a 3X3X3 Matrix with ABC Classification VED Classification and XYZ Classification Generating Inventory Norms using Multi Echelon I nventory Optimization Engine Safety stock. Reorder Pont Target stock Minimum stock and Maximum Stock are generated by the optimization engine Decreasing Variability Increasing Value Decreasing Criticality Consumption History Forecast errors Lead Time History Criticality IN PUTS We will enable continuous monitoring of production processes to detect deviations and trigger optimization 1 3 5 Data streaming Variation encoder Determine constraints Ingest process data from source Create data layer to store incremental data Meta heuristic layer to incorporate business constraints Self supervised encoderdecoder layers Label data as per itemmachineline combinations Train encoder models on historical data Detect planprocess anomalies Calculate latest process constraints on deviation detection Trigger optimization pipeline Determine optimal plan alternatives Input variables Production Schedule Telemetry data Temperature Pressure volume Demand Production Sequence Production Capabilities Inventory Levels Plant information Labor Material Availability Machine Planned Downtimes Machines labor plant holidays Cost labor machine movement cost 6 2 4 Business heuristics Detect deviations Trigger optimization Integrated data platform Data extraction harmonization layer Decision cockpit layer Material Inventory optimization at Supply Network Nodes End state solution construct Safety Stock Prediction Model 1. Predicted Safety Stock Quantity 2. Target Days Supply 3. Updated Demand forecast 4. Actual order quantities Revised Inventory Parameters 1. Revised Safety Stock Quantity 2. Reorder Point Min Inv. Level 3. Max Stock Level 4. Planned Orders 5. Inventory Projections Segmentation of Inventory Plotting inventory consumption over 13 weeks considering sS inventory policy rQ inventory policy Erratic volatile demand Demand Forecasting Layer Source system Data to deliver incremental value every 2 weeks AI Modelling with Optimization UI Insights recommendations Data Model Development Framework building for data extraction transformation loading into semantic model. Checks on data quality and data consistency . Exploratory data analysis while reporting and validating findings sharing insights. Data harmonization Feature engineering to support the model development training Planning consumption model to classify the spare part consumption status Forecasting Parts model to forecast the spare parts needs segregated by plannedunplanned Multiobjective optimization balancing the business metrics of criticality vs availability Development of UI Screen for sharing trends insights UI screen showcasing the forecasting results recommendations Model performance monitoring insights Milestone based delivery Pre Kick off Infra setup data availability Hyper care of data model along with developed models UI Model monitoring and setting up alerts and early warnings ModelOps Framework Monitoring and setting up alerts for drifts MLOps Gearing up for scale HIGH LEVEL PROJECT PLAN Recalibration Point GoLive Kick Off HLD and TDD Preparation Preparation of data from 14 data sets Ingestion framework configurations Data Harmonization Unit test SIT and Performance Testing Unit test SIT and Performance Testing for 1 st Set of Harmonization Build Unit test SIT and Performance Testing for 2 nd Set of Harmonization Build Unit test SIT and Performance Testing for 2nd Set of Harmonization Build UAT Cutover Deployment Hypercare UAT Exploration Zone Ingesting of data from 14 data sets into GCP Exploration Zone Building Data Product Milestone We will require support from PG across the project timelines Week 14 Week 0 Week 5 Week 12 Week 8 Discovery Design Thinking UAT DevOps release Week 10 Week 16 Discovery Design Thinking Infra team Business team Product team UAT DevOps release Development Business Infra Accesses UAT Validation Product owner Scrum master FP Business user Sprint planning sprint review Sprint based UAT Executive summary What we heard from you Region Europe Stakeholders Demand planner Supply planner Customer service Warehouse and logistics team Spa re Parts NNN Background Need for a unified Reason Coding platform for error managementanalysis Reason code initial KPIs for Digital lab OTIF MAPE BIAS SOTIF MVP solution to solve for 50 users now to be expanded later as per feedbackfeasibility Expected Outcomes User persona and stories alignment Solution and technical Architecture BRD and solution themes with logic alignments Data prerequisites for MVP Solution roadmap Proposal for MVP Approach Deliverables Business decision tree for reason code Requirement document with solution roadmap User stories MVP proposal Design thinking workshop cocreation with usersstakeholders 4step double diamond process Discover Define Ideate Design Update Scope 10 material SKUs in a region based on defined criticality volumecostDTMTBF spread across portfolio Model at CategoryPlant or CategoryPlantmaterial level depending on complexity Build predictive model that determines what parts will be consumednot consumed Build predictive model that determines if a consumption of a part will be plannedunplanned 3 screens PowerBI solution Timeline 2weeks discovery 12 weeks development UAT 4 weeks monitoring People till development 0.5 Soln lead 1 SC consult 2 DS 1 PBI 6 weeks 0.5 PM other Pardha People post development 0.5 Soln lead 1 DS 0.5 PM other Pardha Need to remove Note sure if this needs formatting Ashmi Fractal is uniquely positioned to deliver this engagement Provided fact based supply chain solution to multiple clients which has transformed their business operation to grow both vertically and horizontally Inter disciplinary team of domain experts designers and behavioral scientists is uniquely positioned to strategize rapidly prototype and deliver scaled programs end to end 01 We understand spare parts ecosystem across CPG and manufacturing Integrating technology design and AI for large scale analytics deployment and drive adoption enabled by functional SMEs has promoted Fractal as one of strategic channel partner across industries 0 2 We have extensively worked on AIML across technology 03 Extensive experience in delivery rapid protyping through agile process 04 Supply Chain Thought leadership Thorough understanding of PG Supply processes through successfully delivering engagements like CSL CPM CPFR DnD Data engine work Work with 5 of the top 10 CPG in their Supply chain planning space. Acknowledged as Thought Leader in Supply Chain by Gartner Agile Unique and dedicated team of functional consultants technology experts and visualization designers to execute seamlessly To ensure smooth operations we commit to include 70 of the team who have spent 12 months on PG relationship Committed investment in a 3week PG Supply chain induction program In progress Formatted on slide 20 AIML based p redictive models to improve decisions on the spare part replenishment strategy Analytical foundation minimizing split shipments while maximizing serviceability Reducing split shipments rate OBJECTIVE approach A. Preseason enablement Increase analytical maturity by optimal allocation B. Withinseason recalibration Increase business value by efficient monitoring and rebalancing Adopt efficient processes to increase effectiveness of initial allocation preseason across the entire distribution network Improving stock availability and defining dynamic inventory switching rules for ongoing maintenance within season Indranil to update Smart Forecasting to provide 01 Reason to Believe

P&G_Spare_parts_forecasting V2.pptx

Spare Parts Smart Planning Fractal Analytics February 2023 01 02 03 04 05 06 07 08 Reason To Believe Success Story Spare Parts Forecasting Our Understanding of the Need Our Solution Approach Implementation Methodology Commercials Engagement Model Fractal.ai PG Fractal Journey Content 01 Reason to Believe 01 We understand spare parts ecosystem across CPG manufacturing Provided reliable resilient and scalable supply chain solution to multiple CPG and manufacturing clients which has transformed their business operation to grow both vertically and horizontally 02 We have extensively worked on AIML across technology Integrating technology design and AI for large scale analytics deployment and drive adoption enabled by functional SMEs has promoted Fractal as one of the strategic channel partners across industries 03 Extensive experience in delivery rapid prototyping through agile process Fractal has followed Agile delivery approach for multiple client engagements enabling faster time to market with complete team visibility and task transparency 04 Supply Chain Thought leadership extensive domain expertise Thorough understanding of PG Supply processes through successful engagements like CSL CPM CPFR DnD Data engine work PG NGS success stories include TOTaL Smoothie King HoneyComb Dynamic Docking SpyGlass Fractal is uniquely positioned to deliver this engagement Why Fractal Improve Customer Service levels Optimal working capital Inventory Manufacturing excellence through Smart factory Reduce bottom line costs Faster distribution logistics Digital supply chain transformation through AIML solutions On Time Delivery risks solution to predict OTD risks at shipment level and recommendations to mitigate Customer Service Level solution to determine root cases predict In full risk and recommendations to mitigate SafeStock solution predicts optimal Inventory norms End of Life solution determines life cycle stage of product remaining life before delisting OOS solution predicts OOS risks at retailer shelves Inventory Control tower enables cross functional insights Portfolio optimization Manufacturing visibility solution provides 360 insights across PQCDSM Predictive maintenance solutions for real time anomalies spare parts optimization Predictive Quality to model critical process parameters impacting the output grade of FG Cost to serve solutions to identify Cost drivers scenario planning Sourcing Optimizer solution to identify alternative sourcing choices formulations Blend optimizer for effective RM usage consumption Waste management solution to minimize Mfg Waste Dynamic order fulfilment solution to enable same day deliveries Warehouse Mission control to automate DC operations SOE solution to enable operational decisions of fulfilment quotas and priorities Sustainability solution provides insights on Carbon emissions Sustainability metrics across Manufacturing Logistics Sourcing Risk Resilience solution provides risk probability across Supply network nodes and recommends alternatives to mitigate Domain Expertise Weve enabled transformation initiatives and bottomline improvements for our clients across the supply chain 1Bn savings through e2e inventory optimization 25Mn reduction in transportation costs 52Mn savings through alternative formulations 1.3Bn reduction in working capital over 4 years 2Bn Identified new growth opportunities 3Mn Savings with reduced off spec 15 Reduction in capex through longterm capacity planning 1Mn savings per material due to optimized purchase EOQ Impact Created Fractal has partnered with Fortune100 to drive supply chain resiliency Key Clients 02 Success Story Automated e2e forecasting inventory optimization for spare parts Client ask Result Forecast the consumption of Spare parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution 2Mn savings across 30 of Spare parts accessories globally Huge potential savings with scale Efficient utilization of spare parts with AIML based forecasting inventory optimization resulted in 2Mn Cost savings Solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process AIML driven methodology leading to a fully automated procurement system 1 2 3 4 Differential evolution optimization Performing various statistical analysis and hypothesis Differential evolution optimization for SKU Positioning into quadrants to understand Past behaviors and global behaviors Decide modelling technique for Forecasting demand DL CNN LSTM ML Random forest Statistical A SRIMAX GARCH Replenishment parameters at SKU level Min Max ROP safety stock EOQ Dynamic recommendations on minmax inventory norms Estimating and highlighting emerging trends and disconnects. Simulation of inventory norms w.r.t service levels supply demand parameters 03 Our Understanding of the Need Improving spare part tracking visibility through data driven decision making AI modeling to strike an optimal balance. Ensuring effective inventory levels reduced risk optimized budget spending and fewer unplanned downtimes. Background PG is a leading consumer good manufacturer with Specialty plants in numerous locations. PG is looking forward to a strategic partner who can help them with AIML based predictive models which can improve decisions over the spare part replenishment strategy Current GapsPain Points Several slowmoving MRO parts are rarely utilized or consumed but the organization must hold in stock to meet contractual obligations business continuity or capture commercial opportunities Due to lack of visibility of the future demand often these slowmoving items are not consumed but must scrap once they are obsolete and this causes significant strain on cash resource and financial performances for the organization Proposed Approach Fractal proposes two stages to build a reliable forecasting solution to be aligned with PG Phase 1 Predicting stage AIML enabled forecasting model predicting accurate planned and unplanned demand for various SKUs Phase 2 Optimization stage Forecast inputs integrated with an inventory optimizer to propose optimized and balanced inventory Expected Benefits Ensuring effective inventory levels reduced risk and minimum downtimes. Huge potential savings with reduced scrappage and obsolescence Our understanding of the problem Solution scope Current scope in Phase 1 which will only be prediction element while optimizing for business success could be a natural evolution to phase 2 and not part of this proposal scope Engagement is limited to only one region and model will be developed based on 10 material SKUs within defined categories on the criticality measure volumecostDTMTBF spread across portfolio Model will propose the future consumption for a spare part and determine whether the consumption would be plannedunplanned nature UI layer will be using Power BI to illustrate the model output and recommendation 04 Our Solution Approach Planning Model Planning consumption model to classify the spare part consumption status Weighted methods to determine Forecastability Score using consumption related parameters Use consumptionbased classification model to segment different spare parts AIMLdriven spare parts forecasting process by fitting the right forecasting models on consumptionbased segment Tournament selection method for each spare part along with tuning methods to calculate the best forecast Using heuristic methods disaggregate the forecast into plannedunplanned Forecasting Model Optimization to balance criticality vs availability to find balance of inventory control parameters such as safety stock EOQROP minmax etc. Depending on nature of component and segments provide prescriptive recommendations on Inventory policies Optimization Engine Phase 1 Phase 2 Multi step approach to spare parts smart planning execution Smart Planning Current Scope Future Scope Ease of Forecasting Segmentation Using the consumption data perform EDA steps Feature Engineering Hypothesis testing Statistical Transformation Parameter Tuning Differential Evolution to estimate weights to arrive at the final Forecastability score Grouping like behaving spare parts based on the consumption patterns ADICV2 Positioning Spare parts into segments to enable appropriate forecasting process through enablement of smart forecasting method selection Planning Consumption Smart Planning Phase 1 Predictive models to better forecast spare parts consumption Smart Planning AIML model that best fits for each region category spare parts class via model tournament selection Medium Forecastibility Score Start End of WeekMonthQuarterYear flags Sinecosine features Cumulative and last Demand volume feature Low Forecastibility Score CNNs LSTMs Output Planned Unplanned Heuristics to disaggregate Planned vs unplanned Phase 1 Prescriptive recommendations on inventory control parameters Quality Supply disruptions Int Ext Factors Inputs Multi Objective Optimization engine to incorporate weighted function for all the objectives and constraints Evaluation Criteria to find balance between inventory control parameters such as safety stock reorder point min max inventory EOQ on minimizing cost maximizing availability Additional business heuristics to make account for inventory levels constraints and other external factors Constraints Smart Planning Optimization SOP Downtime Failures Maintenance Climatic changes Macroeconomic Indicators Smart Planning Phase 2 05 Implementation Methodology End state vision for the overall prediction model Logical view Data Ingestion Collection Connections Scheduler Data Sources Publish Activities Orchestration Triggers Configuration Persistence Layer Data Processing Transformation Master Data Machine Learning Reports PowerBI Users Govern Monitor DevOps Data Security Diagnostics Logging Audit Authentication Authorization Data Harmonization Data Aggregation Data Processing Consumption Model Reference Data Consumptions Data Production Data Spare Parts Production Data Master Data Publish Publish Spare Parts Consumption Financial Production Parts Planning Financial Optimization Model Consumption Data Quality Validation End state vision for the overall prediction model Technical view PG Intranet Flat files Core Data Lake Service Endpoint Azure Storage Firewall Allow IP of DD API Whitelisted traffic Ingest Azure Data Factory Blob Storage PaaS Storage RAW Semi Processed Processed Quality Checked Processing Databricks Machine Learning Consume PowerBI Govern Monitor DevOps AppInsights Monitor Cost Management Diagnostics Key Vault RBAC AAD BLOB Sensor SharePoint Connector API Connector Harmonization Standardization Data Cleansing Quality Transformation Predictive Forecasting Databricks MLOps reference architecture DevOps reference architecture Start Feature Request Feedback Bug Report etc Plan CodeDev Operate Test Deploy Build Kanban Scrum Sprint Microservice Architecture Git Branching and Workflow Code Quality and Security Cloud Architecture Data pipelines and Transformation Artifacts Packages Container Images Unit Test Functional Test Code Security Scanning Container Scanning Performance Testing Monitor Data Engineering Continuous Release Change and Release management Cloud Infra IaC Configuration Management Performance Management Error Tracking Capacity Monitoring Troubleshooting Incident and Problem Management Infra Monitoring Data Pipeline monitoring Measure ITIL Service Management Monitoring and Observability SLO SLA Dev Sprint velocity Bugs and Issues Log Monitoring CD DevQAProd App Performance Feedback Security and Monitoring Databricks CICD Develop and Test Clone repo Run unit and lint testing Publish Result Build Wheel files Push build files to Jfrog Artifactory Raise a PR for test Dev Pipeline Clone Repo Download artifacts Deploy artifacts and DB jobs Run E2E test Publish test Create a Release Candidate Promote RC to Artifactory Raise PR for release Test Pipeline Pipeline Definition from central DevOps Repo Code commit and PR to Dev branch Jfrog scans artifacts vulnerable packages can be blocked Code Quality and Coverage report Run E2E test on job and data We will adopt an agile methodology to deliver incremental value every 2 weeks Milestone based delivery Discovery Design Thinking Data Model Development AI Modelling with Optimization UIUX Interface UAT Signoff with cutover Dev Team Platform Team Business team using support from PG platform business teams across the project timeline Build unit testing of models and optimizer Model cross validations tuning and accuracy mapping with business defined criteria Hypercare with operational support Data bricks clusters compute workload and resources setup DataLake setups for managing raw and processed data Node scaling depending on workloads Collaborate with business team to validate and review model findings and recommendations Continuous feedback loop with models to improve and enhance Only If production required 06 Commercials Engagement Model Commercials Assumptions for Commercials The above cost is Fractal service cost 3 GBS cost will be charged separately Any hardware software or travel related costs will be charged on actuals based on prior approval from project sponsors  The timelines and efforts assume that we will have the required timely support from PG stakeholders for discussions reviews and signoffs getting systems accesses etc. Any delays will impact timelines and may have a corresponding impact on project costs Scope Deliverables Outcome Engagement Model RFP Response Guide Assumptions 12 Engineering Design All software licenseinstallation will be provided by PG and required folders and accesses to foldersone drive folder as required to automate the pipeline Data Ingestion Processing Data will be ingested from 2 sources not exceedingly more than 5 data files or types such as Spare Parts Production data Consumption data Basic data validation checks such as null values relevant columns data type mismatches shall be performed. Any data quality check aligned with business rules is out of scope Complex data transformations such as multi column mapping and data merging from multiple datasets are not considered in the estimation to be further discussed with PG before project kickoff Model ML Ops Cloud infrastructure for deployment will be provided by PG Access to data sources will be enabled via VPNVDI Data mapping and matching sessionsdocumentation will be provided Assumptions 2 2 General Defined scope on business objective problem statement requirements data sources and key business questions will be aligned at the start of the project PG will review and provide notice of approval or rejection for the services and deliverables delivered by the Fractal within a reasonable timeframe OR 10 business days PG will provide access to Fractals personnel systems and documents as reasonably deemed necessary for Fractal to perform the services. All access to PG systems shall be subject to the PG Enterprise Security Standards. PG will work with Fractal to control and manage scope schedules resources and follow project change control processes as required PG will provide access to software hardware including PG network cloud infra within PG ecosystem The solution in scope of this proposal are as listed in the scope slide in this presentation. Any changes to scope post the discovery phase will lead to a reassessment with Fractal submitting a revised proposal 07 Fractal.ai Our aspiration Powering every human decision in the enterprise Founded in 2000 60 Fortune 500 clients 22 00 Employees 100 Countries 17 Global offices Kuala Lumpur Sweden Growth by focusing on clients people innovation Global Recognitions 08 PG Fractal Journey Relationship Summary ACHIEVEMENTS AND ACCOLADES AT A GLANCE PG TEAMS THAT WORK WITH US DA CFTI Strategic Revenue Management SKU Portfolio Optimization STC Analytics global solutions Analytics and Insights AI Embedded teams with all major SBUs regions and customer teams for deep dive analysis on key business questions Product Supply TOTaL application iPlanning Europe Command Centre Lane Management AMA Control Tower SPO BI DevOps Create data marts develop and support backend data architecture DA Dev Ops CFTI SPO BI SMO IT AMA SOI PSKU Korea Data Platform Pollux Database etc.  CMK Connected Room Solutions Services KBD Drainers Drivers Ecommerce Digital analytics Central IBO team Other PG teams   Next Generation Services NGS RD MyPG Services Continued push for better and more useful analytics 60 of the PG portfolio is investigative and predictive analytics in addition to a dozen cloud implementations at PG. Delivering 25 efficiency and productivity Through scaleup of services automation and process standardization including 10 hard savings annually. Engagements 375 Fractal practitioners committed 99 OND 22 Net Promoter Score Quality of Service 110 82 2 280 60 250 200 2020 E2E Problem Solving Application development DS Advanced Analytics Continually evolving client conversations work profile team 350 202122 Data Science partnership New MSA renewal for next 5 years Scale with people Embedded consultants to region business units Cross Functional Skillset across AI Engineering and Design Regular Consulting training Focus on relevance value Processes Data partnerships TPA Data security setup Performance measurement Process Governance Technology Data Engineering to give you scale Integration with PG set up Alignment on business consumption Sophisticated analytics using AIML Partnership Engaged across business units of AI CMK GBS PS Current focus on driving synergies with rest of the organization Key elements of our successful partnership Done

Pfizer – SC Risk and Resilience.pptx

Supply Chain Risk and Resilience Minimize impact of disruptions before they occur by making supply chains more resilient Todays Supply chains need to have good visibility intelligence on upcoming risks Operational Predict operational risks with impact across nodes Track open shipments as well as strategic network Strategic Assist in long term strategic decisions by summarizing cost and relevant performance metrics Cognitive AI Recommend optimal strategies for mitigation of risk Cost and performance tradeoffs among recommendations Enabling Resilient Supply Chains through traceability risk prediction and mitigation options Traceability Sustainability Product traceability Enhanced E2E visibility to smoothen collaboration across nodes and post event analysis Risk prediction Alternative strategies Risk alerts Pinpointing upcoming failures in advance with mitigation assistance Scenario planning Scenario comparison Whatif simulations Cloud based planning capability to assess impact of various parameters and tradeoffs among scenarios Risk Resilience BioPharma Procurement Inventory Manufacturing Transportation Supply disruptions Supplier issues Lead times Material defects Material quality Errors in ordering or procurement Sole sourcing of critical consumables Lack of substitutes alternatives ancillary supplies Diversity within suppliers Maintain supplier risk profiles Excess Inventory Cost of excess inventory Order volume Out of date stocks   LifetimeShelf lifeExpiry Inventory mismanagement Less inventory turnover Lack of  Order visibility InboundOutbound Unused wasted supplies Increase consumable inventory of vital materials Over production under production Unplanned downtimes Usage of nonrenewable resources nonreusable Production scrap fifo Excess gas emission Capacity constraints Demand surges Supply and demand shocks Counterfeit products Power outages Product with single global manufacturing center Quality assurance challenges Fire life safety loss prevention Gene therapy Production scaling Good manufacturing practiceGMP Cleanroom production Usage of right packaging material Tamper resistant packaging Hazards Equipment damage  compression vibration climate temperature light moisture Contamination and fermentation risks Low quality packaging materials Leakage pinholes Proper sterilizing sealing labeling Shattering of glasses at ultracold temperatures Single use disposable packaging Nonsustainable Storage innovation and management Hygiene management Reusable packing materials Provide relevant product  information bar codes on packaging Product traceability Spoilage at Points of care Waiting times Logistics delays docking Routing issues driver shortages Delivery delays No alternate routes Product availability status in other DCs Temperature control Cold chain logistics   Thermal shippersultracold freezerGPSenabled thermal sensors Environmental changes Excess gas emission Central storage Exposure to light and air Build redundancy Preserving the medicinal value of products Leverage feedback loops Risk Resilience BioPharma Procurement Inventory Manufacturing Transportation Evaluation of suppliers cost hist leadtime ot dist quality ProductsSoleAlt Risk profiles ScoreRank SDE Raw materialProducts GOLF SOS. Multisourcing Org risk Suggest alt suppliers. Scarce difficult easy Govt ordinal local foreign SeasonalOff seasonal Excess Inventory Cost of excess inventory Order volume Out of date stocks   LifetimeShelf lifeExpiry Inventory mismanagement Less inventory turnover Lack of  Order visibility InboundOutbound Unused wasted supplies Increase consumable inventory of vital materials Inventory and capacity buffers Diversification Nearshoring Spot the demand patterns for different products Usage of right packaging material Tamper resistant packaging Hazards Equipment damage  compression vibration climate temperature light moisture Contamination and fermentation risks Low quality packaging materials Leakage pinholes Proper sterilizing sealing labeling Shattering of glasses at ultracold temperatures Single use disposable packaging Nonsustainable Storage innovation and management Hygiene management Reusable packing materials Provide relevant product  information bar codes on packaging Product traceability Carrier reliability 3PLs Score Recent historical patterns Cost Load types droplive Carrier types ownedhiredcontract OT delivery cases delays Trip score op variables finance variables frequency driver details Simulation Mode of transport Target achieved or not Weather conditions Identify routes that have bottlenecks that result in delays Procurement Risks Supply disruptions Supplier issues Lead times Material defects Material quality Errors in ordering or procurement Sole sourcing of critical consumables Lack of substitutes alternatives ancillary supplies Diversity within suppliers Maintain supplier risk profiles Solutions Evaluation of suppliers cost hist leadtime ot dist quality ProductsSoleAlt Risk profiles ScoreRank For the Suppliers risk group its probability is low and its impact is high so it will have a high rating. Risk Priority SDE Raw materialProducts GOLF SOS. Multisourcing Reserved spare capacity Org risk Suggest alt suppliers. Scarce difficult easy Govt ordinal local foreign SeasonalOff seasonal 1. Eliminate internal risks 2. Focus on top 250 suppliers 3. Consider supplier location and country risks 4. Automate data capture to minimize manual efforts Transportation Risks Spoilage at Points of care Waiting times Logistics delays docking Routing issues driver shortages Delivery delays No alternate routes Product availability status in other DCs Temperature control Cold chain logistics   Thermal shippersultracold freezerGPSenabled thermal sensors Environmental changes Excess gas emission Central storage Exposure to light and air Build redundancy Preserving the medicinal value of products Leverage feedback loops Solutions Carrier reliability 3PLs Score Recent historical patterns Cost Load types droplive Carrier types ownedhiredcontract OT delivery cases delays Trip score op variables finance variables frequency driver details Simulation Mode of transport Target achieved or not truckcarrier Weather conditions Identify routes that have bottlenecks that result in delays route tradelane score Inventory Risks Excess Inventory Cost of excess inventory Order volume Out of date stocks   LifetimeShelf lifeExpiry Inventory mismanagement Less inventory turnover Lack of  Order visibility InboundOutbound Unused wasted supplies Increase consumable inventory of vital materials Solutions Inventory and capacity buffers Manufacturing Risks Over production under production Unplanned downtimes Usage of nonrenewable resources nonreusable Production scrap fifo Excess gas emission Capacity constraints Demand surges Supply and demand shocks Counterfeit products Power outages Product with single global manufacturing center Quality assurance challenges Fire life safety loss prevention Gene therapy Production scaling Good manufacturing practiceGMP Cleanroom production Solutions Diversification Nearshoring Spot the demand patterns for different products Solution construct Define data requirement input constraints Acquire required data build views Data transformation in required form for optimization model Products segmentation for direct store delivery to fulfill store orders from manufacturing plant Product with high in revenue with low demand variability and high RFM score or fastmoving can be qualify for Direct to store Delivery Develop mathematical and heuristics models for arriving at optimal replenishment plans At DC to fulfill nonDSD product orders and urgent DSD orders Quantity of DSD products to be stock at plant based on future orders and plant stock capacity Optimization of order allocation for urgent last minutes orders of DSD products from PlantDC and all nonDSD products from DC based on Current inv. Agelevels at DC Required delivery date Inventory levels at stores Order size Production Capacity Inventory stock at plant Graph databasebased integrated approach to visualize flow of goods with associated risks Product journey Tracking of live PO shipments Source to target Automated pipelines to update status of open shipments Strategic network Visualize aligned product flow movement across entities Creating graph database with entities nodes flow and properties such as volume delays etc. Risk integration Highlight risks associated with entities and flows Integrate predicted risk across network on top of traceability Traceability Predict probabilistic raw material supply at risksasset failure risks etc. Estimate dependencies of risks and fluctuations demand on production Predicting overall production at risk and pinpoint overall capacity bottlenecks AI based approach to predict risks across nodes Ris k prediction Assess alternative mitigation strategies and recommend optimal strategy Optimal strategy recommendation Utilize multiobjective optimization for identifying optimal mitigation strategy Mitigation alternatives Simulating impact of heuristicbased mitigation alternatives and assess tradeoffs System automation Automation to translate decision to actions on underlying systems Scenario plan 01 Inbound Risk Mitigation Inbound Risk Predict raw material risks at Manufacturing plants mitigate Probability of On time delivery Predicting on time delivery performance of POs Predicting potential delay in no. of days against each PO Risk predictions for raw materials Alternative strategies Mitigation plans for alternative strategies vendor inter plant movement etc. Holistic picture considering lead time cost and other performance metrics Predicting Raw Material Supply at Risk for next 13 weeks Assessing the Category wise production at risk Inbound Supplier risk prediction and raw material traceability for a large FB CPG Collaborative platform for inbound raw material visibility Early warning signals on risks Recommendations to mitigate risks Real Time Visibility Limited visibility of supply at risk Slow tradeoffs analysis for mitigation strategies Limited impact identification on Supply demand reconciliation SOURCE MAKE DELIVER 11 Product platforms varied recipe and suppliers 25 Plants across North America with copack and coman complexity 13 Distribution centres 42 Customer distribution centres Early warnings through risk prediction Impact of Risk across network Alternatives to mitigate risk 02 Production Outbound use cases Bringing resilience across verticals of supply chain Identification of risks associated in E2E supply chain from performance indicators point of view

Philips_SupplyChain_DeepDive (2).pptx

Philips Supply Chain June 2022 Fractal Team 01 02 03 Deep dive into supply chain use cases QA Alignment on next steps Agenda We understand Philips supply chain challenges and have capabilities to drive impact Improve OTIF by optimizing critical levers Mitigate OTIF risks through predictive Product Shortage Improve demand forecasting of parts and optimize inventory norms Uncleared supplier payments 90 Days is 5 times higher YOY 90M EURO increase in supply cost Q122 Philips parts inventory holding cost is at an all time high Adjusted EBITA dropped by 119M EURO Q122 Vs Q121 Average OTIF of 57 in H122 500M EURO revision to Philips FY22 sales target due to low salesunderfulfillment in Q421 Uncleared Supplier Payments GRIR goods receiptinvoice receipt clearing is a function that you execute in order to clear the Purchases in Transit and Unbilled Payables to the supplier when goods received and the associated invoices have been recorded in the system. 1 2 Improve Demand Forecasting of parts based on Consumption Criticality Demand patterns Optimize Inventory norms with improved forecast and supply reliability of parts Mitigate OTIF risks through predictive Product Shortage and integrated insights on Clear to Build Identify potential opportunities of OTIF improvements with current forecasting inventory norms Fractal proposes to focus on specific use cases based on what we observe in Philips business currently Our MVPs will deliver outsized results with clear recommendations and measures of success Decision cockpits with OTIF risks and alternatives powered through AIML algorithms Early warnings on OTIF risks Improved Fill rates PFR On time up to 8 10 Reduced penalties for missing commitments Demand forecast at plant part level short long term Decision cockpits with Inventory norms recommendations Reduced Inventory holding up to 40 Improved forecast accuracy by 15 20 at Plant part level Enablers Value Impact Invest for impact ..with a multidisciplinary team Decisionbackward approach vs dataforward warrants a crossfunctional team Fractal Team mix SAP experts data engineers Architects Supply Chain domain consultants Behavioural Science Design Thinking consultant Rigorous governance steering committee with committed Philips IT Supply Chain representative to enable actions W e propose an outcomebased MVP approach to improve Philips supply chain performance MVP prioritization grid Intersection of critical Health Systems products key markets and low OTIF performance Fractal investment Upfront effort cost for MVP solution development Outcome based billing Philips Supply Chain business stakeholder accept the MVPgenerated recommendations of specific use case Phase2 onwards Evaluate a gain sharing partnership Fractal receives a percentage of financial gain from supply chain improvement W e need access to supply chain data to deliver the MVPs Illustrative Create training models and the required infrastructure to develop the solution Source data collection from system of records Raw Data Ingestion into Azure Data Lake Store n ear real timebatch Data Transformations Harmonization and model building Storing model output in ADL Generate insights reports from modeled data on Azure Deployment of recommendations into existing systems Source Data Azure Data Bricks Azure Data Factory ADLGold 1 2 3 4 5 a b Selecting the best model c Placing the selected model in registry ADL Silver L1 L2 AZURE SYNAPSE Serverless SQL Pools Illustrative Reporting Layer Internal Systems A Predict OTIF risks High level solution approach for resilient material supply Integrate with CTB Project the product shortages to arrive at potential CTB at Finished goods Recommend best potential fulfilment date Recommendations on optimizing the components usage to maximize CTB of finished goods Predict Shortages Predict upcoming receipts quantity and ETA based on volatility in receipt quantity PO delays demand surge etc. Predict future shortages of parts Determine automated root causes for shortages Potential Fill rates Predict potential fill rates through Supply reliability Demand Mitigate the fill rate risks through optimal part allocation to CTB Philips example and initial findings Semi Finished Good 989603216821 SFG dS ANTERIOR 1.5T PN The above Semi Finished good has been included as it is a critical component to several finished goods such as Biopsy kits Radiofrequency coils etc. Much of the required component dS ANTERIOR 1.5T PN is not clear to build in upcoming 12 weeks due to material shortages 11 out of 35 raw materials 86 is the average OTIF for 9 vendors supplying material PO delay is prominent disruption Improved OnTime InFull delivery Improve on time in full delivery at customer level through prescribed delivery window and at full quantity ordered Monitor and Identify root causes of potential fill rate drops across 13weeks into future e2e automated integrated solution Application to monitor OTIF risk of shipments Predict fill rate and identify risks of maintaining target PFR based on inventory sales Orders Early warnings for fill rate drops highlight high risk priority orders Alternatives to mitigate the risks Improved service level by PFR 3pp and OTIF 8pp 25Mn increase in Gross Sales Value per BU Centralized collaboration with Single Source of Truth Client ask Solution Result B Forecasting Inventory optimization of parts Demand forecasting and inventory optimization of parts through a 3 step approach 2. Demand Forecasting Forecasting at FacilityPartSegment level to train risk elements which would otherwise be lost in generic model Forecasting of parts with factors like historical consumption average Life time avg. daily operations time 1. Parts Risk Classification Criticality score calculated at FacilityPart level based on frequency of usage rarity stability in supplier service levels etc. A second dimension is added to inventory segmentation based on the type of demand 3. Inventory Recommendations Predict Safety stock DFC basis demand supply volatility lead times EOQ at Facility levels Aggregate facility level recommendations to region level for calculating capex opex dollars Improved demand forecast of parts through criticality segmentation AIML interventions Criticality score of parts Segmentation of parts Demand Forecast of parts Dynamic Safety Stock through predictive algorithms Inventory Norms Outcome Recommended safety stock Dynamic calculation to enable early interventions Scenario planning Driver analysis for all components of supply and demand Supply disruptions Demand volatilities Supplier reliability Fulfilment rate Quality checks Leas time variance Supplier capacity Actual sales Demand forecast Promo events Returns exchange Similar products Calculate historical supply related KPI at required time granularity For demand factors calculate risk profiles to capture variance Leverage modelling techniques like Random forest SVM NN to predict safety stock Automated e2e forecasting inventory optimization for spare parts Forecast the consumption of parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process 2Mn savings across 30 of Spare parts accessories globally Huge potential savings with scale Client ask Solution Result Prioritization Matrix for MVP High OTIF performance P1 M3 P2 M5 P5 M4 Low P3 M2 Product criticality P4 M1 P1 to P6 Selected Product M1 to M6 Selected Market P6 M6 High Scope for MVP

Priority Fulfillment - Art of Possible.pptx

Improving Fulfillment for Priority Customers How do we improve the fulfillment of our priority customers Identify buying patterns of customers Tracking and analyzing the customer journey Affinity analysis to identify customer preferences and patterns Optimal inventory policies such as MinMaxFIFO orders Deriving rules for intercompany DCDC inventory transfer Choosing the optimal fulfillment strategy Using thirdparty fulfillment centers Virtual Warehousing Right choice of fulfillment center in real time Accurate forecasting across nodes Fulfilling priority customer demand for new product launches Dedicated fulfillment centers for high density areas Identify right inventory and product mix at all nodes Increase fulfillment efficiency at warehouses Regular updates in the ERP system about the product placement Streamlining Picking and Packing Operations Optimize the Warehouse Layout Identify priority customer segments based on customer demographics and behaviors Predicting Customer Lifetime Value through recency frequency and spend on orders Likeitem analysis to identify demand and buying patterns for new products based on similar existing products Optimize warehouse network based on factors such as Customer and Supplier base Proximity to Transportation Capacity and growth assumptions Choosing the best fulfillment location based on the stock status of nearby locations and the distance to deliver Improved warehouse design for better navigation and timely identifying product location Reducing walking distances through shortest path identification and product picking system Sorting and placing the products based on SKU velocity for faster order fulfillment Monitoring key Inventory KPIs to recommend optimal inventory and sourcing controls across various product categories and DCs Inventory recalibration at a fixed interval to reduce the imbalance in the inventory

PS Forecasting.pptx

Supply chain analytics capability 2022 Fractal Analytics Inc. All rights reserved Confidential Weve enabled transformation initiatives and bottomline improvements for our clients across the supply chain 1Bn savings through e2e inventory optimization 25Mn r eduction in transportation costs 52Mn savings through alternative formulations 1.3Bn reduction in working capital over 4 years 2Bn Identified new growth opportunities 3Mn Savings with reduced off spec 15 Reduction in capex through longterm capacity planning 1Mn savings per material due to optimized purchase EOQ Transforming Tech Supply Chain Art of possible End to end visibility across Product lifecycle Suppliers RD sites Fabrication centre Assembly sites Testing units WarehouseDistributors 3PL Retailers Enable Supply chain management hub The crossfunctional decisionmaking platform developed with implementation of decisions across multiple departments Predict risk probability for various subfunctions and their impact to overall value chain A set of TacticalStrategic levers could be deployed to manage the chip disruptionincreasing supply managing demand mitigating the impacts of the shortage improve On Time in Full OTIF performance Optimize inventory levels based on two critical constraints in semiconductor industry Shorter life span for rapid change in technology Longer lead time Avg 12 weeks Flexible target inventory buffer will be auto adjusted sensing the real time constraints Dynamic scheduling will sense internal uncertainties and external demand changes to output optimal scheduling decision. This will help to confirm and stay committed to the promised date Clear to build will provide the approximate fulfillment level based on the supplier reliability inbound uncertainties and other downstream volatility 02 01 03 04 Risk and Resilience Inventory Optimization Control Tower Smart planning Fulfillment Supply Chain is one of our most tenured practices at Fractal 70 member global team with relevant industry experience 300 person years Supply chain analytics consulting experience 25 APICS certified CPIM CSCP and OR professionals 90 Team certified on MITx supply chain Functional experience 50 Transformation programs Control towers for SOP E Warehouse Operations Logistics Inventory planning CPFR Manufacturing excellence Logistics cost optimization Sustainability analytics Analytics experience 80 of team with data science expertise on opensource platforms 100 of team experience on major visualization platforms 50 of team experienced on cloud deployment GCP AWS Azure Systems experience Data orchestration experience across SAP APO DP APO SNP BW TMS WMS SAP HANA JDA Oracle Historian JDA MES Prophecy Manhattan RedPrairie Experience of SAC Llamasoft Kinaxis Rulex Fractal featured as Specialist Providers in Market Guide for Supply Chain Strategy and Operations Consulting and Preferred Vendor in Gartners Hype Cycle on Supply Chain Strategy Business Process Services Gartner Hype cycle and Market guide 201820192020 2021 Specialist providers offering supply chain strategy operations business consulting with major focus Fractal is the only pureplay analytics organization amongst other SCM consulting IT firms Vendors for Supply chain management SCM business process as a service BPaaS 01 Play Station and accessories Demand Forecasting Demand planning is quite unique for PlayStation Emerging Consumer Trends Catering to rising demand while accommodating for global disruptions in supply chain Backward compatibility among console versions is a strong factor for buying decision among customers Uniqueness of PlayStation demand Demand depends on multiple factors like AAA release Sporting event etc. Simple Product mix but attaining right fit for customer is essential High Customer loyalty and need to upgrade to latest versions Conventional Challenges High seasonality due to surge in demand during the holiday season User behaviour could result in products having a short lifecycle High Inventory holding costs across multiechelon supply Network Our POV on utilizing multiple signals to create a hierarchical demand forecast Leveraging Python to transform multiple datasets in order to take care of missing values outlier handling and categorical data encoding Standardization and normalization of variables for use across multiple prediction algorithms Existing Product Line Terminations Input Standardized data models An ensemble of timeseries models ML algorithms to capture both internal external factors affecting demand SARIMAX for capturing demand correlations and seasonality Triple Smoothing to capture the trend and seasonality XG Boost Regression to capture the impact of external factors affecting demand Light GBM provides quicker implementations and captures the impact of categorical variables impacting demand Demand forecast prediction across the console controller product portfolio A PBI based visualization tool for decision making at different geographical granularities Multivariable simulation environment for analyzing impact of different scenarios on the demand forecast Solution highlights Model at Country level to capture region specific variables Standardized data models and pipelines to enable autoscaling Power BI based UI for analysis and simulations New Product Launches Country Specific Bonus Seasons Black Friday Sports Events Game centric releases Pricing Product features bundling Historical Sales Data Gaming Conventions Internal Events External Hierarchical modeling Decision cockpit 02 S pareparts and device planning 2.08M in savings by replace a manual planning process with an AIML driven methodology eventually leading to a fully automated procurement system Performing various statistical analysis and hypothesis Differential evolution optimization for SKU Positioning into quadrants to understand Past behaviors and global behaviors Decide modelling technique for Forecasting demand Replenishment parameters at SKU level Min Max ROP safety stock EOQ Dynamic recommendations on minmax inventory norms Estimating and highlighting emerging trends and disconnects. Simulation of inventory norms w.r.t service levels supply demand parameters Background Objective Solution framework approach AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process Outcomes Develop a Unique AIML model to dynamically understand temporal behavior of demand and consumption and optimization tool for recommending inventory levels for SKU managed globally Static min and max inventory norms irrespective of demand supply volatility Lack of scenario analysis to simulate inventory requirements service levels Differential evolution optimization Automated e2e forecasting inventory optimization for spare parts Forecast the consumption of Spare parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process 2Mn savings across 30 of Spare parts accessories globally Huge potential savings with scale Client ask Solution Result AIML driven methodology eventually leading to a fully automated procurement system Differential evolution optimization Performing various statistical analysis and hypothesis Differential evolution optimization for SKU Positioning into quadrants to understand Past behaviors and global behaviors Decide modelling technique for Forecasting demand Replenishment parameters at SKU level Min Max ROP safety stock EOQ Dynamic recommendations on minmax inventory norms Estimating and highlighting emerging trends and disconnects. Simulation of inventory norms w.r.t service levels supply demand parameters DL CNN LSTM ML Random forest Statistical A SRIMAX GARCH 02 Appendix Fractals vision of supply chain transformation Trifecta Predictive Risk Mitigation Digital enablement Control towers visibility to transform offline siloed decision making to online connected decision making Cross functional Insights and Early warning predictive alerts recommendations Self serve diagnostics with automated RCA Early warning nudges to proactively manage deviations Smart automation of Operations Predictive risk assessment across Entities Product flows in the Network Identify the bottlenecks for future n days Alternative options with Financial Service Lead times Scenario planning Intelligent decision support system Smart automation for translating decisions to actions on underlying systems Automating work flows for actions Enabling supply chain analytics products through Trifecta Predictive Risk Mitigation Digital enablement Logistics cost optimization Established drivers of Total Cost to Serve reduced latency in decision making Customer service levels single source of truth to visualize proactively alert service cuts risks Connected supply chain e2e visibility of SOE operations metrics associated risks Smart automation of Operations On Time Delivery Predict delivery risks with pickup schedules based on internal and external factors SKU Phase out planning Predict life cycle stage of product remaining life before delisting Inbound Risk Resilience Predict raw materials supplier risks provide alternative mitigation options Demand drivers forecasting improved forecasts enabling better promo supply planning Warehouse Command Center Synchronize warehouse operations enable decisions Supply Chain PL Automated Supply chain Cost allocation to minimize TCO Improve Customer Service levels Optimal working capital Inventory Manufacturing excellence through Smart factory Reduce bottom line costs Faster distribution logistics Digital supply chain transformation through AIML solutions On Time Delivery risks solution to predict OTD risks at shipment level and recommendations to mitigate Customer Service Level solution to determine root cases predict In full risk and recommendations to mitigate SafeStock solution predicts optimal Inventory norms End of Life solution determines life cycle stage of product remaining life before delisting OOS solution predicts OOS risks at retailer shelves Inventory Control tower enables cross functional insights Portfolio optimization Manufacturing visibility solution provides 360 insights across PQCDSM Predictive maintenance solutions for real time anomalies spare parts optimization Predictive Quality to model critical process parameters impacting the output grade of FG Cost to serve solutions to identify Cost drivers scenario planning Sourcing Optimizer solution to identify alternative sourcing choices formulations Blend optimizer for effective RM usage consumption Waste management solution to minimize Mfg Waste Dynamic order fulfilment solution to enable same day deliveries Warehouse Mission control to automate DC operations SOE solution to enable operational decisions of fulfilment quotas and priorities Sustainability solution provides insights on Carbon emissions Sustainability metrics across Manufacturing Logistics Sourcing Risk Resilience solution provides risk probability across Supply network nodes and recommends alternatives to mitigate

Pytest Framework.pptx

PYTEST FRAMEWORK Harini Jeyapal July 2022 Agenda Introduction What is Pytest Run only certain testcases Pytest allows us to run a subset of the entire test suite. Open source Pytest is free and open source. Simple syntax Because of its simple syntax pytest is very easy to start with. Tests in parallel Pytest can run multiple tests in parallel which reduces the execution time of the test suite. Detects Test files Pytest has its own way to detect the test file and test functions automatically if not mentioned explicitly. Skip certain testcases Pytest allows us to skip a subset of the tests during execution. Why and where is Pytest used Pytest is mainly used for API testing even though we can use pytest to write simple to complex tests i.e. we can write codes to test API database UI etc. Pytest installation and commands Python installation Pip install pytest import pytest Pytest requires the test function names to start with  test AES General functions single file substring Grouping testcases Marker Fixtures Inputs conftest Parameterize 1. Lag feature function Working examples i General iiAES General Advance Pytest concepts and commands Commands

Retail - Inventory - Control tower.pptx

Inventory Control tower 27 th Sep 2022 Maximize value Monitor risk Drive agility Retailers can better manage inventory risk with advanced digital capabilities for longterm supply chain success... Capture variability in demand and supply signals early to enable speed to insights Monitor supplier and category performance through scaled common automated platforms Develop line of sight to future inventory risks from open POs contracts vs. Demand signals Optimally allocate inventory across the network Advance markdown management Dynamic inventory norms based on demand signals Identify end of life indicators early Develop advanced scenario planning capabilities to assess risks quickly and compare mitigation strategies Facilitate cross functional collaboration by accelerating adoption of advanced digital tools End to end visibility and actionable insights Shape demand and allocate optimally Improve agility in decision making Inventory Control tower Visualize current inventory across SKUs brands categories inventory segments regions DCs Monitor KPIs Current Minmax Days forward coverage stock OH overstock outofstock inventory turns. Identify opportunity areas for improvement across SKUs DCs segments Predict inventory risks OOS risk identification at SKUDC level and impact assessment on cost and CFR Prediction of Excess Inventory cost impact Codify business rules thresholds for alertsrisks Highlight inventory risks due to product obsolescence SLOB inventory Inventory minmax norms Generate optimal recommendations for minmax stock at each granularity Generate stock projections into 12 weeks horizon Trigger automated alerts for current week future 2 weeks horizon MEIO simulation Simulation of inventory requirements basis CFR demand supply lead time etc. Incorporating constraints like MOQ Customer Service level DC Capacity Holding cost to optimize MEI Recommendations on inventory allocations based on Case fill Promotion demand factors through AI enabled approach for e2e inventory optimization Position this slide as Inventory Control tower with the following components Near real time Visibility Early warnings predictive alerts AIML interventions to predict the Inventory settings dynamically Prescriptive ways to mitigate the risks excess out of stock stock xfers Simulations Scenario plan across multi echelon network Smart automation ALL OF THE ABOVE WITH RETAIL INDUSTRY CONTEXT Digital enablement through a feature rich consumption layer Control tower Nudges Early warnings for supply and operational disruptions Recommendations for course correction End to end synchronization Correlate interdependency of processes Single pilot to auto pilot control tower Visibility Connected realtime visibility and insights Single unified source of truth for communication Collaboration Internal and external collaboration Instant feedback mechanism AI models for predictions Intelligent retail allocation Optimal inventory levels Security Governance Role and functions based access Secured data maintenance Deep Dives Deep dives into Siloed functions Performance and variance analysis Decision Support Smart automation Intelligent recommender systems Near real time visibility Deep dive RCA Supply risk Enable product traceability with e2e visibility of orders Identify potential pain points with predictive alerts and early warnings Inventory placement and mix Dynamic fulfilment Balance load on stores and DC for ship from store Vs. DC decisions Predict fulfilment lead time in eCommerce Predictive and prescriptive capabilities for retail supply chain Art of Possible DC operations Combine AI with business rules to optimize operations Automate workflows for decision making Transportation Identify drivers of cost and on time delivery risk through predictive models and identify opportunities for process improvement e.g. Transportation costs customer service Optimal inventory mix in DCs and stores Automated replenishment of stores from DCs Out of stock Predict out of stock at item store level Analyze category performance and automate mitigation response Predictive Prescriptive Scenario planning wrt Lead times Supply Reliabiilty Inb Mfg . Progress each priority use case through analytics continuum to drive ROI Transform data to decisions AI based predictions of out of stock DC fill rates and eCommerce lead times Recommendations on optimal store level MinMax inventory levels retail allocations Decisions to actions Translate decisions to record and perform actions on system of records Closed loop feedback on actions performed Action tracking monitoring Intelligent Decision support Enable decisions for user personas Heuristic based automations and workflows Automate replenishment fulfilment and allocation decisions Smart automation Working Slides below Prescriptive AIML interventions to predict the Inventory settings dynamically AIML techniques have emerged as critical enablers of the 3Ws and provide cognitive capabilities to control towers What happened What is likely to happen What actions need to be taken Prescriptive ways to mitigate the risks excess out of stock stock buffers Digital Enabler Upend traditional operating model with a  digital operating model  to quickly analyze optimize and evaluate complex decisions before taking action Near real time Visibility Support in sensing demand fluctuations as well as supply disruptions in real time translating that into various risks the risk of poor customer service or the risk of lower revenues or the risk of lower margins due to expensive course corrections. Early warnings predictive alerts Diagnose the root causes of supply chain failures and prescribe corrective actions along with their costs and tradeoffs. Smart Automation Simulations Scenario plan across multi echelon network Smart automation Actionable workflows can be customized to meet unique requirements and process steps required to automate actions within source transactional systems Inventory Control Tower Retail KPIs Grocery KPIs Grocery Reduce OOS and reduce excess Deep Dive RCA Real Time Visibility on Average Days of Supply Excess Days of Supply Expiring Inventory Inventory approaching out of stock Inventory with excess quantity Frozendamaged inventory Obsolete Inventory Prescriptive Prescribe Mitigation actions What actions need to be taken AIML can provide Prescriptive recommendations with an action or a sequence of actions to various roles in the supply chain so that fulfillment execution manufacturing or properly changed plans are ensured. Predictive Predict OOS Excess Transition to End of Life NPI Slow Moving Phantom Obsolete.. What more in terms of retails KPIs derived What is likely to happen The Predictive capabilities of ML are ideal in understanding the effects of forwardlooking drivers of demand to forecast a demand surge at a specific region perhaps due to local events social media buzz unusual weather or competitor supply disruptions. Scenario Planning wrt Impact due to changes in Lead times Supply Reliability Inbound Manufacturing. Solving for each NPI type Illustrative Workflow to identify NPI type Recommendations for each NPI type based on business rules Smart Automation Reference Inventory Management KPIs Tracking What Matters Most shipbob.com httpswww.ibm.cominentopicscontroltowers httpswww.ibm.comwatsonsupplychainresourcessmartersupplychaincontroltower httpswww.ibm.comcaenproductssupplychainintelligencesuitecontroltower httpswww.ibm.comdownloadscas4VZNOLPP httpso9solutions.comtrendingnextgencontroltowerandsoe httpswww.ibm.comdocsencontroltowertopicconceptsinventorydashboard What Your Organization Can Do to Stay Ahead Monitoring right classification of inventory Identifying root causes drivers for nonproductive inventory Predictive prescriptive recommendations for NPI Report inventory reduction due to reduction in NPI The Challenges Behind NPI Nonproductive inventory phantom expired damaged frozen and excess inventories takes precious depot space Lack of visibility of nonproductive inventory regarding regions plants and categories Same stock coverage or safety stock on all product Achieving inventory goals Right stock level for each product group Waste elimination with improvement in margin Lower levels of donate and destroy Approach Challenges Impact Is there any opportunity to mitigate nonperforming inventory NPI Mapping current inventory profile High level solution approach to mitigate NPI Dashboard with warning system to flag inventory by age and endoflife Early Warning signals for Products that will accumulate NPI in near future Mapping out transition probability versus demand Recommend actions for NPI and potential NPI Evaluate inventory category Inventory Analysis by Region plant category shelf life to understand spread of NPI across Supply chain Identify similar historical occurrences using product life cycle analysis Clustering of SKUs using various attributes value volume shelflife Rule based engine to identify NPI Automated root cause analysis for nonperforming inventory Calculate probability of NPI for SKU utilizing historical trends Inventory Analysis Root Cause Analysis Mitigation Strategies Understand drivers of NPI Recommendation

Risk & Resilience V0.2.pptx

0 Introduction Responding quickly to unforeseen situations to avoid lost opportunities Risk mitigation and alternative planning Near real time visibility across global supply chain nodes Volatility and disruption limited risk assessment Traceability of bottlenecks to take preventive actions Category leaders are moving towards demand driven supply chain and planning strategic investments Assessing the impact of future growthdecline across supply chain nodes How Might We improve endtoend experience of our users for strategic tactical supply chain resilience planning via proactive holistic decision making to ensure business continuity Supply chains today are more global interconnected via multiple entities and product flows Logistics Plants to DCCustomers Logistics Supplier to plants Logistics DC to Customers Maintaining quality and proper standards 360 o risks penetration across the product flow Planning executional pillars to optimal resilience mapping Risk Resilience Mapping By deploying advanced solutions such as digital twins companies can significantly improve their ability to address crises as well as cope with daytoday volatility To promote resilience companies needs to develop an effective multiechelon inventory strategy which typically leads to new inventory targets in the supply chains highvolatility nodes Gain endtoend transparency and enable collaboration including outside their four walls. Resilient network for supply manufacturing and distribution achieves flexibility through selective applications of redundancy such as dual sourcing nearshoring Assess the criticality of suppliers vendors and adjust relationships to ensure resource availability. Gain transparency into multiple tiers to fully assess upstream risks Managing the Multienterprise Supply Chain. Redesigning the Global Network Proactively Managing Suppliers and 3 rd party vendors Planning Based on Anticipation Simulation and Scenarios Actively Managing EndtoEnd Risk Setting New Parameters for Supply Chain Buffers AGILITY SUSTAINABILITY RELIABILITY TRACEBAILITY Riskfocused analytics engines powered by AI to use the intelligence generated to get a head start on designing and prioritizing mitigation actions. Production Resilience Inventory Resilience Inbound Resilience Delivery Resilience FIREFIGHTING INTEGRATING STREAMLINING OPERATIONS ACHIEVING STRUCTURAL RESILIENCE Stabilize immediate disruptions while building resilience against future ones Exceeding Risk Appetite Within Risk Appetite PRIORITY Risk Resilience Mapping Resilient Planning Strategic Tactical Responsive  Execution Collaborate Integrate Streamline Compliance to schedule Schedule Capacity Utilization Distribution Logistics Fulfillment Revaluating Inventory Strategies Excess vs OutofStock Buffer capacity Prioritize by importance to the business vulnerability. Setting right control parameters Justintime vs Justincase Simulating the effects of regional demand shifts Set up controls to minimize effects Using  buffers in the form of surge capacity Anticipatory Strategic stock for surge needs Improve docktostock stocktodelivery cycle times Alternative RoutesCarriersTargets Ontime Infull accuracy Maximizing Perfect order while reducing total order cycle time Network diversification Nearshoring Optimizing distribution as of Sales units shipped Supplier uncertainty Product  Flow Structural Inbound Supply plan maximum agility Transparency Raw Material multisource multi design Strategy Geospatial freight distribution flexibility outlay Traceability of bottlenecks to take preventive actions Vendor service volatility and disruption planning Enabling Multifacets risk resilience A Supply chain 360 0 Resilience Enterprisewide data assets Supply chain 360 Demand planning platform MES RD utilization Customer operations Production planning Central decision support system for proactive risk management Master data assets MRP RP inventory WMS TMS OMS ATP Sellin Sellout ARIBA Coupa Supply Planning platforms Supply planner Supply Chain Director Global SOP team Demand planner Production Capacity planner Customer service Stock controller Democratized data assets EndtoEnd network mapping Anticipating risks and actions Growth drivers and their impact Agile Response to disruptions Resolve bottlenecks Long range scenario planning Humanized design driving decisions Collaboration Elevated UI experience Simple and Effective visual telling Utility focussed design First Tier Supplier First Tier Customer Second Tier Customer Second Tier Supplier Supply Side Demand Side Customer End Striking demandsupply side optimal balance to manage resilience Supply side inbound risk challenges opportunities A.1 Supplier Uncertainty Schedule overruns Tasks omitted from Schedule Opportunity to compress Schedule Time Cost Budget Exceeded Unanticipated Expenditure Resources Team is underresourced Materials shortage Machinery unavailable Industrial Action Skills gap Environmental Bad weather results in rework Weather delays progress Adverse effects occur Environmental approvals not complied with Scope Scope creep Scope poorly defined Project changes poorly managed Communication Poor communication Stakeholder dissatisfaction Positive timely communications positive publicity Supplier Risks Supplier uncertainty risks Overall strategy Risk A Risk B Risk C Risk D Requirements Advantages Impact Requirements advantages Describe the requirements advantages and impact of each type of supplier risk described in previous slide Below are the six main steps for us to implement our supply chain management goals this process begins with opportunity assessment and ends in the realization of our supply chain management visions. Can be Used to describe the implantation strategy linear single objective Implementation planframework Graph databasebased integrated approach to visualize flow of goods with associated risks Product journey Tracking of live PO shipments Source to target Automated pipelines to update status of open shipments Strategic network Visualize aligned product flow movement across entities Creating graph database with entities nodes flow and properties such as volume delays etc. Risk integration Highlight risks associated with entities and flows Integrate predicted risk across network on top of traceability Traceability Assess alternative mitigation strategies and recommend optimal strategy Optimal strategy recommendation Utilize multiobjective optimization for identifying optimal mitigation strategy Mitigation alternatives Simulating impact of heuristicbased mitigation alternatives and assess tradeoffs System automation Automation to translate decision to actions on underlying systems Scenario plan How can analytics help in traceability through blockchain Farmers Consolidators i b Logistics Storage ob logistics Contracts Supply chain finance End customers Blockchain Product traceability Supplier risk identification Real time bottlenecks Product quality and recall Product safety Sales and finance tracking Traceability of products from Farm to Table Visibility and integrity in e2e supply chain Tracking all historical transaction Track supplier reliability on the go Create risk profile for supplier based on historical transactions Real time bottleneck alerts in the supply chain Bottleneck mitigation due to instant communication of the root cause Track product quality at each stage of product movements Effectively manage product recall Top quality be maintained during processing Identify product leakage Reduce raw materials and finished goods wastage Integrate with smart contracts to automate payments when certain milestones Automated sales tracking and rebate to distributors Predict probabilistic raw material supply at risksasset failure risks etc. Estimate dependencies of risks and fluctuations demand on production Predicting overall production at risk and pinpoint overall capacity bottlenecks AI based approach to predict risks across nodes Ris k prediction A.2 Product Flow Disruption Schedule overruns Tasks omitted from Schedule Opportunity to compress Schedule Time Cost Budget Exceeded Unanticipated Expenditure Resources Team is underresourced Materials shortage Machinery unavailable Industrial Action Skills gap Environmental Bad weather results in rework Weather delays progress Adverse effects occur Environmental approvals not complied with Scope Scope creep Scope poorly defined Project changes poorly managed Communication Poor communication Stakeholder dissatisfaction Positive timely communications positive publicity Supplier Risks Supplier uncertainty risks Overall strategy Risk A Risk B Risk C Risk D Requirements Advantages Impact Requirements advantages Describe the requirements advantages and impact of each type of supplier risk described in previous slide Below are the six main steps for us to implement our supply chain management goals this process begins with opportunity assessment and ends in the realization of our supply chain management visions. Can be Used to describe the implantation strategy linear single objective Implementation planframework Graph databasebased integrated approach to visualize flow of goods with associated risks Product journey Tracking of live PO shipments Source to target Automated pipelines to update status of open shipments Strategic network Visualize aligned product flow movement across entities Creating graph database with entities nodes flow and properties such as volume delays etc. Risk integration Highlight risks associated with entities and flows Integrate predicted risk across network on top of traceability Traceability Assess alternative mitigation strategies and recommend optimal strategy Optimal strategy recommendation Utilize multiobjective optimization for identifying optimal mitigation strategy Mitigation alternatives Simulating impact of heuristicbased mitigation alternatives and assess tradeoffs System automation Automation to translate decision to actions on underlying systems Scenario plan How can analytics help in traceability through blockchain Farmers Consolidators i b Logistics Storage ob logistics Contracts Supply chain finance End customers Blockchain Product traceability Supplier risk identification Real time bottlenecks Product quality and recall Product safety Sales and finance tracking Traceability of products from Farm to Table Visibility and integrity in e2e supply chain Tracking all historical transaction Track supplier reliability on the go Create risk profile for supplier based on historical transactions Real time bottleneck alerts in the supply chain Bottleneck mitigation due to instant communication of the root cause Track product quality at each stage of product movements Effectively manage product recall Top quality be maintained during processing Identify product leakage Reduce raw materials and finished goods wastage Integrate with smart contracts to automate payments when certain milestones Automated sales tracking and rebate to distributors Predict probabilistic raw material supply at risksasset failure risks etc. Estimate dependencies of risks and fluctuations demand on production Predicting overall production at risk and pinpoint overall capacity bottlenecks AI based approach to predict risks across nodes Ris k prediction A.3 Structural Inbound Schedule overruns Tasks omitted from Schedule Opportunity to compress Schedule Time Cost Budget Exceeded Unanticipated Expenditure Resources Team is underresourced Materials shortage Machinery unavailable Industrial Action Skills gap Environmental Bad weather results in rework Weather delays progress Adverse effects occur Environmental approvals not complied with Scope Scope creep Scope poorly defined Project changes poorly managed Communication Poor communication Stakeholder dissatisfaction Positive timely communications positive publicity Supplier Risks Supplier uncertainty risks Overall strategy Risk A Risk B Risk C Risk D Requirements Advantages Impact Requirements advantages Describe the requirements advantages and impact of each type of supplier risk described in previous slide Below are the six main steps for us to implement our supply chain management goals this process begins with opportunity assessment and ends in the realization of our supply chain management visions. Can be Used to describe the implantation strategy linear single objective Implementation planframework Graph databasebased integrated approach to visualize flow of goods with associated risks Product journey Tracking of live PO shipments Source to target Automated pipelines to update status of open shipments Strategic network Visualize aligned product flow movement across entities Creating graph database with entities nodes flow and properties such as volume delays etc. Risk integration Highlight risks associated with entities and flows Integrate predicted risk across network on top of traceability Traceability Assess alternative mitigation strategies and recommend optimal strategy Optimal strategy recommendation Utilize multiobjective optimization for identifying optimal mitigation strategy Mitigation alternatives Simulating impact of heuristicbased mitigation alternatives and assess tradeoffs System automation Automation to translate decision to actions on underlying systems Scenario plan How can analytics help in traceability through blockchain Farmers Consolidators i b Logistics Storage ob logistics Contracts Supply chain finance End customers Blockchain Product traceability Supplier risk identification Real time bottlenecks Product quality and recall Product safety Sales and finance tracking Traceability of products from Farm to Table Visibility and integrity in e2e supply chain Tracking all historical transaction Track supplier reliability on the go Create risk profile for supplier based on historical transactions Real time bottleneck alerts in the supply chain Bottleneck mitigation due to instant communication of the root cause Track product quality at each stage of product movements Effectively manage product recall Top quality be maintained during processing Identify product leakage Reduce raw materials and finished goods wastage Integrate with smart contracts to automate payments when certain milestones Automated sales tracking and rebate to distributors Predict probabilistic raw material supply at risksasset failure risks etc. Estimate dependencies of risks and fluctuations demand on production Predicting overall production at risk and pinpoint overall capacity bottlenecks AI based approach to predict risks across nodes Ris k prediction A.4 Supplier side risks mockups Supply Chain Performance Global Financial Performance Inventory Results Demand and Sales Forecasting Supplier Compliance Statistics Rate of Contract Compliance By Supplier Category Average Procurement Cycle Time In Days Avg. Procurement Cycle Supplier Classification 3233789 Total Spending 95367 Savings 55354 Foregone Savings Share of manged suppliers by category Supplier Category 650 Category 1 20 Category 2 100 Category 3 25 Category 4 30 Category 5 35 Category 6 Order Placement 0.6d Confirmation Delivery Invoicing 4.6d 1.6d Transportation Execution Location Tracing Shipment Details 192 Facebook 265 Twitter 410 LinkedIn Advertising Budget Marketing Dashboard Social Media Budget Banners Per Month Social Media Per Month Brochures Per Month Marketing Overview Dashboard Brand Preference Dashboard Brand Customers 96363.00 09232020 035225 30K Sell Traffic 96363.00 09232020 035225 21.5K Social Subscribers 96363.00 09232020 035225 30K Social Followers 96363.00 09232020 035225 30K Email Marketing 96363.00 09232020 035225 21.5K User Sign Ups 96363.00 09232020 035225 30K Payment Processed Balance Insights Dashboard Payment Balance in millions 4.6 M 5.2 M Remaining Balance Q1 2018 Q2 2018 Q3 2018 Q4 2018 Q1 2019 Q2 2019 Q3 2019 Q4 2019 Cash Conversation Cycle in Last Years Q1 2020 85 Balance Insights Dashboard 3.2M Visa Card Balance 500000 Mastercard Balance 653000 PayPal Card Balance 1000000000 Gross Profit Insight Dashboard Dec Gross Profit Insight Dashboard Product Financial Insights Dashboard Revenue 2018 Product Risk Global Financial Performance Global Financial performance Team Members 125 Tasks 180 Risks 27 Open issues 25 Project Dashboard Budget 6.5M Project 4 Project Dashboard Project ABC Summary Below is a summary of our Project ABC including status track risk analysis and resources used and overall completed 45 Status Track 20 Risk Analysis 90 Resources Project DEF Summary Below is a summary of our Project ABC including status track risk analysis and resources used and overall completed 45 Status Track 20 Risk Analysis 90 Resources Project KLP Summary Below is a summary of our Project ABC including status track risk analysis and resources used and overall completed 45 Status Track 20 Risk Analysis 90 Resources Project Dashboard Project ABC On Track Summary Below is a summary of our Project ABC including percentage complete budget used so far and open issues to resolve. 45 Task Pending 05 Issues Raised 10 Estimate Month 38 Resources Project DEF On Track Summary Below is a summary of our Project DEF including percentage complete budget used so far and open issues to resolve. 45 Task Pending 05 Issues Raised 10 Estimate Month 38 Resources Project GHI On Track Summary Below is a summary of our Project GHI including percentage complete budget used so far and open issues to resolve. 45 Task Pending 05 Issues Raised 10 Estimate Month 38 Resources Project JKL On Track Summary Below is a summary of our Project JKL including percentage complete budget used so far and open issues to resolve. 45 Task Pending 05 Issues Raised 10 Estimate Month 38 Resources Financial Profit Loss KPI Dashboard Customer Retail Sales KPI Dashboard Industry Revenue Generation Revenue Segmentation Revenue Generated by Market 10K 20K 25K 45K 50K 100K 110K 200K Segment Opportunity Details s Sales Individual Performance KPI Dashboard Contract by Status 39 Contract by Category 11 Contract by Organization Unit 17 Sum of Costs Revenues Contracts to Review Contracts without milestones Contracts with milestones Sales Growth Profit Margin KPI Dashboard Accumulated revenue 12 months Target Sales Growth 13 110 Customer Lifetime Value 200 Customer Acquisition Cost 250 Total Generated Cost Project Development Task KPI Dashboard Project Manager Task KPI Dashboard Scrum with team members Triaged Tasks KPI Dashboard Task Assigned to Priority Status Complete Net Promoter Score Promoter Detractors 48 Customer Retention Customer Effort Score Customer Satisfaction KPI Dashboard Customer Service Call KPI Dashboard Helpdesk Support Ticket KPI Dashboard CUSTOMER EFFORT 20 00 10 CUSTOMER SATISFICATION DASHBOARD CUSTOMER RETENTION 20 0 100 91 1.7 MONTHLY RECURRING REVENUE MRR 35000 Total MRR 15000 Expansion MRR 25000 Net New MRR 45000 Churned MRR SALES REVENUE BY PRODUCTS MRR NET GROWTH last 12 months MRR Growth FINANCIAL PERFORMANCE KPI Profit Margin Comparison Earning before Interest Taxes FINANCIAL OVERVIEW 66 Gross Profit Margin 70 Opex Ratio 30 EBIT Margin 25 Net Profit Margin CASH MANAGEMENT DASHBOARD Days Sales Outstanding Days Inventory Outstanding Days Payable Outstanding CMO DASHBOARD B Production Resilience B Risk templates slides Our Logistics Process Can be Used to describe the planning risks process and steps Supply Chain Roadmap Order Management Lifecycle Request Authorization Confirm Availability Send to Fulfillment Update Status Ensure Payment Create Invoice Place Order Confirm Order Confirm Shipment Receive Order Fraud Check Authorized Payment Receive in Fulfillment Ship Order Capture Payment Update ERP Pick Pack Shopper Experience Order Management Other Systems Order Capture Order Fulfillments Order Delivery Can be Used to describe the lifecycle Supply Chain Operators The main processes are carried out by those involved in the supply chain Can be Used to describe the various roles and ownerships Grow    Shrink tradeoff Phase I QualitySensitive Identify Codify rules by forming hypotheses from data testing significance and aligning on customer behavior with functional team. Phase II ModelSensitive Set up an AIML engine to automate and identify motifs changepoints Correlations Beta Rsquared anomalies efficiently . Learn behavior Codify feedback Implement a workflow to track steps and flag any anomaly in the order journey Learn behaviors by observing false positives and false negatives over time. And better the model to pick up newer patterns Set up a feedback loop to balance model and quality sensitive to codify rules as we discover them Learn newer sophisticated rules and feed them back to phase I to self supervised learn along time Can be Used to describe the resilience strategy multi objective Frameworks KAIZEN 4M CHECKLIST Frameworks SIX SIGMA DMAIC DEFINE Define the problem and what customer requires Select Project CTOs Create Project Charter Develop Highlevel process map Measure Defects and Process Operation Identify project output metric Develop data collection plan Establish process baseline Analyze the Data and Discover Causes of Defects Identify root causes Validate root causes and determine VITALFEW Quantify the opportunity Improve the process to remove causes of defects Identify solution Refine and test solutions Cost benefit calculation Control and monitor your improvement Implement process control Prepare rollout solution Project closure MEASURE ANALYZE IMPROVE CONTROL Project Initiation document and project selection Frameworks DRUMROPEBUFFER Rope is a signal from a constraint drum indicating the amount of materials to be released ROPE Drum determine s the total throughput of the entire system. DRUM Buffer Ensures a constraint for continuous operation BUFFER Workstation A Workstation B Workstation C CONSTRAINT Raw Materials 500 Units 600 Units WorkInProcess 400 units Finish Good Ship Strategic Demand Shortfall Customer retention Integration problems Pricing pressure Industry downturn JV or partner losses Operational Cost Overrun Operational Controls Capacity management Supply Chain Issues Employee Issues incl. fraud Bribery and Corruption Commodity prices Hazard Macroeconomic Political Issues Legal Issues Terrorism Natural disasters Financial Debt and interest rates Financial management Asset losses Goodwill and amortization Accounting problems Types of Risks Identification of Risk Categories Likelihood Impact Within Risk Appetite Exceeding Risk Appetite Obtain an estimate of the risk appetite of the shareholders with the help of the below bar graph. This will help in assessing the acceptable risk level Risk Appetite Risk Tolerance Risk Register Risk Identification Schedule overruns Tasks omitted from Schedule Opportunity to compress Schedule Time Cost Budget Exceeded Unanticipated Expenditure Resources Team is underresourced Materials shortage Machinery unavailable Industrial Action Skills gap Environmental Bad weather results in rework Weather delays progress Adverse effects occur Environmental approvals not complied with Scope Scope creep Scope poorly defined Project changes poorly managed Communication Poor communication Stakeholder dissatisfaction Positive timely communications positive publicity Identify Risks Risk Impact Probability Analysis Risk Impact Probability Analysis Manageable by exchange against Internal budgets Increases threaten viability of project Require some additional funding from Institution Requires Significant additional funding from Institution Requires Significant reallocation of Institutional funds or borrowing Slight slippage against internal targets Delay jeopardizes viability of project Slight slippage against key milestones or published targets Delay affects key stakeholders loss of confidence in the project Failure to meet key deadlines in relation to academic year or strategic plan Slight reduction in qualityscope no overall impact Project outcomes effectively unusable Failure to include certain nice to have elements Significant elements of scope for functionality will be unavailable. Failure to meet the needs of a large proportion of stakeholders Cost Very Low Low Medium High Very High Time Quality Detectability is very high Considerable warning of failure before occurrence Some warning of failure before occurrence Little warning of failure before occurrence Detectability is effectively zero No. direct effect on operating service level Minor deterioration in operating service level Definite reduction in operating service level Source deterioration in operating service level Operating service level approaches zero E. Probability of once in many years D. Probability of once in many operating months C. Probability of once in some operating weeks B. Probability of weekly occurrence A. Probability of daily occurrence Risk Assessment Major uncertainties remain No or little prior experience or data Infrastructure andor resources not in place Some uncertainties remain Some experience and data exist Infrastructure in place but underresourced Few uncertainties remain Significant experience and data exist Infrastructure in place and fully Probability Performance quality cost or safety impacts resulting in major redesign and program delay Performance quality cost andor safety impacts resulting in minor redesign and schedule adjustment Performance quality cost and safety requirements met within planned schedule Impact Risk Map High 5 Medium 3 Low 1 Low Med High Critical No concern Proceed w caution Significant risk Show stopper RISK SCORING SYSTEM Risk Assessment Cont. Insignificant Minor Moderate Major Catastrophic C Other templates Responsive Resilient Frameworks Risk Resilience Balancing the longerterm threat to demand and criticalmaterials volume Providing operators or machines the ability to detect when an abnormal condition has occurred and immediately stop work. Leveling the type and quantity of production over a fixed period of time The right products in the right quantity and at the right time The available production time divided by customer requirement A method of production control where downstream activities signal their needs to upstream processes Continuous improvement of an entire value stream or an individual process to create more value with less waste Managing the Multienterprise Supply Chain. Redesigning the Global Network Proactively Managing Suppliers and 3 rd party vendors Planning Based on Anticipation Simulation and Scenarios Actively Managing EndtoEnd Risk Setting New Parameters for Supply Chain Buffers AGILITY SUSTAINABILITY RELIABILITY TRACEBAILITY BUYER VALUES Large enterprise 1000 users Midsized organization 1001000 users Startups and individuals 100 users MORE VALUE LESS VALUE COMPETITIVE MATRIX Low High High End Mid Range Low End Company A Company B Company C Company D COMPANY B COMPANY C COMPANY D COMPANY A PRODUCT PORTFOLIO STRATEGY HIGH VOLUME MID VOLUME LOW VOLUME The new product expansion strategy will enable us to achieve a CAGR of 56.3 in sales revenue. The total sales of US 43M sales in year 5 will be made of approximately 30M from high end products and about 13M from new products. REVENUE VS. MARKET SIZE OVERALL EXISTING PRODUCT NEW PRODUCT D Dependency templates Jonathan Doe CEO Natasha Smith CXO Name Here Position Name Here Position Name Here Position Name Here Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position Name Here Position Name Here Position Name Here Position Name Here Position Name Here Position Name Here Position Name Here Position Name Position Name Position Name Position Name Position Name Position Name Position Name Position CROSSFUNCTIONAL MATRIX Requirements Analysis Systems Engineering RD Team RD Team System Verification Organization Engineering Research Testing Testing Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5 Iteration 6 Team A Team H Team G Team F Team E Team D Team C Team B AGILE TEAM DEPENDENCY RESPONSIBILITY MATRIX E Mockups for solution and dashboard

Risk and resilience V1 12th Aug.pptx

Risk and Resilience Supply Chain August 2022 Enabling resilient supply chains through structured approach PRIORITY Risk Resilience Mapping Resilient Planning Strategic Tactical Responsive  Execution Collaborate Integrate Streamline OTIF deliveries Allocation optimization Network planning Supplier uncertainty Product  Flow Structural Inbound Downtimes Material availability Production plan Operational Tactical Strategic How might we predict risks mitigate product flow across the network from sourcing lens How to monitor supplier performance reliability tracking ontime infull receipts How to manage inbound Supply chain nodes to ensure right product at right place right time How to maximize equipment utilization and optimize production schedule to meet demand surges How to minimize unplanned downtimes via predictive maintenance How to plan for material availability basis demand volatility and projected production plan What should my target stock levels be based on long term demand trends What is my current inventory profile and how do I mitigate NPI What is my OOS risk based on supply and demand volatilities and how to mitigate What is the optimal distribution network to minimize costs and maximize fulfilment How to continuously monitor outbound shipments and enable early warnings for interventions How to find the optimal fulfilment center allocation for orders basis supply and demand constraints Inventory settings Safety stock Strategic build up Informed decision making through early warnings and recommendations Analyze End to end visibility into key supply chain KPIs with trend analysis Alert Generate early warnings and alerts for humans to inspect upon and providing basis for understanding and validation .  Recommend Reacting to emergencies and reviewing outcomes to improve continuously . Better managing the network  controlling for downstream risks and exceptions. Risk priority flagging Decision support Involves understanding the likelihood and priority of risk. Assessing the risk severity to gauge the nature of impact in terms of time volume on supply chain. Decision augmentation Right actions at the Right time. Take decisions leveraging predictions optimization rules workflows and processes. Decision automation Decision based on whatif scenario planning utilizing machine compute and help to evaluate cost benefit analysis of alternative choices Risk deescalation through traceability risk prediction and mitigation Traceability Sustainability Product traceability Enhanced E2E visibility to smoothen collaboration across nodes and post event analysis Risk prediction Alternative strategies Risk alerts Pinpointing upcoming failures in advance with mitigation assistance Scenario planning Scenario comparison Whatif simulations Cloud based planning capability to assess impact of various parameters and tradeoffs among scenarios A Inbound risk and resilience Business questions across Inbound External Supply 0 1 Operational risk What can we do to stay ahead Evaluate the ASIS process and identify potential risk Estimate the probability of various risk and quantifying the severity on OTIF Simulate multiple scenarios and provide recommendations Visibility and insights into suggested recommendations The Challenges behind On Time In Full Delivery by suppliers Raw material unavailability causes production disruptions Unable to receive products on time for Production or Sale Drop in overall SC Service level Product Shortages to cater the customer demand Difficulties in achieving the revenue targets and impact in the brand reputation What does this mean for Supply functions Mitigate risks associated with product availability and improve customer service Improve planning and mitigating production delays Early warnings for supplier fill rate drops highlight high risk Actionable insights for immediate short term and longterm risk factors Approach Challenges Impact There is a growing need to monitor supplier uncertainty and plan for lead time variations and product shortages Tracking inbound orders risk recommend mitigation options solution approach Assess tradeoff between different scenarios considering various constraints Recommending multiple alternatives to tackle the incoming risk Impact of suggested recommendations Evaluate Existing Risk and gap analysis Track historical patterns of supplier behavior on Delivery Time and fill rates Perform gap analysis to evaluate the current OTIF performance w.r.t. targets Variance analysis to assess the degree of deviation from baseline Bayesian and regression modelling to predict delivery risk and its impact on OTIF Risk prioritization of various risks for immediate business interventions Segmenting risks into warning alarming and emergency Trend Analysis S hortage Prediction Supplier recommendations Predict risk severity and occurrence Scenario Planning and recommendation Generating insights from historical data performing variance analysis Identification of internal and external risks impacting OTIF Exploration of independent and confounding variables Simulate multiple scenario by changing constraints such as like lead time variability demand variability cost forecasts geographic locations etc. Crossvalidation with business on test data to minimize the gap Actionable insights using AssessEvaluateIntervene framework Estimating probabilities of each risks identified using Bayesian belief network Building cause and effect model to quantify the impact of risk using regression methodology Apply RPN risk priority number to find the gap between expected and actuals Create inference networks and monitor risk through Bayesian models Data requirements TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING TRACKING AND MONITORING OTIF HISTORY Supplier Fill Rate 92 Supplier Count 26 Order count 11.34K Delay Risk 6 RECOMMENDATION DELIVERY RISK RISK PRIORITY NUMBER RPN AND DELIVERY RISK TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING RECOMMENDATION Supplier Fill Rate Delivery Risk SCENARIO PLANNING TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING RECOMMENDATION RECOMMENDED APPOINTMENT DATE RECOMMENDED CARRIER RECOMMENDATIONS TRACKING AND MONITORING RPN AND DELIVERY RISK SCENARIO PLANNING RECOMMENDATIONS 0 2 Tactical risk How can analytics help to stay ahead Product risk traceability using graph database blockchain Visibility for continuous assessment and monitoring of product flows and risk assessment impact vs likelihood of occurrence across suppliers Integrating tier II upstream network with their real time data Balancing allocation scheduling to minimize unplanned production outages Dealing with Product Shortages Lack of visibility late detection of disruption where when and how is my product Propriety Single Point sourcing vs Generic cost quantity quality in event of geographicaleconomic disruption Quantifying future disruptions and developing mitigation plan What does this mean for Supply functions Comprehensive views of supply chain vulnerabilities across products lines Optimize capacity and production strategy to mitigate risk impact from single point source tier I Simulated model helps mitigation planning for a downturn Approach Challenges Impact Product traceability can help predict shortages and plan alternatives Trace product flow predict and mitigate product shortages solution approach Real time visibility Enhance product visibility around Product inventory level at supplier Production schedule Product Shipment schedule Customized views ExecutiveRM Planner Production Manager etc. Product traceability to identify risk probe instances Business recommendations Identify the risk associated in the product flow to downstream nodes Provide recommendations for the generic as well as proprietary products Alternate sourcing options to mitigate dependencies Tracking Business Recommendations Predict when and where excesses and shortages are likely to occur Assess impact of different inventory levels and product shortages Alert on the root causes of shortages Prediction Risk prediction Physical to digital i.e. capture info of the physical world and create digital records Graph DB Blockchain for product flow risk traceability from source to destination Risk prioritization using impact vs priority to devise mitigation plan Digital to digital i.e. using advanced AIML technique scenario planning identify when where shortages are most likely to happen DSN considers local global controllable non controllable disruptions scenarios Proactive planning to tackle impending risks surfaces in raw material flow to production sites Digital to Physical i.e. recommending decisions and actions required for generic and proprietary products Strategic initiatives such as negotiation contractconflict management responsive sourcing for network dependent proprietary parts. Alternative sourcing discounting optimal landing cost considering product quality and cost can be recommendations for generic parts. How we can leverage analytics for digital supply network Data requirements   SCENARIO PLANNING OVERALL PRODUCT SHORTAGES BY PRODUCT CATEGORY RECOMMENDATIONS PRODUCT DYNAMICS TRACKING AND MONITORING PRODUCT TRACEABILITY AND SHORTAGES ACROSS SUPPLIERS Product Cat All Product All Region All DIGITAL RECORD Total spend LY 112.32M Tier 1 Supplier Count 26 Total No of Products 11.34K Tier 2 Supplier Count 26 Tier 3 Supplier Count 26 MAPPING RISK FLOW TRACEABILITY Number of Regions 12 RECOMMENDATIONS PRODUCT FLOW SCENARIO PLANNING DIGITAL RECORD Actual vs Simulated Product Shortage overtime Supplier All Product Cat All Product All Region All SCENARIO PLANNING All All Delivery Time Rail Road Air Vessel Mode of Transport All Source of Origin All Trade Restrictions Tariff rates LABOUR Trade Union Presence Yes No PRODUCT SHORTAGES BY REGION Date Range All Growth Outlook Boom Recession Recov ery All All Price volatility Inflation risk Controllable factors Uncontrollable factors SIMULATION TOOL DELTA CHANGE IN PRODUCT SHORTAGE Actual vs Simulated 25 Relative Volume Value Alternate Product RM All SCENARIO PLANNING RECOMMENDATIONS RECOMMENDATIONS Total Products at Risk 100 Products for which warning issued 60 Products for which Alarm issued 25 Critical Products 15 GENERIC PRODUCTS RECOMMENDATIONS AND IMPACT Date All Product Type All supplier All DIGITAL RECORDS LANDED COST BENEFITS FOR PROPRITORY PRODUCTS Warn Alarm Emergency Safe 0 3 Strategic risk What Your Organization Can Do to Stay Ahead Identify capacity gaps in the existing network Utilize AI enabled digital supply chain design tools to optimize total landed cost. Utilizing Analytics to proactively identify climatesensitivegeopolitical regions Effective Restructuring Plan to optimize supply network from reshoring to changing supplier or shifting production The Challenges in Procurement Increasing demand complexity as a result of expanding requirements diversity Existing Internal and External Capacity unable to meet demand Supply Nodes not connected to get Network visibility No sustainable sourcing due to siloed Supply Chain Operations. Climate change External events impacting supply chain network. What This Means for Your Organization Improved predicted capacity can help take strategic decision to set up new manufacturing site or source externally. Optimized Landed cost can unlock demand and increase efficiency to maintain profitability Boost long term security and productivity from climate changes external events by timely implementing restructure plan Approach Challenges Impact Having a 360degree view of the supply network is essential to plan for the longterm We will start by analyzing supply network reliability and then generate long term recommendations ML based capacity planning to evaluate additional supply requirement at a brand and category level Predicting and optimizing total landed cost and lead time using AI to support an evidencebaseddecisionmaking approach Recommending multiple alternative routes to source products to tackle the incoming risk Evaluate Existing Network and key KPIs Track key KPIs such as Capacity Utilization Lead Time Transit Time Volume supply delays for Product Flows across brands and categories Track historical data to understand climate and geopolitical variability over time for key OEMs Understand the Baseline Distribution Network to identify bottleneck routes pathways supplier geographies across products Modelling the entire supply chain as graph based network Identify risk severity and understand the impact on existing network using AI based detection networks Predicting the risk and simulating for 12 to 18 month horizon Perform AI modelling to assess tradeoff between different scenarios considering various constraints Trend Analysis Risk Severity Prediction Recommendations Predict risk severity and occurrence Network Planning Recommendations Generating insights from historical and forecasted demand patterns of various product procurement categories Tracking external climategeopolitical risk data for key OEMs Regions to understand variability overtime Identification of internal and external risks impacting product at brand and category level Use Ensemble regression techniques to estimate additional resources needed to meet forecasted demand Analyze and reduce the total landed costs associated with modifying the product procurement from source to affiliate Evaluate and bridge the gap by crossvalidation Deduce Evidencebased Strategies to deal with long term risks Analyze the severity of risk based on future demand requirements capacity cost and lead time Model risk prone pathslanes using graph based neural networks generated by AI Building cause and impact model to quantify the risk using regression methodology Use Regression to predict and simulate the risk for a 1218month horizon What if analysis for multiple scenario by changing constraints such as geographic locations type of transportation Alternate vendors etc. Methodology to optimize supply network using process parameters and external variables Data requirements TRACKING AND MONITORING EXISTING NETWORK LANDED COST DISTRIBUTION ACROSS VENDORS Product Demand and Capacity by Category V1 V2 LEAD TIME AND PRODUCT VOLUME PREDICTIONS Supply Chain Disruption Choose Scenario RM Requirement Fulfilled 70M per week RM Requirement UnFulfilled 1M per week Change in Landed cost 70M per week Total Lead Time 2 weeks Delay Simulated Vs ACT 2 Risk nodes in current lane 2 Rail Road Air NETWORK PLANNING RECOMMENDATIONS 55 99 25 5 A CTUALS

Smart OPS Dock_Yard_OTIF.pptx

Smart OPS Team 3 November 2022 01 02 03 Dock capacity planning Yard management OTIF Sections Flow 1.0 Inbound TWIX OTIF Yard Management Dock Capacity Planning When to start pickpackload and Storing in stage area Which loads to start out of all loads Pickup Appointment datetime OT Risk of loads droplive Outbound What is the waiting time of each truck Where the truck have to wait slots Pickup Appt datetimeLRDT used in twix outbound to determine which loads to schedule pickpackload Dock capacity used in twix as constraint resource planning for twix to determine max how many loads to pickpackload out of all loadsupper bound staging area if dock not available then stg area not available to pickpackload Appointment dock scheduling pickpackload allocation When to start outbound process Info regarding priority cross dock inbound loads Visibility Which outbound trucksloads are in my yard How to Sequence Trucks to docks for inbound Prioritization Predicting IB Unloading time Info regarding cross dock inbound loads Pickpackload time as a constraint to unloading put away time should happen before pickpackload Drop load appt window 24 hrs Live load appt window 6 hrs loads constraint to outbound Flow 2.0 Inbound TWIX OTIF Yard Management Dock Capacity Planning When to start pickpackload and Storing in stage area Which loads to start out of all loads Pickup Appointment datetime OT Risk of loads droplive Dock capacity droplive Dock type categorization can be used in twix Outbound What is the waiting time of each truck Where the truck have to wait slots Pickup Appt datetimeLRDT used in twix outbound to determine which loads to schedule pickpackload Dock capacity used in twix as constraint resource planning for twix to determine max how many loads to pickpackload out of all loadsupper bound staging area if dock not available then stg area not available to pickpackload Appointment dock scheduling pickpackload allocation When to start outbound process Info regarding priority cross dock inbound loads Visibility Which outbound trucksloads are in my yard Dock schedule is optimized according to realtime ETA predictive to minimize waiting time and inform scheduler whether the truck will be late How to Sequence Trucks to docks for inbound Prioritization Predicting IB Unloading time Info regarding cross dock inbound loads Pickpackload time as a constraint to unloading put away time should happen before pickpackload Drop load appt window 24 hrs Live load appt window 6 hrs loads constraint to outbound Add Explanability 2.0 OTIF Bin change but based on dist history etc 3. Why whitelist not working 2. bnlear limits white list search to specified edges in white list python New data Walmart 4. Causalnex 4. PBI 1. PBI Done Walmart Causalnex old data issues direction acyclic mem issue Plan binning alerts logic recc carrierscluster lanes ontime timings volume last 63 months spot consideration 3.0 Yard Dock not in sync with trailer 3. Check door condition for 999 4. Add truck in input data 2. Consider late arrival 5. Consider dock not available at appt time 5 . Stg id add dock instead of door 5. Load importance column Dispatch appt time 5. ML Algo Optimization PBI 1. Dock Prophet 2. 60 days and 30 days 1. Lag value change 3 . Multiply the data with constant divide the results back 4. Hierarchy weekly and divide for daily average 5. Simulate more data PBI 1. Dock type data request Explanability ER Table star schema PBI Story Dock 6030 new data lag Flow yard Otif flow Walmart date change V1 V2 Tracker Git update V1 V2 ER Diagram Yard Dock OTIF Key attribute Composite attribute Multivalued attribute Derived attribute Relationships ER Diagram Key attribute Composite attribute Multivalued attribute Derived attribute Relationships Phone Warehouse Code Location Capacity Has Doors Yard Contains Lots Shipments Goes to Has Inbound Outbound Belongs to Customer Deliver by Carrier Goes on Trips Journey Driver Trucks Type Location Dock no. Stg location Stg capacity Type Status Location ID Dates Type Code Status ID Name Customer Email ID Name Number Volume Container type Carries Shipments Between Pickup RDD Location Name Lot no. Need by date Phone Phone Location Pincode Country State City Phone Phone ID Trailer location Stg location DropLive Dates Appt time ID Door Stg location Trailer Dates Appt time Arr time Unload time Dispatch Trailer Arr time Load time Dispatch ID Distance Origin Destination Dates Dates Tender accept Actual arrival Dates Load complete Checkout LRDT RDD ER Diagram ER Diagram N 1 1 1 1 1 ER Diagram 01 Dock Capacity Planning Dock capacity planning What Forecast of loads that is going to go out on a given date loadsdocksshipments all same Why To determine how many docks to open will be occupied and how many other open slots will be available How Time series to forecast the of loads how many docks should I keep open Impact Carriers book in delays if no slots Lack of workforce to load which will lead to delay in loading Input V1 Historical load patterns Holidays Seasonality Others Upcoming order details Load priority Complexity RDD Output of loads TWIX To determine how many loads and when Question Overall of loads Customer level needed Load Priority Complexity Shifts Solution framework CodeFlow Drop split Filling in blank dates Visualize trend Ffill bfill interpolation Moving avg 3090180 days Rolling mean Additive decomposition Weekly resample Monthly resample Quarterly resample Outlier check Stationarity test Train test split Auto arima Rmse mape mape2 Prophet error Other models for multivariate V1 Flowchart Model Output Drop Type Linear Interpolation Live Type Linear Interpolation AutoARIMA MAPE 11.24 RMSE 18.19 Random Forest MAPE 12.73 RMSE 18.33 Xgboost MAPE 13.77 RMSE 19.54 Lightgbm MAPE 13.04 RMSE 18.50 AutoARIMA MAPE 24.97 RMSE 2.93 Random Forest MAPE 24.86 RMSE 3.28 Xgboost MAPE 36.08 RMSE 4.18 Lightgbm MAPE 27.57 RMSE 3.19 Adaboost MAPE 13.69 RMSE 19.81 Test 60 days Decision Tree MAPE 36.49 RMSE 4.48 Model OutputDrop Model OutputLive Model OutputDrop Model OutputLive Model Output Drop Type Forward Fill Live Type Linear Interpolation AutoARIMA MAPE 16.26 RMSE 5.79 Random Forest MAPE 12.01 RMSE 16.28 Xgboost MAPE 12.36 RMSE 17.93 Lightgbm MAPE 11.96 RMSE 15.82 AutoARIMA MAPE 16.39 RMSE 5.85 Random Forest MAPE 14.51 RMSE 5.96 Xgboost MAPE 32.95 RMSE 11.26 Lightgbm MAPE 22.69 RMSE 8.21 Adaboost MAPE 13.01 RMSE 16.99 Test 30 days 02 Yard Management Yard management What Where the truck have to wait in the yard What is the waiting time of each truck Visibility What is where truck Best yard slot What is coming in and going out How V1 Visibility integration of tms and wms intransit visibility Heuristics V1 Optimize yard slots when in allocate slot based on dock time Optimize yard slots when in allocate slot based on dock time rdd etc. Move during stage timeload time back to yard based on pickup time notify carriers lrdt ready for pickup ETA Prediction Input V1 In time Out time dock scheduled timings target unload end time Appointment timings Master data of yard and dock Location loads unloads Pickup appointment datetime Shipment data PO TMS data WMS in and out gps average turn times empty slots inbound appointments Output Visibility Yard Slots Questions Only for drop Or both droplive Content of truck Shipment optimizer shipment with trailer ETA for shipment arrival or ETA to be assigned to dock Specific distribution of yard Execution Visibility Where the Truck have to wait in the yard Best Yard slot What are all the next carrier moves at what time What is coming in and going out What is the waiting time of each Truck Dock Status at that moment Lot Status at that moment Trailer Status at that moment Truck comes in checks door availability Queue Yard lots allocation Swapping Update all the status data frames Doormap Lotmap Trailermap in real time Next Carrier moves at what time Visibility to the operatordriver Unloading Endtime from inbound in dock ops slide appointment time should be from inbound based on endtime in lot ops Yard Management Inbound Dock scheduling Queue Yard Assumptions Door can only be inb or outb Lot can only be inb or out Droplive together Door wise only for now can include stg later Realtime flow with cdat WH Yard Structure Appointment time Yard Process Flow Dataframes 1. Doormap 2. Lotmap 3. Trailermap 4. Carrier move 5. Queue V1 assumptions Door can only be inb or outb Lot can only be inb or out Consider only early arrival and ontime arrival with buffer 10mins Truck ontimeearly Dock available ontime loadunload and late arrivals Droplive together Inbound comes unloads goes off Outbound comes loads goes off Door wise only for now can include stg later Realtime flow with cdat DISPATCHat Alternate stg area WH V1 Flowchart V2 Flowchart Appt time If many are delayed dont consider queue check load importance DISPATCH APPT TIME NEW COLUMNS add priority carrier data Carrier move df added to track movement of shipments V2 Flowchart WH V1 Scope V1 assumptions Door can be either inb or outb Lot can be either inb or out Consider only early arrival and ontime arrival with buffer 10mins Truck ontimeearly Dock available ontime WH V1 Scope Flow Flow Assumptions Truck ontimeearly Dock available ontime Separate inb out docksslots Flow Check if dock available if not go to parking based on the same conditions Flow Check timing of slots already occupied Flow Not considering this for v1 Loop Loop Loop Loop Data Request What Where the truck have to wait in the yard Allocate proper slots for the trucks based on heuristics What is the waiting time of each truck How to reduce waiting times of the trucks What is coming in and going out Find daily avg turn times dock schedule times loads etc Based on that day lrdt loads determine outbound loads Make sure the truck leaves on time to arrive on time at the cust end Prioritize loads Wmsstaging data dock schedule timings appt time target loading end time status of the docksdoors load id inbound or outbound load load type The number of available dock doors Yard data lot status trailers loadedempty oos how many lots total inbound outbound wise oos move requests avg of moves Amount of time elapsed from gate arrival until the trailer is moved to the door Elapsed unloading and loading time The type of trailers in the yard trailer type 2040 Tms data in time Inbound The number of inbound trailers passing through the gate per day week month and year of unloads per day door wise Outbound The number of outbound trailers passing through the gate per day week month and year of loads per day door wise How Identify status of the docksavailable docks based on staging data Identify slots in the yard Calculate waiting time etc 1 Master and Location data    a Yard Slots Inbound and Outbound slots total slots Slot location latitudelongitude Slot details and OBIB ops marked against each Slot. Refer to Dock_Staging_Mapping File. Couldnt provide LATLONG    b Dock Inbound and Outbound docks total docks dock location Dock details and OBIB ops marked against each dock. Refer to Dock_Staging_Mapping File. Couldnt provide LATLONG    c Trailer Location parking dock Provided as part of Transaction data Inbound and outbound data 2 Slot Status Out of service available closed Refer to Dock_Staging_Mapping File 3 Trailer Time related data points Trailer id intime outtime arrival departure Provided as part of Transaction data Inbound and outbound data. Refer to ArrivalDispatch columns. 4 Trailer ID to Load ID Mapping Provided as part of Transaction data Inbound and outbound data. Refer to Shipment ID and Trailer Number columns. 5 Inbound Loads Load ID Provided as part of Inbound Transaction data    a Appointment Time    b Unloading Time Duration    c Dock Scheduled    d Drop vs Live Load detail    e Target unloading End Time    f Intime 6 Outbound Loads Load ID Provided as part of Outbound Transaction data    a Appointment Time    b Dock Scheduled    c Load details    d Target Load End Time    e Drop vs Live Load detail    f OutTime Rough What Where the truck have to wait in the yard Allocate proper slots for the trucks What is the waiting time of each truck How to reduce waiting times of the trucks Load the container and put it back to yard What is where truck Best slot What is coming in and going out Make sure the truck leaves on time to arrive on time at the cust end Prioritize loads Find daily avg turn times dock schedule times loads etc Based on that day lrdt loads determine outbound loads Wmsstaging data dock schedule timings appt time target loading end time status of the docksdoors load id inbound or outbound load load type The number of available dock doors Yard data lot status trailers loadedempty oos how many lots total oos The type of trailers in the yard move requests avg of moves Amount of time elapsed from gate arrival until the trailer is moved to the door Elapsed unloading and loading time trailer type 2040 Tms data in time Inbound The number of inbound trailers passing through the gate per day week month and year of unloads per day door wise Outbound The number of outbound trailers passing through the gate per day week month and year of loads per day door wise maintaining a weekly or daily average of specific outbound trailer types andor carriers and comparing it to todays number can reveal increasing or decreasing durations of time spent in the yard minimize the nonloaded traveling distance and obtain the shortest completion time to finish the loading and unloading of containers in multiple vessels minimize vessel delayed departures optimizes fuel consumption and waiting time at the port by reducing vessel operatingspeeds to optimal levels and minimizes the effects of late arrivals to the carriersschedule 03 OTIF Execution Need OT if actual delivery within 24 hrs. RDD. Bayesian Networks to determine interdependency between variables and provide real time OT probability Alerts as per business process and recommended pickup appt windows Recommending alternate carriers for each lane similarity scoring as per alerts generated for a load OTIF Whats the need Visibility into shipments at Risk Inference impact of activities on the final risk of on time delivery Alerts and Recommendations to enable early intervention Recommend optimal pickup schedule at lanecarrier level Collaborative tool with alerts and recommendations WIP Bayesian Network to recommend pickup appointment and identify interdependencies and their risk scores across outbound processes Real Time Risk generation Generate a realtime risk score for each load based NOTEARS algorithm to determine best network with an optimized score and arc strength satisfying minimum threshold Blacklist weakest arc strength andor do not make business sense. Whitelist edges as per business knowledge Supervised model to detect probability of on time arrival considering all evidence present Bayes Network 1 Bayes Network 2 Optimal PickUp Window Optimal pickup window for each load based on all associated factors Simulate pickup appointment times for each lane Inference based model providing simulated appointment times lane information as evidence Arrive at opt window with ot business defined threshold VARIABLES Time based Delivery appointment Pickup appointment Load start time Load complete time Load checkout time Lane based Origin Destination preferred carriers Carrier based Carrier tender acceptance date transit time deviations ETA WIP Optimal pickup window Loads at risk Alerts and recommendations Inference based modeling to measure impact of shipment profile carrier history and lane information Simulate pickup appointments for each lane and arrive at optimal time window with on time probability or business defined threshold Leverage optimal window information to measure risk and recommend alternate pickup strategies Predict on time delivery risk for all outbound shipments Inference modeling to capture interdependency of variables and monitor the risk from moment of creation to delivery Refresh delivery risk at business defined frequency Recommend alternate pickup appointment or carriers within a lane based on ML model outcome to mitigate high risk Alerts Carrier Pickup Appt Checkout defined on business process and risk score to help user zero down on shipments with specific issues Outcome of the algorithms WIP Data requirements We will be revisiting the data sources and availability in the discovery phase for each of the customers Infull risk LRDT Forecast Shipment Customer SKU Granularity Scale to all customers Heuristics Compare Av. Inventory OH Intransit each sku Inv forecast Av inv T. shipped Hierarchical Ts Infull risk each sku Min base OH LRDT display Short shipment InFull toll In full Rough In full Rough In full prediction Toll Predicting occurrence of shipment daily weekly for next 5 days Infull flow Shipment forecast sku granularity Prob of ship occurring risk prediction from tollOnly for loads where lrdt has not occured OH InTransit shipment forecast cut lrdt occurred provide classification predict prob of in full inv data Min OH inv cannot go below that get how actual order reqd provide Where in process before lrdt OTIF What V1 Predict OT Risk probability at the end node V1 Optimal schedule for pickup date time based on rdd vs lrdt Are we adhering to the promise date and appointment time How V1 Determine OT Risk probability V1 Optimal Pickup window for each lane based on rdd vs lrdt Determine optimal pickup appointment date based on n days from promise date eta estimated time of arrival of carrier track eta to check whether the carrier arrives on time to pickup. Calculate OT risk realtimeevery refresh horizon based on expected time at each stage buffer Impact Penalties on carriers not delivered the shipments on time Input Shipment data LRDT RDD origin destination tender status load start load checkout appointment datepromise date chk in chk out time order created tendered pickup loading checkout Arrival time Output Optimal Pickup Appointment timing will be used in TWIX to schedule pickpackload OT Risk Questions Rddpd always full stock Directly take shipment data or poso data Carrier customer decides lrdt Solution framework V1 Flowchart order_created scheduled_in_tms scheduled_in_ tms tender_accepted_date tender_accepted_date lrdt lrdt load_complete load_complete load_checkout load_complete ontime_flag load_checkout ontime_flag lrdt ontime_flag origin_city ontime_flag destination_city ontime_flag V2 Alerts if load complete lrdt 0 red 8 yellow otherwise green difference in hours colors logics 1 Tender Accept start_ttl green last_ttl red otherwise yellow 2 lrdt load checkout start_ttl yellow last_ttl red otherwise green if inbetween 3 load complete if load complete lrdt 0 red 8 yellow otherwise green difference in hours   Alerts 1 Change Carrier tender acceptance red greater than last_ttl or max_lrdt 2 Reschedule Pickup Appt . if lrdt outside min max lrdt range with 12 window buffer 3 Set Pickup Appointment if change carrierreschedule pickupSet pickup 4 Loading incomplete if load_complete after lrdt 5 Late Checkout if lrdt after last_ttl Walmart Training on walmart Cpds prob varies with origin lrdt bnlearn WIP use saved model on test DAG with white lists white_list destination_city ontime_flag lrdt ontime_flag origin_city ontime_flag lrdt load_checkout black_list lrdt origin_city Lane not captured Bnlearn python source code white lists filters to selected edges will limit search to those edges. Defined DAG order_created scheduled_in_tms scheduled_in_ tms tender_accepted_date tender_accepted_date lrdt lrdt load_complete load_complete load_checkout load_complete ontime_flag load_checkout ontime_flag lrdt ontime_flag origin_city ontime_flag destination_city ontime_flag Output Ontime Steps 1. Reading Data 2. Calculating and 10 binsbinning hours before delivery mabd for every milestone 3. Calculating distance bins. 4. Defining OnTime Flag both after delivery and 24 hrs before delivery are considered not OnTime 5. Define DAG using business knowledge param learning to generate CPDs 6. Inference fit using every row as an evidence to get conditional probability for OnTime 7. Deciding probability cutoff where accuracy sensitivity specificity intersect to classify rows into ontime not ontime not used as of now 8. Calculated average deviation in hours between 2 subsequent milestones based on distance bins. missing dates shouldnt impact risk 9. Saved the model bins deviation probability cutoff in pkl file to be used further. 10. Fill missing date time stamps 10. Use trained DAG bins on test data to generate OT Ontime Variables used in model Binned Difference between rdd and 1Order created 2Scheduled in TMS 3Tender accepted 4LRDT 5Load complete 6Load Checkout Categorical 1Origin 2Destination DAG from data Kroger ontime_flag destination_city origin_city scheduled_in_tms order_created lrdt DAG from data same DAG used for window andcurrent ot ot varies with lrdt but doesnt vary with lane. Structure of the model built ot becomes conditionally independent of destination and origin given lrdt ontime_flag lrdt order_created scheduled_in_tms tender_accepted_date load_checkout destination_city load_complete origin_city DAG from data Target DAG from data same DAG used for window andcurrent ot ot varies with lane but doesnt vary with lrdt . Structure of the model built ot becomes conditionally independent of lrdt given destinationorigin Optimal Window Steps 1. Reading Data 2. Creating lane variable using origin and destination city 3. Calculating and binning hours before delivery rdd vs lrdt 4. Defining OnTime Flag both after delivery and 24 hrs before delivery are considered not OnTime 5. Simulated appointment hours before delivery mabdvslrdt from 1 to 121 for every lane 6 .Use Stored cuts from train data to bin appointment hours before delivery mabd_vs_lrdt 7. Use Defined DAG from risk model 8. Ran the DAG model and got conditional probabilities of Ontime using inference fit with origin destination and lrdt bins passed as evidence 12. Selected those mabd_vs_lrdt for which the ontime probability is maximum. 13. Mapped to corresponding lane the min and max appointment hours before delivery. Work Distance Train bins Old Lane Model Simulated Output OTIF Current scenario Deliveries of shipments beyond RDD are penalized for certain of invoice value Shipments have multiple milestones from creation to delivery and no single source of truthtracking exists Appointments are driven by carriers based on tender acceptance Users need to check multiple systems SAP TMS etc. to get full picture of the shipments to take action Multiple stakeholders involved cross collaboration is challenging Whats the need Visibility into shipments at Risk Inference impact of activities on the final risk of on time delivery Alerts and Recommendations to enable early intervention Recommend optimal pickup schedule at lanecarrier level Handle customer level customizations like refresh frequency on time definition exception handling etc. Integration of a various data sources like SAP TMS WMS GPS tracking etc. Collaborative tool with alerts and recommendations Absence of single source of Truth Insights and actions manually driven consume time No alerts or recommendations on orders at risk Challenges Suboptimal schedule for pickup and delivery No real time risk monitoring to intervene and save shipment Optimal pickup window Loads at risk Alerts and recommendations Inference based modeling to measure impact of shipment profile carrier history and lane information Simulate pickup appointments for each lane and arrive at optimal time window with on time probability 90 or business defined threshold Leverage optimal window information to measure risk and recommend alternate pickup strategies Predict on time delivery risk for all outbound shipments Inference modeling to capture interdependency of variables and monitor the risk from moment of creation to delivery Refresh delivery risk at business defined frequency Recommend alternate pickup appointment or carriers based on ML model outcome to mitigate high risk Alerts defined on business process and risk score to help user zero down on shipments with specific issues Outcome of the algorithms Data requirements We will be revisiting the data sources and availability in the discovery phase for each of the customers Interactive UI to track multiple facets of a shipment Quic k summary of projected risk and on time by customer Which customers are at most risk for on time deliveries Which origin DCs have most of the shipments at risk What is the projected on time How many recommendations are available to make interventions How many loads need planning Execution screen to track all active loads Continuous monitoring of outbound shipments to improve on time deliveries Which are the shipments at highest risk What actions need to be taken to mitigate risk Has the carrier been notified of the next step Are the appointments setup to be optimal from a delivery point of view Is it the right carrier to execute the load Better planning Optimal appointment schedule recommendation How many shipments require pickup and delivery appointment to be set across DCs What is the optimal pickup appointment and the dock number based on the shipment profile Optimal pickup appointment window determined by ML model at lanecarrier level Optimization algorithm to assign available docks to maximize OT Alternate recommendations Mitigation strategies for shipments at risk Are there shipments that can benefit from alternate pickup appointments and what are the new appointments Are there shipments that can have a better on time delivery probability with an alternate carrier What is the updated risk if I follow the recommendations Flow 2.0 Inbound TWIX OTIF Yard Management Dock Capacity Planning When to start pickpackload and Storing in stage area Which loads to start out of all loads Pickup Appointment datetime OT Risk of loads dock capacity droplive Dock type categorization can be used in twix Input to TWIX on total loads capacity of docks Outbound What is the waiting time of each truck Where the truck have to wait slots Pickup Appt datetimeLRDT used in twix outbound to determine which loads to schedule pickpackload Dock capacity used in twix as constraint resource planning for twix to determine which loads to pickpackload out of all loadsupper bound staging area if dock not available then stg area not available to pickpackload Appointment dock scheduling pickpackload allocation When to start outbound process Info regarding cross dock inbound loads Dock schedule is optimized according to realtime ETA predictive to minimize waiting time and inform scheduler whether the truck will be late Visibility Which outbound trucksloads are in my yard How to Sequence Trucks to docks for inbound Prioritization Predicting IB Unloading time Insight of Inbound Availability given to Twix Info regarding cross dock loads Pickpackload time the unloading put away time should happen before pickpackload Drop load appt window 24 hrs Live load appt window 6 hrs loads docks constraint to outbound Add Explanability General Questions Immediate RDD based on priority and rdd okay to have penalties for other shipments Recommendations to change appointment date and carrier in scope The trailer after getting loaded from the dock directly leaves the wh or it will go to the yard and leaves from the yard after some time whr does outbound comes in here loads in inbound dock capacity workforce Assume stock 100 Forecast keeping changing

Smart Ops Inbound Prioritization.pptx

01 Smart OpsInbound Prioritization Overall Flow Inbound TWIX OTIF Yard Management Dock Capacity Planning When to start pickpackload and Storing in stage area Which loads to start out of all loads Pickup Appointment datetime OT Risk of loads droplive Dock capacity droplive Dock type categorization can be used in twix Outbound What is the waiting time of each truck Where the truck have to wait slots Pickup Appt datetimeLRDT used in twix outbound to determine which loads to schedule pickpackload Dock capacity used in twix as constraint resource planning for twix to determine max how many loads to pickpackload out of all loadsupper bound staging area if dock not available then stg area not available to pickpackload Appointment dock scheduling pickpackload allocation When to start outbound process Info regarding priority cross dock inbound loads Visibility Which outbound trucksloads are in my yard Dock schedule is optimized according to realtime ETA predictive to minimize waiting time and inform scheduler whether the truck will be late How to Sequence Trucks to docks for inbound Prioritization Predicting IB Unloading time Info regarding cross dock inbound loads Pickpackload time as a constraint to unloading put away time should happen before pickpackload Drop load appt window 24 hrs Live load appt window 6 hrs loads constraint to outbound Overall analytical approach to Construct Inbound Prioritization Raw Data Prediction of time Monitoring Inbound Sequencing Optimization Script Classification of Inbound shipments to track late Ontime and Early deliveries Using rulebased algorithm EDA and Feature Engineering Prediction of unloading   put away time Extract the features impacting the Unloading and Put away time Implementation of Quantile Regression Sequencing of Trucks based on the values of the decision variable from the solution of LPP Calculation of priority score using different attributes of shipments and waiting time of Trucks Formulation and solution of LPP for assigning the docks Monitoring Algorithm flow diagram IB Data Current Time IB Appointment Time IB Arrival Time Late On Time Early Arrival arrival time less than appointment time buffer Yes No If arrival time Equals appointment time Yes arrival time is null and appointment time is lesser than current time buffer Yes No Heuristic approach P rediction of Unloading and putaway time of a shipment Establish causality and generate a causal model to identify the factors impacting the Unloading and putaway time of a shipment Quantile Regression Outcomes Predicted Minimum and Maximum unloading putaway time for a shipment This predicted time will act as a constraint as well as a criteria to determine by what ti me the docks will be free. Causal Modelling Estimate unloadingputaway time using quantile regression in order Using quantile regression predict the minimum and maximum unloadingputaway time Product Specifications Truck Specifications WH Shift details Features impacting Unloading Putaway Time Warehouse Constraints Prediction FF Selection of attributes for Priority score calculation Priority Score Calculation Assigning of weights to attributes Mathematical formulation Different attributes are taken into consideration for Priority score calculation Attributes such as Outbound Requirement and waiting time of a truck etc. Assigning weights to different factors attributes based on user input Priority scores are calculated for different trucks Mathematical program to assign optimal sequence to trucks at different docks. Solvers Ortools Pulp Formulation of problem based on constraints like Dock capacity Droplive etc while maximizing priority scores Truck Sequencing Optimization Priority Score Calculation Approach Output IB Shipment Data IB Arrival time Calculated scores Normalised Waiting time Priority Scores Priority Scores for each truck is calculated based on assigned weights and Variables The truck with the highest priority gets the maximum priority score and so on. Current Time Trailer waiting time V2 Other Variables Product type Truck Volume Unloading time Other Features Assigned weights Priority Scoring w10.50 w20.25 w30.15 w40.10 Outbound requirement V1 IB HVG V3 IB DG V4 Priority Score Factors V x Assigned Weights W Truck sequencing optimization Approach Output Shipment Data Unloading time Data Other features Dock Availability Inventory Status Carrier Penalty Objective Maximize Priority Score X Decision Variable X Reward function Where Priority Score Score Depicting Importance of a Truck Weights Rewards on different sequences of any dock Decision Variable X 01 binary variable Subject to constraints Staging area capacity Assigning truck only once Special Goods Shift time available Staging area mapping to docks Solvers Ortools Trucks assigned to optimal sequence at different docks based on factors like arrival time droplive goods type etc Automated system giving optimal sequencing of trucks considering real time constraints like outbound requirements unloading time putaway time staging area capacity shift time Special goods etc 02 ER Diagram Carrier Carries Truck Size Carrier ID Truck Capacity Location Carrier Name Shipment Placed In Warehouse LiveDrop Flag Shift Time Special Goods Appointment time No of Pallets Shipment ID Trailer Unloading time Carrier ID Staging location Arrival Time Putaway time Docks Special goods staging location Staging location Staging location capacity ER Diagram Unloading time Shipment ID IB ER Diagram 03 Mockup UI Designs V1 V2 Tracker Data Preprocessing to analyze and derive features for the model Applying Probabilistic model to predict the probability of early Late or Ontime shipments. Predicting arrival time intervals using quantile regression. Monitoring of Inbound Shipments to classify Late early and On time arrivals Prediction of Arrival time for Late and early shipments Shipments classified as late early and ontime along with their predicted arrival time intervals. Explore results and validate output with business on the inbound monitoring Monitoring to Classify Early Late and Ontime shipments Monitoring Approach Outcomes

Solution Approach for ABC's Inbound Operations_22-04-2022.pptx

Inbound Scheduling and Workforce optimization April 2022 01 02 03 Fractal Overview Our Understanding of the Problem Solution approach Content Fractal targets niche markets globally winning businesses with a client first think big act fast approach Founded in 2000 3400 Employees 100 Countries 16 Global offices 100 Fortune 500 clients P art n e r s h i p s Investors Client Partnerships Global Recognition 1.5B Market cap 680M capital raised Fractal is partnering with a leading Medical Surgical Pharma distributor from past 7 years to deliver significant business impact 300Mn incremental margins per annum 60100Mn Reductions in vendor chargebacks Single business segment 90 time improvement for gotomarket Executive summary Problem understanding ABC follows a hubspoke distribution model and uses NDC to supply its 26 other regional centres high proportion of Generics is distributed With multiple constantly changing contracts with their drug retailers and GPOs ABC needs to optimize Inbound scheduling and Workforce. ABC wants to solve for material relocation to all its 26 RDC regional centres Solution approach Fractal proposes a decision support system to optimize NDC and RDCs Optimize inbound scheduling for materials and workforce optimization of NDC Measure optimization improvements through key KPIs such as Dock utilization trend Manpower utilization etc. Optimal inventory reallocation across RDCs secondary priority Execution timelines For Inbound scheduling and workforce optimization solution we plan to deliver in 1418 weeks including 2 weeks of initial discovery phase Our differentiators Client First with 10 years of experience serving healthcare industry Institutionalized analytics through globally scaled programs that drive data driven decision making culture Client Value realization across early stages of client engagement to strategic partnerships Flexible Engagement Modes in highly secure OnshoreOffshore Operations Objective Business context ABC has contracts with suppliers to receive inbound materials on fixed schedule at the warehouse. ABC incurs additional surcharge owing to not assigning docks to the inbound supplier trucks ABC follows a hubspoke distribution model and uses NDC to supply its 26 other regional centers RDCs. It uses Suppliers fleet for Inbound and have its own 3PL partnerships for outbound Generics volume is larger than branded drugs and there occurs a constant contract changes per clients evolving need. ABC places order with suppliers on a weekly basis and the DCs get 1 week to execute on new contract changes The companys challenge is with inbound scheduling and dock assignment resulting in additional payment to suppliers. Our understanding of the problem ABC wants to improve the inbound scheduling for materials and optimize workforce to reduce this operational impact on business due to delays  NDC is always at capacity hence scheduling is always a challenge for inbound Multiple contracts exists with Pharma Retailers and GPOs that undergo frequent changes hence the need to solve for Inbound scheduling and workforce to enable truck unloading in less than 24 hours 02 Solution approach Optimizing ABCs Inbound operations Workforce through structured problem solving H ow can we optimize ABCs Inbound operations at NDC and RDCs NDC and 26 RDCs Optimal material reallocation Optimize inventory movement across ABC DCs based on capacity utilization and outbound commitments Visibility across all contracts to enable real time reallocation across ABCs clientele National Retail Pharmacies Hospital systems etc. Guidance on Inbound receiving for new contracts Optimize receiving schedule Analyze historical commitments and impact on DC capacity and resource utilization Recommend optimal receiving commitment for future contracts Recommend receiving sequence and dock appointment for inbound trucks Optimize dock allocation based on availability and other DC constraints Recommend required work force and resources to adhere to baseline schedule High priority Inbound operations workforce guidance Solution construct Analyze historical contract trends Assess impact on dock capacity and work force utilization Build model to recommend optimal receiving schedule for each upcoming contract Short horizon guidance 7 days to optimize inbound operations Model for historical receivingunloading times based on order complexity and shift Predict required time and resources for currentupcoming contracts Identify the optimal appointment time for unloading. Determine the optimal unload sequence for incoming trucks with Detention charges as constraints Optimize dock allocation based on receiving priority Dock availability capacity and other requirements to be considered Recommend workforce required to unload the truck Visibility into current DC status and upcoming tasks Contract receiving guidance for each supplier Optimized receiving schedule with dock recommendation User driven simulation to update the constraints to assess impact Optimizing Inbound operations at scale through AED approach Data ingestion layer to harmonize data from multiple sources and other formats Manhattan PkMS Labor Management TMS Ariba Coupa Centralized analytical dataset for visibility and modeling Error logging and exception handling as per best practices Operations visibility with baseline schedule summary and key metrics Visibility cockpit to schedule over time to observe latest status and changes required Interactive UI to select optimization constraints and baseline parameters Predict optimal schedule to receive contracts based on historical performance and current constraints Optimization algorithm for order prioritization and dock assignment Prioritize outbound requirements to prioritize receiving IPMIPGenetic algorithm for optimization Our approach to build a dynamic optimization engine Inbound orders Create visibility into all inbound orders Analyze items in the truck to calculate receiving complexity Assign end clients priority to achieve supply demand synchronization Resource availability Identify available docks for receiving Labor and machine availability Stagingstorage requirements DC constraints Current status of the DC and pending tasks Other DC constraints such as maintenance holidays space trainings etc. Outbound requirements Map the incoming orders to outbound requirements Identify dock to dock distanceproximity to select optimal receiving dock Optimization engine Dynamically update constraints based on latest snapshot Optimization to get an optimal schedule with user supplied parameters Penalty functions to minimize trailer wait timedemurrage costs Linear programming Genetic algorithm Simulated annealing etc. to be considered Output Recommended receiving time for contract across suppliers Recommended dock assignment Trailer receive sequence Visibility across key metrics for the warehouse with projections Mock up of User Interface to drive decisions Inbound visibility Guidance for new contracts Operational Visibility Optimized Sequencing Guidance on Inbound through put to enable contracts Monday Click to Expand SKU details Operations visibility Inbound operations overview Inbound shipments for today Top3 Drugs Inbound Equipment availability Number of Drugs 68 Number of cases 32343 units Shipments 93 Volume 21 CuM Drug A Drug B Drug C Workforce 53 Reach Trucks BOPTs Others 23 23 13 Supplier Pfizer Submit Operations visibility Inbound operations detailed view Optimized Sequencing Receiving schedules Dock allocation Unutilized Hours 6.7 We would need various data sets We will explore additional data required for optimization during problem discovery sessions Optimizer UI layer with all constraints and business parameters considered Integrated solution with source systems and hosted on cloud to generate real time insights Simulation engine Deliverables Deliverables We will start with NDC and scale the solution and features in a phased manner Automated scaled solution across region products and customers Deliverables High priority Timelines Week 2 Week 18 Week 12 Data source discovery thinking sessions 1 Introduction to key stakeholders and setting agendas Access to required tools and platformsservers Understanding of input data provision of extracts Aligning on solution methodology and success criteria Identify business rules explore key inputs Generate risk profile of inbound contracts to provide guidance for commitment Build optimization model and power bi tech stack to allow user driven runs Reporting layer to Indi cate the key metrics and baseline schedule Analytical model build Feature Eng. UAT Deployment 3 5 Business and technical Documentation UAT signoff and production movement Support timely refresh process Issue resolution tracker update Bug fixes if any Week 0 Deploy all pipelines to cloud and setup data connectivity to source systems Create simulation engine to conduct scenario analysis Validate business scenarios. CICD pipelines for easy deployment Cloud deployment and automation 4 Week 16 Data harmonization building unified data layer EDA 2 Enhance quality and utility of available business data Consolidate and harmonize data from multiple sources Data quality checks data augmentation and exploratory analysis Feature engineering for model building and optimization Week 4 Scope Deliverables and Outcome Engagement SponsorProduct owner Program Lead Final point of escalation Business SPOC IT SPOC Regular communication cadence and IT architecture alignment For all reviews status updates and internal approval for the analysis Governance Team Data owners SME Business Teams IT Team3rd Parties Transactional data procurement Analysis documents Coding and design best practice document sharing Overall thought leadership Weekly relationship steering meeting Supply Chain Global Solution Leader Proposed Engagement model Project Manager Solution lead Visualization Expert Supply Chain Consultant Design experts Data Scientist Data Engineer Assumptions Solution Design Inbound shipments visibility considered for next 7 days this can be customized based on business priority Inbound scheduling doesnt include any live load UI layer will be using Power tech stack Power BI Power Automate and Power Apps to enable user driven optimization runs User driven run and data refresh will be triggered using Automate and power apps with Power BI as the UI layer to host the functionality All software licenseinstallation will be provided by ABC and required folders and accesses to foldersone drive folder as required to automate the pipeline Engagement Delivery Cloud infrastructure for deployment will be provided by ABC Access to data sources will be enabled via VPNVDI Data mapping and matching sessionsdocumentation will be provided

Sun Pharma Analysis.pptx

RFP on Identification of Suspicious Order 28 th Jun22 Problem Sun Pharma has more than 6000 direct customers who act as distributors and supplies Sun Pharma products to retailers hospitals semidistributors and other modern pharma trade channels. Out of these distributors few distributors have fluctuating monthly purchasing pattern due to various reasons. Sun Pharma wants to identify reasons for these fluctuations in realtime and limit invoicing of any suspicious orders or suspicious SKUs. Sun Pharma is looking at vendor partner who can help to setup a system for identification of suspicious orders and suspicious distributors. Data Input Data 3 sheets Sale972076 rows Div Category41 rows Site Category58 rows Combined Sale and Site Category datasets on ID ITEM_SER Div code Div CategoryLegacy 1relation Unique Identifier on Sale dataset ID Month State SiteCode CustCode Item_ser Data PreProcessing Column State and Cust_name cleaned without spaces Cust_code is a combination of Cust_name and City. For unique Customers Cust_name is considered Derived Period variables FY FYMonth FYQuarter FY MonthNo Insight 1 Distribution of of orders vs of customers 97.21 of customers ordered more than 5 times Suspicious behavior of customers ordering more than 400 times. Very less number of customers ordered more than 400 times. Insight 2 Cohort Customer Retention Insight 2.1 Cohort Average Customer Sales Insight 2.2 Cohort No of Transactions Insight 3 Pareto Analysis Item_ser Site_Category Insight 3.1 Pareto Analysis Div Category Cluster Category Insight 3.2 Pareto Analysis Therapy Division

Sun Pharma RFP_Fractal Response.pptx

Suspicious Order Monitoring System Fractal Analytics June 2022 01 02 03 04 05 06 Our understanding of your requirements Solution overview Overall Analysis of Sample Data Questions Queries Engagement model and Commercials Case Studies Appendix Content 01 Our understanding of the requirements Our understanding of the situation Background Objective Sun Pharma has more than 6000 direct customers who act as distributors and supplies Sun Pharma products to retailers hospitals semidistributors and other modern pharma trade channels. Out of these distributors few distributors have fluctuating monthly purchasing pattern due to various reasons. Sun Pharma wants to identify reasons for these fluctuations in realtime and limit invoicing of any suspicious orders or suspicious SKUs. Sun Pharma is looking at vendor partner who can help to setup a system for identification of suspicious orders and suspicious distributors. 02 Solution overview Fractal works with our clients across the Data to Decisions journey Ingest Organize Process Personalize deliver Discover Design Design Artificial Intelligence Engineering Design Thinking Data Engg. As A Service Machine Vision Conversational AI Business immersion Domain understanding Data discovery Stream data Stream data processing unit Trend detection BI reporting Databases Staged data Stream data staging unit Data harmonization Data transformation and load ETL Virtualization Monolith data management Distributed data management Data warehousing Visual exploration Storyboard narrative Advanced analytics Outlier detection Forecasting Recommendation system Optimization Device Apps DB Apps Fractals Solutioning Approach How to identify suspicious orders Product segmentation Distributor segmentation controlled and noncontrolled substances Crosswalks Pharmacy dataset at the zipcode level to population counts Identify pharmacies that have high levels of product purchasing If an order deviates substantially from a normal pattern the size of the order does not matter and the order should be reported as suspicious Ordering activity that is unusual relative to a peer group of similar customers Identify licensed distributors Periodical analytics to review ordering and behavior Prescriptions for large quantities of controlled substances for individual patients. Flag the order if it exceeds 6month maximum might be a normal variation too due to demand surge The ratio of controlled vs. noncontrolled quantities Abnormal combinations of products different product family etc Items outside of the scope of practice Orders with a large percentage of products from the same family Enriched deciles of each outlet by brand market basket and overall pharmacy size Few Categories for Suspicion Orders Illustrative WIP 03 Overall Analysis of Sample Data Scope Data information Input Data 3 sheets Sale972076 rows Div Category41 rows Site Category58 rows Combined Sale and Site Category datasets on ID ITEM_SER Div code Div CategoryLegacy 1relation Unique Identifier on Sale dataset ID Month State SiteCode CustCode Item_ser Data PreProcessing done by Fractal Column State and Cust_name cleaned without spaces Cust_code is a combination of Cust_name and City. For unique Customers Cust_name is considered Derived Period variables FY FYMonth FYQuarter FY MonthNo Analysis Insights provided using Power BI dashboard Customer Wise Trend Analysis Customer Analytics Deep Dive Customer Volatility Analysis Suspicious Order Snapshot WIP Analysis ongoing 04 Questions Queries Questions Requirements from Sun Pharma 05 Engagement model and Commercials We offer various engagement models based on client requirements Key Terms and Conditions 06 Appendix Cohort Analysis Customer Retention Average Sales Transactions Pareto Analysis Item_ser Site_Category Insight 3.1 Pareto Analysis Div Category Cluster Category Pareto Analysis Therapy Division Powering every human decision in the enterprise Hiring Grooming Work Culture Hiring at scale 100k resumes Acceptance rate 0.52 Net Promoter Score Client Partnerships select examples I n v e st o r s Acquisitions P art n e r s h i p s Client Advocacy Accelerators solutions Global Recognition 2021 Fractal Analytics Inc. All rights reserved Confidential Todays challenges are complex. We need a new way of problem solving AI Analytics Algorithms that match or exceed human performance in a broad range of cognitive tasks Engineering Seamlessly connect data pipelines to automate decisions in real time Design Design for users emotions in their decision making make it simple intuitive effortless nonconscious We have clearly defined career tracks to drive deep capability and learning programs tailored to need Career grid 2.0 has six career tracks for scaled problem solving 3 superspecialized career tracks of Data Scientist Data Engineer and Designer 3 dualspecialty career tracks of AI Engineer Information Architect and Decision Scientist Fractal works with our clients across the Data to Decisions journey Ingest Organize Process Personalize deliver Discover Design Design Artificial Intelligence Engineering Design Thinking Data Engg. As A Service Machine Vision Conversational AI Business immersion Domain understanding Data discovery Stream data Stream data processing unit Trend detection BI reporting Databases Staged data Stream data staging unit Data harmonization Data transformation and load ETL Virtualization Monolith data management Distributed data management Data warehousing Visual exploration Storyboard narrative Advanced analytics Outlier detection Forecasting Recommendation system Optimization Device Apps DB Apps Fractal serves Insurance clients on AI topics across the value chain including 8 of the top 15 global PC insurers UW Pricing Product Mgmt Claims Servicing Marketing Distribution Customer Experience Examples of our work Fractals suite of accelerators allow rapid deployment of AIdriven solutions at scale Deep learningbased solution to identify unique investment opportunities in financial markets Diagnosing disease based on healthcare imaging data to recommend personalized treatment plans An AI backed algorithmic suite to enable businesses to spot explore and exploit anomalies and patterns that have bottom line impact Redefining experimentation conducting multiple trials to rapidly test and validate ideas to enable better decisions Deep learningbased solution for strategic revenue management to generate optimized promotion calendar on defined business goals and constraints Machine learning solution to drive deep understanding of customer behavior from their transaction and interaction footprints Triedandtested methodology for rapidly identifying the conscious and unconscious drivers of client behavior and designing interventions Automated Insights for Digital Evolution removing friction points in the customers digital journey Delivering realtime analytics at your fingertips providing right information to the right person at the right time through nextgen selfserve analytics Framework for the ingestion of unstructured data through Natural Language P rocessing

Super retail group - 6th Sep (1).pptx

Super Retail Group September 22 01 02 03 04 05 Problem statement Solution construct proposed including sample mockups Relevant case studies Gildan PG DSG maybe showcasing optimization capability in fulfilment execution space Data requirements Tentative timelines Agenda 01 Problem statement Ordering and flow planning efficiency for optimal inventory replenishment Validating our understanding of problem statement WIP Improve customer service and bring efficiencies in product flow planning through targeted analytic interventions enabling intelligent sourcing execution decisions Products are suboptimally grouped optimally in purchase orders with limited consolidation Purchase orders placement to container packing efficiency is manual and solely at the discretion of consolidators Objective Challenges Desired outcomes Intelligent sourcing execution decisions Optimized product grouping in PO placement to meet the net requirements at DC Product flow prioritization during container consolidation and during container movement from port to DC to maximize service within constraints of labor fleet container size etc. User experience and scalability User experience for seamless integration into existing buyer workflows Data architecture from ease of scaling of MVP Little prioritization of product flow resulting in SKU stack up at DC adding burden to limited storage capacity Notes captured by Sanaul JDA system does the demand side planning OK no support required. Issue is primarily on the supply side where Purchase Order has incorrect product grouping and does not account for constraints Some of the issues with PO placement identified are Multiple different lines on the same PO with different time for planning coming into DC Container capacity constraints Products arriving early that need to land later Products get prioritised basis gross profit and are classed into A B and C priority. Other factors considered are promotions opportunity buy Leeway at DC to readjust 3 days. Predeparture couple of days when less than a container load 02 High level solution approach High level solution construct Intelligent Optimization Models Analysis of ordering patterns to group items as per inventory need for efficient storage utilization Intelligent mapping of orders to suppliers Optimize product flow from ports to distribution center Robust data engineering Consolidation of orders and supplier performance data along with master data e.g. routing flow planning constraints etc. Staging of datasets in robust and scalable data models Automated preprocessing and validation routines before model consumption Intuitive user experience User personas and detailed user flow User experience design Embedding application in existing procurement and warehousing process Data engineering Optimization models User Experience WIP Improve sourcing execution Analyze time phased net requirements at DC and create optimal orders for suppliers Optimize the container build during freight consolidation to maximize service based on lead time PO priority container dimensions etc.   Intelligent optimization Problem solving approach Prioritize the port to DC product flow in optimal fashion that serves the inventory needs at different distribution centers within the constraints of labor fleet capacity etc. Optimal ordering Order prioritization for container consolidation Optimize product flow from port to DC 1 2 3 Order placement Order Receipt Cloud architecture Data engineering Optimization models User Experience illustrative APAC DEs cloud and platform generic architecture Smart Planning Execution Intelligent Optimization Optimize purchase order placement Optimize for mapping products to Purchase orders by analyzing various supply and demand side constraints Develop composite ranking of products to suppliers based on delivery performance reliability lead time price etc. Develop intelligent mapping mechanism to assign products to purchase orders to containers based on cost and packing efficiency Analyze sellin pattern Analyze sellin patterns from distribution centers to regional warehousescustomer stores Decode behaviors and trends based on volume and velocity of items shipped e.g. best sellers fast movers slow movers etc. Consolidate demand based on inventory needs i.e. seasonality holiday calendars forward deployment needs substitutions promotions etc. Optimize product flow Optimize the product flow from port to distribution centers maximizing service and efficiency and minimizing cost Prioritize orders assign destination DCs based on constraints such as labor space and available fleet etc. Simulation to understand the impact of loss in sales vs profitability to better sequence the containers. Data engineering Optimization models User Experience Analyze Sellin Patterns Association Analysis Analysis and modelling of shipping and inventory data sources to get key drivers of product flow Association analysis to understand how and which products flow across the network Monitoring of shortterm to longterm identified patterns to classify repetitive behavior and insights on optimal consolidating candidates Understanding behavior What items are frequently orderedshipped together from distribution centers How are downstream centers purchasing and utilizing the demands How are s easonality trade promotions substitutions cannibalization among other factors impacting association across the portfolio Consolidating patterns Simulation to evaluate associated tradeoffs for alternative strategies on different candidate product bundles Factoring the impact of seasonality promotion and other factors in simulation to generate upcoming insights Consolidation and recommendation products based on overall simulation scores and business criticality Analyze SellIn Patterns For the distribution network analyze the historical sellins to observe product movement patterns Data engineering Optimization models User Experience Optimal Placement of POs to Containers Smart allocation of the Purchase Orders into containers to maximize the packing efficiency To ensure the containers are filled full and efficiently container dimensions product dimensions container type etc. will be considered Product to PO Mapping Optimally group the product demand into Purchase Orders with the objective of minimizing the cost and time and maximize availability Various supply and demand related constraints such as supplier EOQ lead time MOQ of DC Safety stock price will be considered Scenario Planning Conduct Simulations to view changes in cost and time Assess tradeoff between consolidation of DCs over individual DCs by considering changes in supplier and container constraints which will help to strategies the orders Optimize Purchase order placement Data engineering Optimization models User Experience Optimize purchase order placement I ntelligent mapping mechanism to assign products to purchase orders to containers based on cost and packing efficiency Supplier Constraints Location Constraints Time Constraints Cost Constraints Demand Expected Delivery Date Product Optimization What if Scenarios Order Placed to suppliers based on the demand inventory and excepted delivery date Supply and demand related constraints Group demand into POs to optimize to achieve minimize cost time Sensitivity analysis to compare various scenarios based on cost quality and speed Genetic Algorithm Branch and Bound Optimization Neural Networks Constraints Demand Sensing Analyze demand of DC DCs Variability in product Projected Sales Optimize Purchase order placement Using optimization models to consolidate products into POs  Container Information Use container information such as container type container dimensions i.e. volume and weight constraints. Pallet information quantity will be a factor Product Dimension Use Product Dimension data to analyze size type Weight volume etc. For each product Packaging information to understand if single double stacking is permissible Container Utilization The Objective is to maximize the number of pallets that can be loaded to improve utilized container capacity Ensure complete space utilization by also considering the constraints such as pallet stacking Height constraints container constraints etc. Use Optimization technique to map each purchase order to appropriate container based on Container dimension Inventory needs PO priority Pallet Size Qty etc. Leveraging AIML to intelligently map POs to Containers Inventory Assessment Understand the inventory needs on hand inventory safety stock of each DC Identify priority POs based on the demand Shipment Data Shipment data such as the source destination info start date end date the estimated time taken trade policies ship type number of containers etc. Freight consolidator information will also be considered Optimize Purchase order placement Optimal solution Product to PO Mapping PO to Container Mapping Module 2 Optimization of Product to Container mapping Module 1 Optimization of Product to POs mapping Output Optimization layer Designed Mathematical Model considering the objective Constraints and business requirements Obj1 Min. Cost Obj2 Min. Lead Time Obj3 Max. Service Level Optimize for the selected multiobjective function and measure impact on supply chain KPIs using Gurobi Optimization solver Input Parameters Demand Inventory Supplier Information Open Orders Container Information   Constraints Inventory on hand Supplier capacity Container constraints dimensions Product Dimensions Pallets Qty Lead times Inputs Constraints Designed Mathematical Model considering the objective Constraints and business requirements Obj1 Max. Container Packing Efficiency Solving the mathematical model using Gurobi Optimization solver optimizer Optimize Purchase order placement Execution engine inputs Container Purchase Orders Products Quantity etc. DC Current Inventory Demand Safety Stock Port Detention charges demurrage free period etc.. Prioritizing the right product Priority score will be calculated considering the Inventory on hand average daily sales future demand and safety stock Create a relative normalized score of each product available at the Port Sequencing of the Containers Aggregate the normalized score of each product in a containe r to create container score Containers normalized score will be used for sequencing across DCs. Scheduling of the Containers Scheduling the movement of containers across various days Scheduling will depend on the vehicle availability time taken from Port to DC port constraints etc. Optimize product flow operations Optimize sequencing and scheduling of the containers from Port to different DCs Optimize Product Flow Data engineering Optimization models User Experience Dynamic sequencing through predictive algorithms Container Sequencing Outcome Calculate normalized priority score for each product Calculate Aggregate score for each Container depending on the SKUs composition Sequenced container flow subject to operating conditions Container Dynamics Demand Inventory Dynamics Product Dynamics Container Priority Quality checks time Resource constraints Port dynamics Projected sales Demand forecast Promo events Returns exchange Similar products Priority score calculated considering the Inventory on hand average daily sales future demand and inventory norms For demand factors simulate risk profiles to capture variance Leverage modelling techniques like Random forest SVM NN to predict container ranking Optimize Product Flow Dynamic scheduling through optimization algorithms Trade ins Quality Checks Network disruptions External Factors Allocation optimization Inputs Optimization engine to Maximize Priority Penalty functions basis container score detention charges waiting times resource availability. Additional business heuristics to make account for inventory levels dc constraints and other external factors Constraints Port Congestions Container  Scheduling optimization Container Priority Seasonality Existing inventory Availability constraints DC Constraints Normalized scores Port Constraints Optimize Product Flow . Port A DC information SEQUENCING First Stage output is a sequenced container flow subject to demand and operating conditions Input Prioritization Sequencing Output input to Scheduling stage SECHEDULING Optimization Scheduling the detailed movement of containers to meet demand of each DC and minimize cost by taking port constraints into consideration Sequencing Scheduling in action Optimize Product Flow Demand Controls Safety Stock Reorder Points.. Review Monitor Order Policies Demand Controls Safety Stock Reorder Points.. Review Monitor Order Policies DC N Demand Controls Safety Stock Reorder Points.. Review Monitor Order Policies DC B DC A Product PO Consolidation PO Container Assignment Product Purchase Orders Mapping Purchase Orders Containers Mapping Port to DC Sequencing Scheduling Sequence and Schedule Containers based on Inventory needs Port charges Resources Availability Simulate for DC location etc. DC B DC C DC A Day N Day N1 Day N2 Day N3 Smart planning executional flow DC C DC B DC N DC C illustrative HORIZON OPPORTUNITY User experience Data engineering Optimization models User Experience illustrative Gildan inspired user flows for and sample mockups screenshots AED design Learn More 6.5Mn Incoming In vs. Out of Compliance 27 Containers 325 Open POs Flow Analysis 14 At Risk 19 In Trouble 67 On Track 210 days Schedule Product Pattern Analysis Product Order Correlation FSN Analysis Week 12 Week 24 Purchase Order Placement SKU Demand Default Vs Clustered DCs P roduct Distribution Default Distribution Default Each DC acts as individual entity Clustered DCs Analysis Across DCs Analysis Across SKUs SKU Demand against time Distribution center DC1 Distribution center DC1 SKU SKU 1 13 Cost Optimized 13 25 25 time Optimized 20 Container efficiency Optimized 20 Delivery Time Week 4 Week 15 DC 2 DC 3 DC 1 DC 2 DC 3 DC 1 Purchase Order Placement Demand Across DCs Default Vs Clustered DCs P roduct Distribution Default Distribution Default Each DC acts as individual entity Clustered DCs Analysis Across DCs Analysis Across SKUs SKU Demand against time Distribution center SKU 1 13 Cost Optimized 13 25 25 time Optimized 20 Container efficiency Optimized 20 Delivery Time SKU1 SKU 2 SKU 3 Distribution center DC1 SKU SKU 1 SKU1 SKU 2 SKU 3 Week 4 Week 15 Sequencing and Scheduling Date 2 0082022 2 1082022 Port All Sequencing Scheduling SNS SKU Normalized Score Container 3 Container 1 Container 2 Container 4 PORT Day N Day N1 Day N2 Day N3 C3 DC2 C1 DC1 C2 DC4 C4 DC3 03 Relevant case studies 04 Data requirements 05 How we will deliver Typical timeline for MVP Data Discovery Assessment Access Authentication Collection of Order Inventory historical data Ingestion and data cleansing Transformation of data to make it consumable Solution prototyping MVP Definition Conduct Workshop for Pilot scope and align on MVP Understand data sources MVP Development Deployment Development of Models Testing Training of Models Weekly connects with Business POCs on progress and Business alignments End to End testing of MVP UAT MVP GoLive UAT Business Validations Cut Over GoLive indicative Reference Sequencing of the Container C1 C2 C3 C4 Create a product priority score. Normalized Priority Score FnInventory on Hand Average daily sales Future demand Safety stock. Beautify Merge next 2 slides into one Scheduling Beautify Merge previous and after  slides into one Container SKU DC Mapping Inventory On Hand Average Daily Sales Future Demand Calculate normalized priority score for each product Container Sequencing Time taken from port to DC Vehicle Availability Port constraints etc. CONSTRAINTS Container Sequencing Scheduling INPUT Calculate Aggregate score for each Container depending on the SKUs composition Sequencing Scheduling Beautify Merge previous 2 slides into one Optimize inventory replenishment flow PO receipts Freight consolidation Ports Distribution centers Supplier Constraints DC capacityStorage Labor Container capacity Lane fleet availability Flexibility within FAK rates Detention free days Port to DC transfer window 23 days Inventory stock and hand Product flow Flexibility for optimization Objective Prioritize PO flow for maximizing service and minimize cost Objective Maximize service Minimize transportation cost Minimize warehouse cost and port demurrage costs PO What we heard Patrick Group orders for location to improve service and reduce costs MOQ is out of scope Prioritize what is in the container to DC Bring what is needed early. Example Currently Wk. 1 is mixed with Wk. 38 resulting SKU stack up at DC eating storage capacity. Can we use data to decide what should arrive at DC There could be some room to work with freight consolidators within FAK rates Sandeep Combine line items in PO in different containers based on priority Melinda Currently PO gets raised automatically can the recommend DC transfer instead Schematic Supplier Freight consolidator Ports SRG DC network Supplier 1 Supplier 2 Supplier 3 PO1Item 1 PO2Item 2 PO3 Item 1 PO4 Item 2 PO5 Item 1 PO5 Item 2 Trucking and carrier selection SRG has no say in freight consolidators decision of ocean corridor selection Port 1 Port 2 Distribution center 1 Distribution center 2 Distribution center 3 Scope of optimization PO1Item 1 PO2Item 2 Prioritize trucking and day of shipment within the flexibility of 3day window and detention free limit at port Use case 1 Port 2 to DC 3 Vs. DC2 to DC3 Port 2 to DC 2 Use case 2 Distribution footprint Sequencing and Scheduling Sequencing Provide optimal sequencing of containers to move from port to DC Calculate a normalized priority score to rank each container based on QtyDemandStorage of DC Scheduling Essential for planning and executing the movement of containers Timeport to DC port constraints Detention charges Mode of transport availability Overall Construct Setting up the solution framework for success Estimate an equation between Sales Volume and Price for each Product at weekly level. Both Sales Volume and Price is dynamic and depend on each other. Very high price will reduce the sales and very low price will reduce the gross profit. Factors considered Current Price Previous Price Original Price Cost Cyclic temporal Features Week Month Quarter Country Dynamics Channel Dynamics Forecast Sales for each Product at weekly level using historical Demand data using Machine Learning Factors to be considered Lags of Sales Mean Encoding Weighted Past Sales Price Trends Product Hierarchies Cyclic temporal Features Week Month Quarter Optimizing Current Selling price w.r.t Sales Forecast Constraints to be considered Volume between 100130 of Forecasted Volume Current Selling price Between Cost to Original Selling Price Optimize the Total Revenue Recommend Weekly price based on forecasted sales for 13 future weeks Modify slide to fit content Keep less technical Dispatch STR Date GTIN Ship from Ship To Consolidated Available dz Units On Hand RQ Dispatch details Inboundoutbound Shipto Customer Order GTIN SoldTo ShiptoCustomer PO DC Order line Order Number Open RQ Open USD Order Maintenance GTIN Part No Quantity Pallets Cube Total Weight Weight Constraints Warehouse constraints capacity docks inventory Cubing Constraints Mapping data GTIN Product master PLM master Size Batch Shelflife line orders constraint Cartons pallets constraint Load builder Output Load No GTIN Ship from ShiptoCustomer SoldTo Quantity Weight Capacity Item No. product group Size Color Priority Truck Number etc. KPIs Holes fill rate per truck versus non holes SKU SKU Count per truck Load value Load Mix Truck Avaibility size route constraint Defining the constraints required. Assigning penalties or rewards Running the optimization algorithm Defining the objective Functions Open order data Master data and constraints layer Data Harmonization Optimization Engine layer Publish layer Consumption layer We will consider multiple layered approach to arrive at solution Stock Production GTIN ShiptoDC Open RQ Priority Production plan Batch Throughput lot size Stock cover Production plan GIT Replenishment Shipment Cost breakup Item Shipment volume DIFC Min Max boundary Shipments Lanes CDCRDC details etc. KPIs Inv and WH KPIs etc. Cube Master Palletization Floor Load Volume Slip Sheet Case Dimensions SKU Master TMSWMS master LanesRoute Lot size WH capacity Location master Carriers Lane constraints product grouping carrier constraints Module 1 Supply Planner LogOps team Shopfloor team Modify slide to fit content Keep less technical By combining visualization and monitoring techniques we are able to make business interpretability more accurate for our clients Use rich visualization techniques to deep dive the data. One stop roof for effectively map visualize and monitor the models and forecasted numbers. Further help in understanding what if and what else scenarios to make business interpretability more accurate. Answer Business Question Input Data Extract Answers business questions and facilitates decisions making on demand planning performance reporting. Keep Track and Fasten the Decision Making .Fastens the decision making for Demand Planning Department . Allows user to optimize future scenarios to achieve business objective. Accurate and seamless integration of data Gauging the impact efficacy of demand planning performance and manual overrides of forecasts. Recalibrate Schedule Recalibrating above schedules based on real time perturbances to ensure automation of data collection data processing aggregation and visualization. Keeps Track of expected demand and coursecorrects for reports. Accurate and seamless integration of multiple data sources in demand planning dashboard Input data extracted automatically from system. Modify slide to fit content Keep less technical DC1 DC1 DC1 Day 90 Orders are placed to suppliers based on inventory required EOQ Destination DC Receipt date JDA supply plan based on Demand at DC SS and service policy Lead time Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5 40 FT Day 15 Day 45 40 FT Optimization 1 Optimally build the container Container dimension Inventory needs PO priority Etc. Optimization 2 Prioritize containers Inventory needs Port detention Simulate for DC location Port to DC scheduling JDA and Oracle planning suite Supply execution optimization Ordering and flow planning efficiency for optimal inventory replenishment Ask Currently the items we need later in the season are mixed with priority items in the purchase order resulting SKU stack up at distribution centers eating away valuable storage capacity Develop a mechanism for prioritizing containers to DC based on inventory needs within constraints of storage equipment fleet and labor Develop scenariobased simulator to recommend DC transfer vs. order placement Assumption MPC Assignment Optimal placement of Material to PO to Container MultiStage Assignment problem User Input DC Consolidation Time Priority Supplier Constraints EOQ Safety Stock Lead Time MOQ Stage 2 Input Minimized Cost Minimized Time Container Constraints Capacity Dimensions Hazardous Non Hazardous Time Output Container Efficiency Result Optimal placement of POs

Supply chain case studies AIML Azure.pptx

Supply Chain case studies Azure AIML January 2023 01 02 03 04 Customer delivery certainty Load build optimizer SKU Phase Out Predictive Quality Topics Increased service levels by predicting delivery reliability and enabling early interventions Near real time risk monitoring of all deliveries Optimal delivery windows to maximize service levels Alerts and recommendations to enable early interventions Leverage Bayesian networks to capture interdependency of processes Determine on time risk probability throughput the outbound delivery life cycle Determine optimal appointment windows to increase on time delivery reliability 8 improvement in service levels Automated risk tracking at shipment level to reduce manual efforts by 80 Client ask Solution Result Technical architecture Service level prediction Data Level Security using Azure Active Directory Group Enterprise Data Lake and Processing Authentication against Client Az AD Data Ingestion using Data Factory OutputInsights Data Foundation Data Lake Data Bricks ADF AAD PySpark Data Workbench Data Cleansing Processing KPI Creation Azure Blob Storage DevOps Azure DevOps CICD pipeline for migration GIT Versioning Orchestration Data Validation Notifications Logic Apps Modelling Process PySpark Modelling Workbench Analytical Modelling Attribution Modelling Data Bricks On time risk Predicting risk for on time delivery across delivery milestones Federated Data Model Client Azure Suite of Applications Outbound scheduling Optimize pickup schedule to maximize OT for all outbound orders Optimal windows Predict optimal pickup and delivery windows to maximize OT Warehouse monitoring Monitor warehouse metrics and alert on outliersdeviationstrends Docker React.js Automated refresh Source system SAP Data for CSOT Carrier and other mappings Source system TMS Data Sources GPS data 10 Improved dock capacity through dynamic dock allocation Visibility of docks based on shipments for t30 days horizon to enable better operations planning Capacity dock management Process improvements for complete dock capacity utilization Automate approvals for overbooking or early delivery requests Project daily Shipment volume Loads Containers dock door capacities Determine time to load outbound containers based on load complexity store ready load routes etc Hourly visibility of dock door capacity utilization Dock door Optimization Schedule determination 810 capacity gains through optimal number of docks work force Reduced site controlled CSOT by 1.5 2.5Mn reduction in Transportation work force Client ask Solution Result Technical architecture Dock optimization Data staging Data extraction and synchronization Final output UI Data storage Authentication Admin Standard user Blob Data lake gen2 Webapp Active directory Key vault Data factory Source system Writeback Forecasting 10 improvement in service levels by optimizing POs consolidation into outbound loads Improve fill rates of trucks while maximizing for revenue service level and margins Automate data consolidation and order priority calculation Create intuitive UI layer to analyze optimized output and writeback to source systems Multi objective optimization with user input to determine objective priority Dynamic constraints with option for user overrides Load metrics to help user approve the output and write back to ERP systems Improvement in revenue per truck by 30 Service level by 10 and truck fill rate by 5 Flexible userdriven ability to change objective functions and monitor impact Eliminate manual effort thus faster processing time. 90 reduction in time Client ask Solution Result Technical architecture Load build optimization Manual Automated refresh Source system SAP Data for cost time and other mappings Source system OMSWMS QlikView Blob Storage Data Factory Data Lake store Databricks SQL DB Data Sources Data Staging Data Ingestion Raw Data Analytics layer Output data Batch Input Logic for writeback automation Writeback to OM system User Input Assumption Writeback via API OM team to provide connectivity Optimization engine Data preprocessing Feature rich UI Scenario analysis Scheduled refresh Prevented excess production of SKUs by predicting when it will enter the decline stage of the life cycle Generate insights on SKU decline pattern leading to excess inventory manufacturing Recommend inventory redeployment opportunities across the network Enable proactive decisions for SKU delisting Portfolio optimization Predict SKU Life cycle stage and map the current life cycle Predict beginning time period of phaseout based on sales inventory patterns and similar products features Recommend deexpedite strategies and production master data settings to increase utilization of running OH WIP inventory Predicted SKU Phase Out Load 618 months in advance Identified rebalancing opportunities in bw regions Systematically deexpedited production qty to prevent excess Client ask Solution Result Technical architecture SKU Phase Out 3Mn reduction by avoiding product off spec Identify the root cause leading to issues with firmness of product Provide recommendation on how to control the various controllable factors to produce products as per firmness specification Critical process parameters for Golden batches Developed supervised learning drivers models to identify the significance of critical tags impacting product firmness Enabled SOP enhancements alerts for course correction 3Mn reduction by avoiding product off spec reducing rework product waste Improved Customer satisfaction Client ask Solution Result Technical architecture predictive quality Function App Analytics Layer Output data written back Web App UI Layer Static Data Streaming Data Azure Cloud Architecture Databricks Data Ingestion

Tetra Pak Use Cases.pptx

Tetra Pak Supply Chain July 2022 Fractal Team 01 02 03 Predictive Maintenance Spare Parts Inventory Optimization QA Agenda Improve Demand Forecasting of spare parts based on Consumption Criticality demand patterns Optimize Inventory norms balancing stockouts and excess inventory with improved forecast and supply reliability of parts Mitigate machine failures through anomalous clustering of behavior and integrated insights on prescriptive maintenance Identify potential opportunities of minimizing unnecessary maintenances and improving signaling on planned outages Fractals point of view on specific use cases of Predictive maintenance Spare parts optimization A Predictive Maintenance Dynamic behavior assessment for machines flows to predicting disruptions Data aggregation at cyclebatches and time level for temporal parameters KPI creation Time at bottom top mean percentiles time to attain maximum time between failures etc. Segmentation of machinesparts based on overall performance scores and business criticality Classification and Identifying anomalies by introducing thresholds stress testing of flowsentities basis the standard operating principles Crossvalidate with business on identified anomalies Finalize threshold based on business inputs Analysis and modelling of past data sources to get key drivers of disruptions Prediction of failure through Classification Heuristic Time series random forecast Lag based models to predict failures N days in advance Failure mitigation and contingency plans Tactical and operational contingency plans to avoid unnecessary maintenance costs Simulation to evaluate associated tradeoffs for alternative strategies on probability and window lengths Monitoring of shortterm and longterm failure incidents to classify repetitive behavior Alerts of upcoming disruptions delays and other events to corresponding stakeholders involved Recommendations of actionable insights and contingency plans for alerts and triggers and potential cost impacts associated Segmentation of machines and flows among quadrants as per cost impact and failure profile Recommendation of operational changes for each machine to optimize risk. Ex Multisource spare part procurement avoidance of false maintenance early signals for planned maintenance etc. Enabling smarter machines for manufacturing process Predictive Maintenance Simulation Sample Dashboards Unsupervised anomaly detection and prediction for printers Key Features indicating printer performance Result 662 anomalies for 26 printers for the entire year Key indicators Ink consumption as of print area and aborted jobtotal Jobs There are unscheduled machine failures resulting in loss of printing time ink revenue and high operational expenditure Identify and predict anomalies in advance for proactive scheduled maintenance Minimal understanding of printer processes Asynchronous data with different levels of granularity ranging from millions of data points a day to 1 data point a week Data points missing completely for days Identifying features impacting printer performance Develop an unsupervised anomaly detection model using adaptive distribution at dailycomponentprinter level and survival models to predict time to failure Situation Challenges Solution Unsupervised anomaly detection for gearbox using sensor data Reduced batch cycle time through discrete event simulation To simulate for most optimal batch cycle times with achievable combinations of parameter metrics schedules Simulator with statistically viable and robust solution to ensure that historical anomalies leading to longer Batch Cycle Times can be avoided in future Reduced number of Change overs across manufacturing lines Outcomes Multiscenario matrix of CPPs using Monte Carlo Simulation Genetic algorithm Simulation to find best possible combination Simulate CPPs and assess the impact on BCT Comparison with BIC batches Optimize BCT Monte Carlo simulations of risks for the optimal BCT relevant CPPs Genetic algorithm for integrated Manufacturing Scheduling Identify best possible combinations of CPPs to attain optimum BCT Simulate CPPs using conditional probabilities from BNN modeling B Spare parts forecasting Inventory optimization Demand forecasting and inventory optimization of parts through a 3 step approach 2. Demand Forecasting Forecasting at FacilityPartSegment level to train risk elements which would otherwise be lost in generic model Forecasting of parts with factors like historical consumption average Life time avg. daily operations time etc. 1. Parts Risk Classification Criticality score calculated at FacilityPart level based on frequency of usage rarity stability in supplier service levels etc. A second dimension is added to inventory segmentation based on the type of demand supply variability and intervals 3. Inventory Recommendations Predict Safety stock DFC basis demand supply volatility lead times EOQ at Facility levels Optimize recommend minmax ranges to balance stockouts and excess inventory Aggregate recommendations to region level for calculating CAPEXOPEX spend Improved demand forecast of parts through criticality segmentation AIML interventions Criticality score of parts Segmentation of parts Demand Forecast of parts Dynamic Sequencing through predictive algorithms Inventory Norms Outcome Recommended safety stock Dynamic calculation to enable early interventions Scenario planning Driver analysis for all components of supply and demand Sequencing of Containers Scheduling of Containers Supplier reliability Fulfilment rate Quality checks Leas time variance Supplier capacity Actual sales Demand forecast Promo events Returns exchange Similar products Calculate historical supply related KPI at required time granularity For demand factors calculate risk profiles to capture variance Leverage modelling techniques like Random forest SVM NN to predict safety stock Automated e2e forecasting inventory optimization for spare parts Forecast the consumption of parts required to run operations Optimize Safety Stock for the parts without compromising on Service levels criticality e2e automated integrated solution AIML modelling to dynamically understand temporal behavior of demand and consumption Insights and recommendations enabling actions on various inventory and stocking parameters Automated planning process 73 acceptance ratio with 4 performance across different business units category types 2Mn savings across 30 of Spare parts accessories globally Huge potential savings with scale Client ask Solution Result Forecastability statistics Stocking recommendations Sample Dashboards Released 261Mn Inventory reduction of spare parts Business Use Case Solution Approach Solution Benefits 1 Out of Compliance 67 Audit Yield In vs. Out of Compliance 2 In Compliance 3 Total Audit Items 2 reduction in Inventory through improved demand forecast of spare parts Demand forecasting of service parts and optimizing inventory levels. Scrapped inventory contribution was USD 4.5MM Problem statement Solution Approach Solution Benefits Solution Benefits Overall potential inventory savings AUD 159683 Overall potential inventory savings AUD 3236 Reduced 40 of Safety stocks by enabling visibility at optimized service levels Poor service levels despite high inventory levels Static Inventory targets thresholds Optimize safety stocks at DC level Business Case Solution approach Inventory control tower to explore hidden patterns from the data to identify pain points and opportunity areas for the business to work on A model predicting safety stock quantity based on historical demand error shipments order quantities service levels replenishment quantities Gauging the impact efficacy of predicted safety stock quantities on the service levels and recommending min max inventory norms Decision treebased model for prediction of safety stock quantity Monte Carlo simulation technique to run multiple scenarios Early warnings decision cockpits for insights deep dives Demand variables Supply and customer service variables Our approach to model demand and supply volatilities to determine inventory norms Data sources Optimization Input variables 1. Predicted Safety Stock Quantity 2. Target Days Supply 3. Updated Demand forecast 4. Actual receipt quantities Output Variables 1.Revised Safety Stock Quantity 2. Revised Target Days of Supply 3. Beginning Ending inventory 4. Days of Supply 5. Target Stock Constraints MOQ Production Fill Rate CTS Capacity Holding cost Objective function Optimal inventory to carry replenishment quantities between DCs Codified adhoc business rules on DRP E.g. Factory is open only on specific days Fusion flow Pull flow Push flow Model iterations and optimization Random Forest model 1. Running model on training dataset 2. Running model on test dataset Output Safety stock quantities Calculation of proxy safety stock target variable for the model Model iterations Dependent demand MOQ Production fill rate Compliance to schedule PurchaseProduction Orders Predicted Target days of supply Predicted Safety Stock Inputs Constraints Evaluating safety stock by simulating inventory policies Tool simulates inventory consumption over 13 weeks considering policies s S Inventory Policy S Max Stock Rolling average N weeks demand forecast Predicted safety stock s Safety stock Predicted safety stock qty Replenishment qty S Ending inventory in multiples of MOQ r Q Inventory Policy Q Order Qty MOQ or in multiples of MOQ r replenishment point Predicted target days of supply Replenishment qty r Q Ending inventory 1 2 Output Revised Safety Stock Quantity Revised Target Days of Supply Beginning Ending inventory Days of Supply Target Stock Simulation Optimization W e need access to supply chain data to deliver the MVPs Illustrative Create training models and the required infrastructure to develop the solution Source data collection from system of records Raw Data Ingestion into Azure Data Lake Store n ear real timebatch Data Transformations Harmonization and model building Storing model output in ADL Generate insights reports from modeled data on Azure Deployment of recommendations into existing systems Source Data Azure Data Bricks Azure Data Factory ADLGold 1 2 3 4 5 a b Selecting the best model c Placing the selected model in registry ADL Silver L1 L2 AZURE SYNAPSE Serverless SQL Pools Illustrative Reporting Layer Internal Systems Predictive maintenance for varied time to failures

Toll Demand Sensing - Final.pptx

Toll Demand Sensing September 2022 2022 Fractal Analytics Inc. All rights reserved Confidential 01 02 03 04 Agenda Our understanding of the problem statement Solution approach Data requirements Mockups 01 Our understanding of the problem statement Improving inventory control through shipment forecasting and inventory recommendations Requirements To improve shipment forecasting to establish the right quantity available at the right time for the customers To optimize inventory management through safety stock and reorder point recommendations Challenges Low demand visibility for products leading to lower service levels Reactive approach to shipment failures Lack of streamlined inventory policy No standard KPIs to monitor goals and guide decisionmaking Outcomes Avoid losses due to low CSL through better demand sensing Enhanced visibility on inventory and ordering Actionable insights and shorter time to decision Move from reactive to proactive decision making 02 Solution approach Enabling better inventory guidance through these three elements Market Sensing Demand Forecast Segmenting the SKUs based on demand patterns Demand sensing and forecasting using historical data and external variables Shortterm to Longterm multihorizon forecasting Simulation with internal and external factors Inventory Monitoring Visualize current inventory levels across various levers and granularities Realtime monitoring of inventory health through trends and reports KPI tracking and alerts Identify opportunity areas for improvement across SKUs DCs segments Inventory Recommendations Inventory policy and EOQ guidance Safety stock recommendations Determination of optimal reorder points Whatif analysis to arrive at optimal quantity to keep and order Inventory Visibility Visualize current inventory across SKUs brands categories regions inventory segments DCs Realtime stock monitoring through ageing reports and SKUlevel yearonyear comparison Automated KPI creation and tracking KPI development benchmarking and monitoring for KPIs like inventory turnover rate backorder rate stockouts etc. Custom notifications to highlight problem areas based on KPIs Deep Dives and Root Cause Analysis Deep dives and diagnostic capabilities for root cause analysis Inventory trend and analysis with orders shipments and cut quantities for better exception assessment Nudges and Alerts Nudges for inventory changes and anomalies Generating alerts for drop in service level with the reasons for drop Early warning signals for expected stock deviations A single source of truth to enhance visibility Inventory Control Panel Leveraging AIML to Sense and Forecast Demand Demand patterns Seasonality trends demand variability Business decisions Changes in promotions price closeouts External factors Port congestions holidays inflation Time Series Forecasting Accurate Forecasts with Demand Sensing Shortterm to Longterm multihorizon forecasting Segmentation Classification of products based on demand patterns Using business rules statistical methods and AI and ML techniques for segmentation Sensing and Forecasting Demand sensing using holidays macroeconomic factors business decisions etc. SKU level forecasting based on identified segments and external variables Simulation Whatif Analysis for adjusting forecasts using business constraints internal and external factors 1 2 3 Tailormade segmentation approach based on historical demand data Using AI and ML methods Similarities in demand time series can be identified using Dynamic Time Warping Segments can be created using Clusteringbased methods Using Statistical methods Using Average Demand Interval and Coefficient of Variation Demand can be segmented into Lumpy Erratic Smooth and Intermittent Using Business rules Products are classified into category ABC based on Volume of the products Products are classified into category XYZ based on the forecast ability Tailored segmentwise Demand Forecasts at the right granularity Inventory POS Data Promotions Holidays Macroeconomic factors Product Hierarchy Seasonal factors External Variables Internal Variables Time Series Forecasting Product Segmentation Statistical methods AI and ML methods Business rules Demand Engineering Lagged demand Interval between demands Holidays macroeconomic and seasonal factors as features Inventory to Demand Ratio Baseline demand To be generated using reference product Existing Product New Product Shortterm to Longterm multihorizon forecasting at SKULocationSegment Level Aggregated forecast at DCSKUGeography level Fast prioritybased response management through multihorizon demand simulation React and adjust to short and longterm changes in demand Compare Original Forecast vs Recommended Forecast vs Simulated Forecast Change in Simulated vs Recommended Demand Internal Factors Constraints External Factors Simulation Inventory Port Congestions Network disruptions Climatic changes Macroeconomic Indicators Market Sentiment Closeouts Seasonality Price SOP Cannibalization Demand Cost Storage Lead time Optimize Inventory Through Dynamic Safety Stock Inventory control to explore identify pain points and opportunity areas A model predicting safety stock quantity based on historical demand Gauging the impact efficacy of predicted safety stock quantities EXPLORE PREDICT SIMULATE Decision treebased model for prediction of safety stock quantity Monte Carlo simulation technique to run multiple scenarios Early warnings decision cockpits for insights deep dives Demand Forecast Purchase Orders MoQ Shipments Supply Lead Time Customer Service DoH Demand Variables Supply CS Variables Predictive Model Random Forest Training Data N Observations Sample 1 Sample 2 Sample N In Bag 1 Out of Bag 1 In Bag 2 Out of Bag 2 In Bag N Out of Bag N Average of Single Tree Prediction Optimization Monte Carlo Simulation Pred 1 Pred 2 Pred N INPUTS Demand Forecast Dynamic Safety Stock Calc Current DoH Target DoH CONSTRAINTS DC Capacity Lead Time MOQ Optimum Inventory Policy Selection Process 03 Data requirements Data requirements Data requirements 04 Mockups Inventory Control Panel Logo 0 100 10 20 30 40 50 60 70 80 90 0 100 Fill Rate Forecast Accuracy Backorder Rate Days on Hand XX days Orders at Risk XXYY Cut Quantity XX Understocking Issues Overstocking I ssues Understock SKUs XXYY Dead Stock SKUs XXYY Overstock SKUs XXYY Overstock cost XX 10 20 30 40 50 60 70 80 90 0 100 17 Alerts Logo XX Excess Inventory XX Dead Inventory XX Deficit Inventory Selection Control Inventory Control Panel Inventory Screen Demand Drivers Demand Simulation Inventory Risk Mitigation Plan Impact 21 SKUs are understock 12 are Overstock Forecast Bias and Accuracy must be managed properly Alert Mitigation SKU Count Stock Stock Outs XXYY POs Delayed XXYY Lost Sales XXYY Inventory Risk Forecasting Risk Supply Risk Alerts No Moving SKUs Other Risk Parameters Forecast Deep Dive Logo Monthly Weekly Y early Demand Simulation Logo Delta Simulated vs recommended 10.82 Seasonality Adjustment 4.8 Promotional Adjustment 5.5 Demand Shift Adjustment 6.5 Simulated Forecast Volume 580K Original Forecast Volume 480K Run Simulation Save Simulation Inventory Tracking and Simulation Logo A Distribution of Excess Deficit Inventory by Product Segmentation High Moving Medium Moving Low Moving Top 10 Products by Excess Days of Supply Vs PFR Top 10 Products by Deficit DOH Stock Target DoH Stock vs Forecasted Simulated Target Stock Simulation SKU MOQ Lead Time PFR Edit Interactions ABC X Category_1 123 Inventory Overview 12 8 45 Logo 15 320 Alerts Logo XX Excess Inventory XX Dead Inventory XX Deficit Inventory Selection Control Link To Inventory Control Panel Link To Inventory Screen Link To Demand Drivers Link To Demand Simulation Inventory Risk Mitigation Plan Demand Forecasting Risk Mitigation Plan Impact 21 SKUs are understock 12 are Overstock Stock Outs XXYY POs Delayed XXYY Lost Sales XXYY Alert Mitigation 21 SKUs are understock 12 are Overstock Alert Mitigation SKU Count Stock Inventory Overview 12 8 45 Logo 15 320 Inventory Management Logo Safety Stock 120 Inventory OH DoH Next 3M Demand Next 3M Recom Demand Inventory Simulation Logo Current OH XX MOQ XX Safety Stock 500 Target Stock 1300 Inventory Logo Current Stock Next 4 W Demand 2100 DOH Stock 980 Avg Lead Time 9 MOQ 210 Safety Stock 120 Analytical approach to solution construct simplify Predictive Solution Descriptive Solution Prescriptive Solution Visualize current inventory levels across various levers and granularities Realtime monitoring of inventory health through trends and reports KPI tracking and alerts Identify opportunity areas for improvement across SKUs DCs segments Segmenting the Product SKUs based on demand patterns Demand sensing and forecasting using historical data and external variables Shortterm to Longterm multihorizon forecasting Simulation considering internal and external factors Inventory policy and EOQ guidance basis of product types Safety stock recommendations as per classification of products Determination of optimal reorder points Whatif analysis to arrive at optimal quantity to keep and order Inventory Control Panel for inventory overview and regulation Market Sensing and Demand Forecast to arrive at the right quantity needed at the right time Inventory guidance and recommendations on safety stock and r eorder points Phi 3 step Inventory Overview Single source of truth for track and trace Realtime visibility monitoring control We envision a single source of truth for you through an Inventory Control Panel Inventory Control Panel Nudges Early warning indicators Course correction using thresholds Deep Dives Deep dives into siloed functions Performance and variance analysis Automated KPI tracking Monitor service level performance using KPIs Databacked insights and benchmarks Product Segmentation Low demand Low Perishability Low Demand Medium Price Slow moving Medium Perishability Low Demand High Price Perishables Fast moving High Perishability High Demand Low Price Bulk purchase Low Perishability Low Demand High Price Long term forecast based on historical demand Long term forecast based on reorder frequency replenishment count Products with lumpy erratic demand Forecast based on demand seasonality and quality P roduct s will be segmented based on demand pattern perishability price and volume to apply the best fit AI ML forecasting technique and inventory recommendation LOW MOVING SKU Medium Perishable Low Demand SKU Categorization Inventory Replenishment Strategy based on SKU Classification MEDIUM MOVING SKU Medium Demand L ow Perishable 02 03 Segmentation Set Dynamic Target Approach HIGH MOVING SKU High Demand Low Price 01 Identify parameters to classify SKU into the right SKU category Parameters could be Demand Lead Time Criticality SKU analysis based on Days on Hand Calculate the upper the lower limit based on SKU identification and heuristics approach SKU classificationbased on Inventory target into high medium and low criticality based on demand variability and Days on Hand Heuristics Based Calculation High Inventory Alert for Component Replenishment Med Inventory Low Inventory No Action SKU Classification Dynamic Target Inventory Policy Continuous Review Period Review MinMax Inventory Just in Time Tailored Demand Forecasts to the Right Granularity. Inventory POS Data Promotions Holidays Macroeconomic factors Product Hierarchy Seasonal factors External Variables Internal Variables Bayesian Inference Hierarchical time series modelling Product Segmentation ADI CV DTW KMEANS ABC XYZ Classification Feature Engineering Lagged demand Interval between demands Holidays as a feature Inventory to Demand Ratio Long term1218 months Forecast at SKULocationSegment Level Aggregated forecast at DCSKUGeography level Reference product Baseline demand for new product Demand Drivers and their impact Longterm forecast models based on different demand patterns. Improved forecast accuracy and demand responsive supply chains. Making forecasts more robust for shortterm dynamic inventory planning  by incorporating current events from external data sources. Longterm forecasts on sales historyPOS data adjusting for  Activity planning i.e. promotions sales contests and Launch planning i.e. new product introductions phase outs etc. Optimal inventory and better forecast accuracy after adjusting for changes in macroeconomic indicators. CSL Benchmark X CSL Trend Ageing Report Perishability Report Inventory Trend Delivery Report Back Order Report Stock Outs Inventory Tracking 2 Product and Category Deep Dive Status Reports Customer Service Level Enabling end to end customer service levels improvements Demand forecast mismatch Identify reasons for returns failed shipments Incorrect deliveries Planning errors at DClevel Provides insights on excess stock and wasted storage cost and  blocked capital. Helps manage products with short shelf life. Spots inefficiencies in the past inventory levels. How delivery efficiency can be improved A higher backorder rate tells you that your forecasting is inefficient  Aids to decide Reorder level and reorder point Provides understanding of the products demand variability Helps in classifying products. Weeks On Hand Replenishment Rate Volume Sold 3 4 1 Features Data Discovery to identify and integrate data from different sources Engineering Refine derive features to build a single version of truth Pipeline Enable dynamic Monthly data refreshes Tailored Demand Forecasts to the Right Granularity. State of the art algorithm Analysis Comparing Last year with current year forecast performance  Showcasing best performing product categories and channels w.r.t their forecasting error   Inseason vs Preseason forecast analysis  Trends and seasonality Product clusters Disaggregated Demand Hierarchical time series modeling Selcting Right Inventory Policy Through Continuous Monitoring Compare the values with the Target Significant Dev Insignificant Dev Re Assess Inventory Policy Products Following this process r educe average inventory overall cost stockout events