Opportunities for Predictive Analytics within the Procure-to-Pay Process
By: Suviti Chari
Tags: Predictive Analytics, Vendor Management, Procurement, Neural Networks, Procurement Strategy

Opportunities for Predictive Analytics within the Procure-to-Pay Process

1.0 Introduction to Procure-to-Pay

The procure-to-pay or purchase-to-pay process (P2P) is a structurally important pillar of any business. The procurement department of a business, on average, manages about 60% of total enterprise spending.(1) Put simply, the P2P process begins with the need of a certain good or service by the business and ends with the acquisition of the aforementioned material followed by payment received at the supplier end. Albeit simple in theory, the P2P process involves numerous lengthy steps where in a single order may sometimes take months from the point of order to the point of fulfilment and payment. The process overview flow chart can be further detailed into the following steps:

§ Identification of a Requirement

§ Authorizations of Purchase Request

§ Approval of Purchase Request

§ Procurement

§ Identification of Suppliers

§ Inquiries

§ Receipt of the Quotation

§ Negotiation

§ Selection of the Vendor

§ Purchase Order Acknowledgement

§ Advance Shipment Notice

§ Goods Receipt

§ Invoice Recording

§ 3 Way Match

§ Payment to Supplier

The maturity of Internet-supported supply chain process saw streamlining of this P2P chain. The development and implementation of automated e-procurement and enterprise resource planning (ERP) systems sought to reduce the total cost of acquisition and increase efficiencies with considerable success. Big ERP providers tend to embed e-procurement products within their ERP software.(2) The e-procurement value chain serves to integrate stages such as vendor and catalogue management, purchase ordering, order status, e-invoicing and e-payments among others. The simplification of the process, resulting in reductions in cycle times, frees up crucial time for procurement professionals to focus on clearing exceptions, building deeper supplier relationships and strategy formulation, as opposed to firefighting daily procurement issues — the routine, pen-to-paper tasks prior to the advent of such information and networking systems.

Companies that have adopted the e-Procurement technology found to save 425 in purchasing transactions costs due to simplification of the process as well as reduction in purchasing cycle time, which in turn increases the flexibility and provides more up-to-date information at the time of placing of purchasing orders.

A report consolidated by Aberdeen Group revealed that enterprises that had adopted e-procurement saw, on average, an increase in key performance areas such as spend under management, requisition-to-order costs, requisition-to-order cycles and percentage of maverick spend. The results are highlighted in Table 1. In addition to these factors, a 4.75% discount with suppliers was observed owing to more informed negotiations. (3)

These now commonplace systems of automation are far from perfect, and several limitations exist. So much so, that a recent economic crime study reports that 29% of companies are affected by at least one form of procurement fraud, making it the second most incident economic crime.(4) Additionally, Oracle Applications User Group ranked procurement process most vulnerable not only to fraud but also to waste and errors.(5) Glaring gaps exist that allow for inefficient procurement management as a result of a lack of knowledge, skills and incentives to reduce overall spending.

To skeptics P2P appeared to have reached the boundaries in terms of cost savings and efficiencies in key areas. However, with the looming, inescapable presence of disruptors namely AI, Machine learning, and Predictive analytics, a new wave of competitive advantage is unveiling.

2.0 Procurement Planning

2.1 Forecasting Analytics in Procurement Planning

Demand, price and choice forecasting is instrumental to continuous, sustained business success. The ability to plan proactively based on the internal requirements of departments and also to anticipate external customer demand can generate substantial cost savings. Utilizing spend and supplier data to forecast future demand, price and choice can facilitate effective procurement by ensuring materials are bought not in surplus or in dearth, improving supplier selection, category management and contract review in the process

2.1.i Moving Average and Quarterly Average Models

Some of the most commonly used forecasting methodologies include moving average and quarterly average models. A moving average model calculates the demand or price based on the average value of three months prior. The order, however can be changed i.e. MA can be calculated based on average of n number of month where n is normally an odd number to retain symmetry. This order also generally improves the smoothness of the curve, with better smoothening observed with greater n. A major drawback of this simple technique is its accuracy with seasonal or cyclic price or demand trends.

Similarly, quarterly average (QA) methodology calculates the average value over a quarter and typically applies last year’s quarterly data to the current year forecasting. This technique overcomes some of the shortcomings of MA by considering seasonal fluctuations and thus generates more reliable results.

Graph 1: Moving Average and Quarterly Average

MA and QA are over-simplified approaches and is not a recommended basis for strategy formulation. These methodologies do provide a quick way of familiarizing oneself with the data on hand and may be applicable for some simple cases. It also benefits from low-cost and ease of implementation.

2.1.ii Exponential Smoothing

Another common time-series forecasting method is Exponential Smoothing (EA). Variable weights are assigned to data points in the past, where larger weights are given to more recent observations, and these weights decrease exponentially as observations get older i.e. more recent data points carry a higher associated weight.

EA is in effect a family of forecasting methods. A simple exponential smoothing is a time series forecasting method that can be used for data without a trend and seasonality and is thus inefficient for situations of rapid growth and seasonal cases. The level is described by an alpha parameter. Double (Holt) exponential smoothing is appropriate for cases witnessing stable growth, not for flat lining stable or rapid growth cases. This time series additionally takes into account the trend (the tendency to increase or decrease over time) described by an additional beta parameter. Triple (Holt-Winters) exponential smoothing takes into account a seasonal component represented by gamma parameter; seasonality is a repeating variation in value. Trend and seasonality can take on an additive (linear change) or a multiplicative (exponential change) effect.

Forecasting in Tableau

A time series exponential smoothing function is available on Tableau. It automatically tests and optimizes up to 8 different ES models. Seasonal cycles are detected automatically by Tableau based on the time aggregation; if aggregated by months, a 12-month cycle is matched; if aggregated by days, a weekly cycle is matched.

One date dimension and one measure value are required. Create a line chart, Date under column shelf Under [Analysis], select [Function] and then [Show Forecast]. Choose forecast length and model accordingly. The more the observations within the time series, the more accurate the automatic model fitting by Tableau. Addition of R forecasting packages is also possible owing to the well-integrated R and Tableau interface. (6)

2.2 Predictive Analytics in Procurement Planning

Advanced analytics can be employed to predict the needs of particular departments, the order size, the cost of an order, the particular requestor associated with an order and so on. The benefits of an order prediction can allow procurement officers to plan ahead of an approved order; they will be able to consolidate similar requirements, and communicate with requestors and aggregate orders before they are placed to save on purchase costs. (7)Additionally, flagging particular requestors with a history of invalid, untimely or higher rate purchases becomes a more facile, efficient process and can be used to prevent occurrences of fraud and once again prevent value leakages.

2.2.i Predictive Markov Models

Fundamentals

Markov Chain models is a stochastic analysis; however, the 1st order process is memory-less i.e. the sequential actions prior to the present state are irrelevant instead the future state is predicted only in reference to the present state. For the purpose of analyzing requestors and purchase orders, a 1st order chain as opposed to k-order chain is suitable. Deshpande showed that increasing chain order can result in longer algorithm run-time and reduce the data set coverage. This is as a result of the nature of such PO datasets, where several requestors exist with many possessing insufficient purchase order history.

The first step in creating a Markov model is defining the type of state and describing transitions between states. A transition probability matrix (TPM) is created based on the probabilities of transitions between states, or the state remaining as is and predictions may be made using the current state and TPM. The mathematical manipulation is depicted in Figure 1 using a simple matrix with 2 measurements.

Figure 1: Original state x TPM = Future state

Data Preprocessing — Clustering Methods

Westerski et. al. utilizes a 1st order Markov Chain aimed at arming current procurement software with predictive capabilities.(8) Item order descriptions form the basis set on which hierarchical clustering algorithm is applied in the initial data pre-processing. Hierarchical clustering is an unsupervised learning techniques which assigns objects in tree-structured clusters. It is assumed that Westerski et. al. employs an agglomerative clustering process whereby each data point begins as a single cluster and clusters are merged together based on a similarity measure. A q-gram method, the distance given by vector sum over the absolute differences, was the string metric used for the similarity measure between text descriptions.(8)

Partitioning K-means cluster algorithm can be employed instead of a hierarchical clustering with q-gram distances, where k-means is a measure of Euclidean distance between observations. A feature extraction using a TD-IDF (Text Frequency-Inverse Document Frequency) algorithm is required to convert the string set to numerical features. The function serves to increase the importance of terms that appear multiple times in a description while decreasing the importance of generic terms that appear in many descriptions.

Another input option can be expense codes or item names, instead of item descriptions, to reduce the data preprocessing steps however this depends on the genericity of the dimension. Description fields are likely to come in free text format which can be difficult to transform it into a suitable vector format. A suggestion is using Rapid Miner’s text mining extension to enable this conversion with relative ease, making use of operators to remove punctuation, stop words, extra white spaces etc. (9)

Markov Model Algorithm

For a single requestor, two states can be defined where the state depends on the feature values associated with unique item description. Looking at a requestor’s order history, a transition between states is defined when feature values change, and a no transition is recorded when the feature values between states remains the same. First orders for a requestor are chronologically arranged. A transition probability matrix (TPM) can be constructed from the ratio of the amount of times a given transition was observed (in the training phase) by the total amount of time any transition from the origin state has occurred, where every state has its own probability distribution dependent on the previous state.

Requestor 1

Figure 2: Transition between subsequent orders (blue) and Transition from origin state (orange) for n orders

An order prediction can be made for a requestor by simply using the transition matrix and the last completed order item feature values. This model can also be applied to orders specific to department or sub-department orders to produce predictions for each department. A major advantage of this model to predict requisitions internally, is to enable aggregation of orders from different departments/groups thereby attaining better deals to reduce PO costs. In addition, this predictive capability would aid in proactive budgeting and to reveal anomalies.

3.0 Vendor Management and Selection

Vendor management is the process that empowers an organization to take appropriate measures for controlling cost, reducing potential risks related to vendors, ensuring excellent service deliverability and deriving value from vendors in the long-run. Selecting the most suitable vendors, obtaining accurate pricing information, consistently evaluating and monitoring vendor performance and managing relationships with vendors are all aspects of vendor management and is paramount in cutting total acquisition costs. Applications of advanced analytics within vendor management, albeit a laggard compared to for example marketing, is forecasted to experience the largest growth in coming years.

3.1 Forecasting Analytics

A trend analysis can be performed on a month on month basis to visualize emerging and declining vendors. Corresponding shares of business can be mapped either based on criticality of material or the volume of business. Apart from keeping tracking of vendors and identifying monopoly vendors over certain materials, the historic data can be used to forecast the future total cost of ownership by vendors. This could illuminate trends that such as the steady monopolization of certain vendor in order to take action to mitigate risks associated with having a single vendor. On the other hand, over diversification can be problematic if it reduces the influence of key vendors, and forecasting share of business will prepare the procurement department in tackling such risks and to leverage future states in negotiations.

3.2 Advanced Analytics in Vendor Selection

Inefficiencies and costs arise from choosing an inappropriate vendor, as a result of various factors predominantly unreasonable prices, poor quality and late deliveries. Several advanced analytical techniques can be utilized to select appropriate vendors based on the most relevant vendor performance indicators. It is generally accepted that the three most important spheres to a buyer are the vendor’s asking price for a material, the quality of said material and the assurance of on-time delivery.(10) Auxiliary criteria, according to Muralidharan et al. (2002) may also include technical capabilities, financial position, flexibility, service, facility and past performance attitude.(11) The top priorities to selecting the best supplier depends heavily on the firm’s individual needs; perhaps the timely delivery of a good is more important than the cheaper rates offered by a different supplier. This renders supplier selection as a multi-criteria decision making (MCDM) problem.

An interesting consideration is the ability of these advanced analytic techniques to consider the complete repository of suppliers to a business. Traditionally, procurement teams were focused only on the spend of the top suppliers to achieve the goal of maximizing cost savings. The Pareto 80/20 principle is the basis of this rationale; where about 80% of the spend will be covered by 20% of suppliers and vice versa. The buyer/supplier spend tends to follow a Zipf distribution (Figure 3). The long tail of several suppliers (in yellow) can comprise of 50% of the total spend, and in these cases focusing on the top suppliers (in green) can distort the true picture. Companies are beginning recognizing the sizable monetary value of the tail-spend, and advanced analytics can be implemented with increasing efficiencies.

Figure 3: Spend as a Zipf distribution. The area of the yellow end may be higher than the green albeit split over a more suppliers.

The techniques explored include Analytic Hierarchy Process (AHP) and Artificial Neural Networks (ANN) for selections of vendors based on pre-defined criteria. In order to have a more comprehensive sourcing strategy, it may also be useful to predict in advance if a vendor will comply with a contract or not. Additionally, to have a consolidated, objective profile of every vendor, there is scope to explore text mining of unstructured data such as news and websites. A Naïve Bayes model is explored for this purpose.

The same predictive techniques can be adapted to other use cases such as predicting supplier-attrition risk, and predicting supplier fraud. Supplier compliance can also be predicted using text mining analytics, allowing improvements in lead time, quality and price.

3.2.i Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process was first introduced in 1980 by Thomas Saaty.(12) Trade-offs would naturally exist between the parameters important to a buyer. For example, a cheap supplier may not necessarily produce the highest quality and vice versa. Hence, AHP is particularly effective when there are contrasting criteria as the best option is not the one that optimizes each individual criterion but rather the one that achieves the optimal trade-off between criteria.

The first step is the construction of a pair comparison matrix between the different criterion based on the Saaty Scale (Table 2) which represents the importance of one criteria over the other.

Intensity of Importance

Definition

Explanation

1

Equal importance

Two activities contribute equally to the objective

3

Weak importance of one over another

Experience and judgement strongly favor one activity over another

5

Essential or strong importance

Experience and judgement strongly favor one activity over another

7

Demonstrated importance

An activity is strong favored and its dominance demonstrated in practice

9

Absolute importance

The evidence favoring one activity over another is of the highest possible order of affirmation

2, 4, 6, 8

Intermediate values between the two adjacent judgments

When compromise is needed

Table 2: Saaty’s scale of Importance

AHP analysis incorporates both quantitative and qualitative criteria in which the decision maker considers the relative importance of two factors at a time with respect to a common higher-level criterion. The evaluation and calculation in AHP can be divided into four stages: scoring alternative under each criterion, weighting the criterion, calculating the final score and ranking the alternatives. (13)

The first step is the construction of a pair comparison matrix between the different criterion based on the Saaty Scale which represents the importance of one criteria over the other (Table 3). In the simple example for a manufacturing company given in following tables, quality has been given a greater importance than price and reliability (Note the table calculation are based on column criteria over row criteria). Column sums are generated in order to normalize the comparison matrix as in Table 4.

Criteria

Quality

Price

Reliability/Capacity

Quality

1

5

3

Price

0.2 (=

)

1

0.33 (=

)

Reliability/Capacity

0.33 (=

)

3

1

Column Sums

1.533

9

4.333

Table 3: Pair Comparison Matrix and column sums for selected criteria

Criteria

Quality

Price

Reliability/Capacity

Row Averages

Quality

0.652

0.556

0.692

0.633

Price

0.130

0.111

0.077

0.106

Reliability/Capacity

0.2174

0.333

0.231

0.261

1

1

1

1

Table 4: Normalized column sums and row averages for selected criteria

After the pair-wise comparisons are determined by the decision maker, the next step is to calculate a Consistency Ratio (CR) to measure how consistent the judgements have been relative to large samples of purely random judgments. The Consistency Ratio (CR)=CI/RI, where CI is the consistency index and RI is the index for the corresponding random matrix. The calculations are not shown here, however this is an important computation to validate that given weights are consistent. (14)

A similar procedure is followed to score each supplier against one another with respect to quality, price and reliability/capacity. With a large number of suppliers, the AHP model can become complex, and hence a necessary next step is shortlisting the highest ranked suppliers. A pair-wise comparison matrix with respect to Quality criterion for the top 3 suppliers are shown in Table 5.

QUALITY

Supplier 1

Supplier 2

Supplier 10

Row Averages

Rank

Supplier 1

1

3

1/3

0.272

2

Supplier 2

1/3

1

1/4

0.119

3

Supplier 10

3

4

1

0.608

1

Table 5: Pair-wise Comparison Matrix with respect to quality criterion for suppliers

Using same methodology, the scores for the same suppliers for Price and Reliability/ Capacity criterion are found; only the final calculated row averages are shown in Table 6.

PRICE

Row Averages

Rank

REL./CAP.

Row Averages

Rank

S1

0.091

3

S1

0.142

3

S2

0.455

1

S2

0.333

2

S10

0.455

1

S10

0.525

1

Table 6: Row averages (from pair-wise comparison matrix) with respect to quality and reliability/capacity criteria for suppliers

The final step is the calculation of the overall score for each supplier (Table 7). This is computed by multiplying the matrix summarizing the company’s preference for each supplier for each criterion by the preference vector for the three criteria. In other words, overall rating for each supplier can be found via a cumulative sum of the product of each supplier rating and weight of criteria for a specific supplier. (15)

Criteria

X

Supplier

Quality

Price

Rel./Cap.

Criteria

S1

0.272

0.091

0.142

Quality

0.633

S2

0.119

0.455

0.333

Price

0.106

S10

0.608

0.455

0.525

Rel./Cap.

0.261

Table 7: Overall supplier score calculation

3.3.iii Sentiment Analysis to gauge Vendor Performance

*Text Analytics

Data mining and other analytic methodologies can be applied to textual data to excavate business insights. It is estimated that approximately 80% of all usable information to a business exists in an unstructured form. Data mining field extended towards textual information is called text mining and the identification of sentiment from this textual information is called sentiment analysis. Computing the human language poses a challenge; not only to compute the occurrences of words but also the meaning they generate within a context. Natural Language Processing (NLP) are the algorithms to parse sentences into words in a semantically meaningful manner. Data preprocessing is required to convert the textual data into a usable input. The generic steps normally involve the removal of stop words (a, the, etc.), trimming words to their stem form (“purchasing” to “purchase”).

*Application of Sentiment Analysis to Vendor Performance

Vendors are typically evaluated based on relatively static information such as size, production capability, quality, certificates etc., following which certain vendors are selected. If the company has previously done business with suppliers a record is kept and performance is continuously evaluated (eg. scorecards) and updated to past records. This repository of records can easily be translated into simple forecasting models as in Section 2.1 highlighting any trends in performance.

Since fulfilled transactions are required to gauge current performance of a vendor, managing risks tied to a vendor are reactive rather than proactive. For this reason, a sentiment analysis is recommended to gauge vendor performance and compliment traditional techniques. Sentiment analysis is the automatic identification of sentiment i.e. the positive or negative human reaction from textual information. There exists a wealth of information on the capabilities of suppliers that may give more accurate profiles. Consolidating vendor information on social media (Twitter, Facebook), blogs and news, feeds, ratings and reputation from 3rd parties, web logs, customer surveys may be able to indicate or hint towards a vendor’s future state. Any large anomalies that could arise from such an analysis could aid in risk detection ahead of time, or to switch to a supplier with some projected advantage. The largest obstacle to generating insights from the data are tackling its unstructured and heterogeneous nature.

A Naïve Bayes classification is most commonly used for sentiment analysis. The R tm package can be used for processing steps such as handling corpus, creation of term document matrices and data preprocessing. (16)

3.3 Predictive Capabilities within Contract Management

3.3.i Predicting Contract Consumption

Supplier compliance can be a major source of value leakage within the P2P process. To identify and quantify leakages, visibility into purchasing contracts and their key features such as start and end dates, supplier requirements, current consumption rates is required. Advanced compliance analytics can utilize data from ERP systems and invoices to continuously monitor compliance rates of vendors. It is favorable to impart additional predictive capabilities into such models.

Leveraging machine learning algorithms can predict when a purchasing contract is 100% consumed, recognize when contacts are due and predict expiration of contracts. Equipped with this knowledge, optimizations within the procurement department can be realized. The buyer can identify contracts that require renegotiations at the optimal time, circumventing hasty, expensive renegotiations. Any risk of contract overconsumption can be mitigated by handling such contracts proactively, thus ensuring better negotiated power.

A supervised k-nearest neighbor (k-NN) model is an easy to implement and ‘go-to’ choice for predicting the probability that a purchasing contract will be entirely consumed. A k-NN approach was briefly discussed in Section 2.2.i. Briefly, k contracts in the training set are identified most similar to the contract of interest. On the basis of these k similar neighbors, an outcome is predicted. In this case, the outcome is categorical (Yes/No contract consumption = 100%) and the ratio of neighbor records provides the probability of the outcome.

Parameters such as contract life cycle time, quantity, on-time delivery, requestor behaviors (order history), on-time delivery, can be inputted with the output result, whether a contract will be fully consumed or not, giving a reasonable prediction. However, as k-NN is a “black-box” technique, the relationship between the different inputs and output is unknown. Additionally, the training set may have to be considerably large to find an appropriate correlation between inputs and output, owing to the particularly complicated, web-like interactions contract consumption may have to external factors, such as market demand-supply.

Ideally, a predictive model would be built around the 3 different states of contract consumption; under consumption, 100% consumption, and overconsumption. To take this a step further, a model that can predict the percentage of contract consumption would exhibit greater sophistication. Potential supervised algorithms for this purpose are an artificial neural network (ANN) or a random forest (RF) (see Section 4.0) approach.

3.3.ii Predicting Vendor Contract Compliance

With text mining, additional capabilities are realized to differentiate and classify documents, namely contracts. By leveraging a supervised machine learning, high risk contracts.

4.0 Receiving Material

The total time taken is process a PO is the time taken from the receipt of a purchase requisition line item to the PO’s transmission to the vendor. The reduction in PO cycle time could have a drastic improvement in an organizations’ efficiencies catapulted by the fact that about 50% of procurement labor is employed in the ordering of materials and services whereas the other half primarily focused on selecting vendors, managing vendor relationships, and developing strategies.(17) The PO Cycle time includes the delivery time, a chief metric to ensure the scheduled arrival of necessary materials for successful execution of a project.

Interest in hybrid algorithms, known as ensembles, are gaining traction within the predictive analytics field owing to their increased sophistication and accuracy. Ensembles generally combine the results from several other models, some of which have been discussed above. Of particular interest here, is the random forest algorithm, a data mining algorithms which is in itself an ensemble.

Supervised learning based on historical data of closed POs, using a random forest algorithm is one approach to generate a predictive model. The decision tree algorithm forms the preliminary basis on which random forests are built. Simply put, a random forest is an average of several decision trees; it will create a random training set of observations and categorical predictors several times over and generates a prediction for each set, before making a final prediction based on the mean (or weighted mean) of the sample predictions. This general technique of sampling is known as bootstrap aggregating (or bagging) which increases the randomness between training sets, so that decision tree sampling sets are not strongly correlated in order to reduce overall model variance. Random forests have the additional feature of randomness whereby at each split a random sample of predictors are also chosen from all the initially selected predictors.

While decision trees are classification techniques, random forests can be used for either classification or regression problems. The regression random forest algorithm is best suited for continuous inputs such as delivery times. Depending on the data set, various predictors can be chosen, for example item number, quantity and estimated delivery time (given by supplier). Each of these factors are a good indication of predicted delivery times. In addition, factors like geographical location of vendors could be taken into consideration. The R package randomForest can be employed.

Liu et. al. used a random forest regression coupled with a dimension reduction to successfully predict delivery times better than supplier estimates.(18) For example, for 95% of POs, their best model gave approximately 35% more accurate predictions for delivery days than the given supplier estimates. Liu and coworkers employ a dimension reduction in order to find other influential predictors such as plant ID, vendor ID and Material group.

Several predictors, as in the work of Liu et. al., will generate large number of trees which increases the performance and offers stable predictions. However, this also leads to slower model. For real-time applications, this can be ineffective and thus it is often recommended that 2–3 predictors of highest importance are sufficient. The training phase for these algorithms may be fast, however creation of predictions once trained is rather slow.

5.0 Inventory Obsolescence/Slow moving (Inventory Optimization)

Inventory Management

Obsolescence can have a drastic effect on several industries as the scraping and holding costs of obsolete inventory can reduce profits by up to 1% each year. The data mining techniques used for maximizing profit in inventory management are generally time-series forecasting followed by optimization.

Traditional methods for forecasting the obsolescence of an inventory material include obsolescence risk and life cycle A obsolescence risk method calculates a probability (as a risk index) that a material will become obsolete. Josias and Terpenny created a risk index with key metrics of manufacturers’ market share, number of manufacturers, life cycle stage, and company’s risk level. The method allows for the metrics to be weighted depending on the industry requirements. (19) The life cycle methods estimated the life span of a material in order to define the time when the material may ‘expire’. Rojo et. al. employs manufacturers, years to end of life, stock available vs. consumption rate, and operational impact criticality as key indicators for materials with potential high obsolescence risk. (20)

Van Jaarsveld et. al. utilizes demand data to estimate the risk of obsolescence. The method manually groups similar materials and maps demand over time.(21) When a drop-in demand is witnessed a formula is used to approximate the risk of obsolescence. However, it fails to forecast the demand and therefore is not reliable for predictions in the far future.

Terpenny et. al. attempts to overcome the limitations of the obsolescence risk and life cycle methods by using machine learning to forecast obsolescence risk.(22) Active or obsolete labels and their specifications are fed into the supervised machine learning algorithm and the algorithm generates a prediction model. Training and test model validation method of 3 algorithms were tested namely Artificial neural networks (ANN), Support Vector Machines (SVM), Random Forest (RF). For the ANN analysis, the nnet R package was used resulting in an accuracy of 91.6%. A SVM analysis using the svm function in R was used to create a model with 92.4% accuracy. A RF model was created using R package randomForest function generating a 92.5% accuracy.

1. Bartolini A. CPO Rising 2011: Innovative Ideas for the Decade Ahead. 2011.

2. Attaran M. The coming age of online procurement. Ind Manag Data Syst. 2001;101(4):177–80.

3. Gupta A. E-Procurement Trials and Triumphs. Contract Manag [Internet]. 2008;48(1):28–36. Available from: http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=28754740&site=eds-live

4. Survey C. PWC_EconomicCrimeAThreatToBusinessClobally. 2014;

5. PWC. Optimize procure-to- pay processes for profitability , compliance Table of contents. 2012;(September).

6. Tableau. How Forecasting Works in Tableau [Internet]. 2019. Available from: https://onlinehelp.tableau.com/current/pro/desktop/en-us/forecast_how_it_works.htm

7. Wang G, Miller S. Intelligent aggregation of purchase orders in e-Procurement. Proc — IEEE Int Enterp Distrib Object Comput Work EDOC. 2005;27–36.

8. Westerski A, Kanagasabai R, Wong J, Chang H. Prediction of enterprise purchases using Markov models in procurement analytics applications. Procedia Comput Sci [Internet]. 2015;60(1):1357–66. Available from: http://dx.doi.org/10.1016/j.procs.2015.08.209

9. Melvin Tan HC, Wee-leong L. Evaluation and Improvement of Procurement Process with Data Analytics. Int J Adv Comput Sci Appl. 2015;6(8):70–80.

10. Yadav V, Sharma MK. Multi-criteria supplier selection model using the analytic hierarchy process approach. J Model Manag [Internet]. 2016 Feb 8;11(1):326–54. Available from: http://www.emeraldinsight.com/doi/10.1108/JM2-06-2014-0052

11. Muralidharan C, Anantharaman N, Deshmukh SG. A Multi-Criteria Group Decisionmaking Model for Supplier Rating. J Supply Chain Manag [Internet]. 2002 Sep;38(4):22–33. Available from: http://doi.wiley.com/10.1111/j.1745-493X.2002.tb00140.x

12. Saaty RW. The analytic hierarchy process — what it is and how it is used. Math Model [Internet]. 1987;9(3–5):161–76. Available from: https://linkinghub.elsevier.com/retrieve/pii/0270025587904738

13. Asamoah D, Annan J, Nyarko S. AHP Approach for Supplier Evaluation and Selection in a Pharmaceutical Manufacturing Firm in Ghana. Int J Bus Manag. 2012;7(10):49–62.

14. Kubde RA. The Analytic Hierarchy Process Based Supplier Selection Approach for Collaborative Planning Forecasting and Replenishment Systems. Int J Eng Res Technol. 2012;1(7):1–12.

15. Bruno G, Esposito E, Genovese A, Passaro R. Journal of Purchasing & Supply Management AHP-based approaches for supplier evaluation : Problems and perspectives. J Purch Supply Manag [Internet]. 2012;18(3):159–72. Available from: http://dx.doi.org/10.1016/j.pursup.2012.05.001

16. Yazdizadeh P, Ameri F. A Text Mining Technique for Manufacturing Supplier Classification. In: Volume 1B: 35th Computers and Information in Engineering Conference [Internet]. ASME; 2015. p. V01BT02A036. Available from: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/DETC2015-46694

17. APAC. The APQC Open Standards Benchmarking®. 2018.

18. Liu J, Hwang S, Yund W, Boyle LN, Banerjee AG. Predicting Purchase Orders Delivery Times Using Regression Models With Dimension Reduction. 2018;V01BT02A034.

19. Josias CL. Hedging future uncertainty: A framework for obsolescence prediction, proactive mitigation and management. ProQuest Diss Theses [Internet]. 2009;(February):169. Available from: http://search.proquest.com/docview/304924750?accountid=14511%5Cnhttp://sfx.ucl.ac.uk/sfx_local?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&genre=dissertations+&+theses&sid=ProQ:ProQuest+Dissertations+&+Theses+Global&atitle=&title=Hed

20. Romero Rojo FJ, Roy R, Kelly S. Obsolescence risk assessment process best practice. J Phys Conf Ser. 2012;364(1).

21. Van Jaarsveld W, Dekker R. Estimating obsolescence risk from demand data to enhance inventory control — A case study. Int J Prod Econ. 2011;133(1):423–31.

22. Jennings C, Wu D, Terpenny J. Forecasting Obsolescence Risk Using Machine Learning. In: Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing [Internet]. ASME; 2016. p. V002T04A033. Available from: http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?doi=10.1115/MSEC2016-8625