Future of Supply Chains & 

IoT / Sensor Data Use

July 19th, 2023

Sven Dharmani - EY
Introduction

Sven Dharmani 

Partner, EY

Supply Chain Leader for Advanced Manufacturing & Mobility / 

Automotive Sector 

25+ years of global experience in supply chain transformation.

Focus on Implementing industry 4.0 capabilities such as predictive 

maintenance, advanced analytics, digital twins, machine learning, natural 

language processing and sensor data / IoT integration to solve complex SC 

& Operations challenges.

Extensive experience in driving large scale transformations in fortune 100 

corporations globally

Page 1
Discussion Topics

1. Future of Supply Chain

2. Digital Supply Chain – Case Studies

• IoT/sensor data, natural language processing & predictive analytics in Supply Chain

• GPS Tracking in Supply Chain – Marine Transportation

Page 2
1. Top supply chain priorities to recovery and beyond

Strategic 

architecture

Transparency 

and resiliency

Cost & cash 

reduction

Sustainability Digitally networked 

supply chain

Key interventions

Rapidly redefine 

and integrate your 

supply chain 

strategy. 

Alter your global 

trade flows, global 

tax models, supply 

chain operating 

model and 

footprint.

Design and build 

agility into your 

supply chain footprint 

and supplier network. 

Improve your 

disruption response 

through real-time 

monitoring and 

scenario planning. 

Drive a step 

change in your 

supply chain cost 

and working 

capital profile 

allowing you to 

fund the 

transformation. 

Embrace the 

future of a circular 

economy by 

engaging suppliers 

and industry 

partners, aligned 

with available 

incentives to 

drive competitive 

advantage.

Move from doing 

digital to being 

digital. Implement 

supply chain 

technologies that 

open up new revenue 

streams rather than 

simplify efficiencies. 

Close the talent gap 

in digital fluency.

Page 4 Confidential, Proprietary to EY
1. Resiliency and Sustainability - identify gaps and develop a path to short-term

fixes and long-term value

Resiliency = 

Visibility + Agility

Embed end-to-end visibility, simulation 

and risk monitoring

Design omni-capable agile networks.

Secure alternative sources of supply.

Develop a resilient operating model and 

workforce.

Create a trusted and secure supply

chain.

Future Vision of Supply Chain

Cost take out & 

cash extraction

Industry 4.0 & 

Digital SC

Traditional Demands

Sustainability = 

Environmental + Social + Governance

Establish sustainable and 

diverse sourcing.

Enable traceability, visibility and

disclosure.

Decarbonize the value chain.

Introduce circularity into your 

business model.

Assess impact of new taxes and 

incentives for a sustainable supply 

chain.

Page 51 Confidential, Proprietary to EY

Legend:

*Relevant topic for today’s discussion

**Relevant topic for subsequent discussion
Discussion Topics

1. Future of Supply Chain

2. Digital Supply Chain – Case Studies

• IoT/sensor data, natural language processing & predictive analytics in Supply Chain

• GPS Tracking in Supply Chain – Marine Transportation

Page 6
Optimization of 

national 

maintenance 

activities 

through datadriven 

scheduling 

decisions and 

ML-driven 

reliability 

improvement

EY role

• ETL and Data Structuring: 

EY used natural language 

processing to structure textual 

log comments for predictive 

analysis

• Advanced Analytics/AI: EY 

developed predictive models 

incorporating log and 

IoT/sensor/telematics data, as 

well as a suite of visualizations 

for opportunity identification

The Challenge

• Identify opportunities to 

reduce scheduled 

maintenance activities to 

drive cost savings

• Maintain or increase 

reliability of agency 

infrastructure to ensure 

national safety

Value delivered

1. Predictive model: Developed 

analytical models on historical 

system health data to predict 

equipment failures and drive 

preemptive maintenance 

response to improve quality and 

reliability of service

2. Maintenance optimization 

analysis: Enabled exploratory 

analysis of current 

maintenance activities to 

identify areas for deep dive, 

and created models to 

understand optimal schedule 

for maintenance

Gathered log data

Developed thesaurus 

and part glossary

Standardized 

log data 

terminology

Structured log data 

(verbs, parts) 

through NLP analysis

Data analysis

(descriptive, 

correlation, 

predictive, etc.)

Present 

recommendations

Methodology

2A. U.S. Public Sector Client – Case Study

Page 7 Confidential, Proprietary to EY
We combine multiple AI modelling approaches to identify risks, predict failures, and minimize downtime

Predicting Failures

Natural 

Language 

Processing

Data Mining

Create dataset from 

unstructured text.

• Utilize maintenance log 

comments from field 

technicians and convert 

to a tabular format for 

modeling

• Data requirements

maintenance log text

Failure 

Analysis

Time Series 

Forecast

Forecast IoT / sensor 

performance and detect 

anomalies given historical 

sensor data

• IoT / Sensor performance 

forecast using univariate 

time-series model, e.g.

ARIMA 

• Data requirements: 

historical and streaming 

telemetry data in time 

series

moderately complex

Estimate the probability of 

machine & parts failure 

based on maintenance logs

• Descriptive statistics 

about error occurrence, 

machine failure, and 

component lifetime

• Survival analysis using 

Kaplan-Meier model and 

parametric distribution 

fitting

• Data requirements: error 

alarm and maintenance 

log data

less complex

Machine 

Learning

Predict the failure 

likelihood and remaining 

lifetime given machine 

conditions and 

maintenance data

• Error & failure prediction 

using classification 

model, e.g. ensemble 

learning, neural network

• Data requirements: 

machine specification, 

maintenance log, error 

alarm, and historical 

telemetry data

most complex

2A. Predictive Maintenance Modelling Approaches

Page 8 Confidential, Proprietary to EY
2A. One example of Smart Facility impacts

Smart 

Facilities

Site A

Site C

Site B

3 Hour Drive

Remote Radar Facilities

- Current: Maintenance Technician’s drive to 

facility weekly to conduct the planned 

maintenance actions which might include just 

visual inspection

- Future: Sensors allow equipment to be remotely 

monitored. Data enables trend analysis, 

prediction, and insights System A: 

Automate 

Weekly 

Maintenance

System A: 

Automate 

Weekly 

Maintenance

System A: 

Automate 

Weekly 

Maintenance

System B: 

Automate 

Weekly

Maintenance

System B: 

Automate 

Weekly 

Maintenance

System B: 

Automate 

Weekly 

Maintenance

Reduced planned 

maintenance 

actions and 

travel time; 

increased realmonitoring and 

insights

Page 9 Confidential, Proprietary to EY
2B. ISO Tank GPS Enablement – Logistics Visibility

GPS data on returnable containers – Improved turns leading to higher asset 

utilization, reduced inventory and stable supply

Based on Q1 2019 data

2.0 

Turns

3.1 

Turns

Cycle Time Improvement

Page 10 Confidential, Proprietary to EY
2B. ISO Tank GPS Enablement – Logistics Visibility

Optimized vessel routing

Developed visibility of ISO tanks utilizing data from GPS tool to 

showcase the value of location and route mapping.

Findings

• Norfolk to Detroit via Canada took ~20 days not including stops

• Norfolk to Detroit direct takes ~7 days

Recommendation

Review route optimization opportunities to optimize offload at Norfolk 

instead of Halifax to decrease transit time

Improved ISO tank utilization through newly 

developed visibility tools

$xxM

Defined, standardized and trained key GPS 

deployment processes (installation → activation 

→ registration) in multiple international sites.

51 

Days

39 

Days

Tracked quarterly metrics 

based on process design 

training and improvements

Process Improvement 

Savings Q3 2019

Outbound Results as of Q4 2019

Page 11 Confidential, Proprietary to EY
2B. ISO Tank GPS Enablement – Logistics Visibility

Findings

• Complete Cycle (Full to Customer Empty to Barry) = 40 days

• 18 days spent at client in Leverkusen (45% of 40 day cycle time)

• Cycle 2 (next slide) on track to take ~40 days

Recommendation

• Evaluate process efficiencies 

• Identify opportunities to replicate on other routes

Findings

• Tank has not moved for almost 2 years

• Originally used for Phenyl TCS shipments thru Feb 2017

• Dow owned - using for storage 

Recommendation

• Designate tank as storage only

Long cycle time for ISO Tank No movement of ISO Tank

Page 12 Confidential, Proprietary to EY
2B. ISO Tank GPS Enablement – Logistics Visibility

Improved cycle time & 

Reduced total transit time

Improved supply chain and reliability driven by GPS tracking data

Page 13 Confidential, Proprietary to EY
2B. ISO Tank GPS Enablement - PowerBI Dashboards

Developed analytics and dashboards from multiple 

sources (external GPS vendor and SAP) to monitor:

• Asset locations

• Alerts 

• Routes 

• F&I product performance 

• Battery health 

• Idle status

Page 14 Confidential, Proprietary to EY
Appendix

Page 15
EY is ranked as a global leader in Supply Chain

2021-2022

ALM Intelligence Pacesetter Research — Supply Chain 2021-2022

Consistently ranked in the Leaders’ 

Quadrant by Industry Analysts

2021 Analyst Rankings

Leader on IDC MarketScape Worldwide 

Supply Chain Services (2021)

Leader on ALM Vanguard of Supply Chain 

Planning consulting providers

2020 analyst reviews

 ALM Intelligence ranks EY a 

Leader in Supply Chain 

Planning, 2020

 ALM Intelligence ranks EY a 

Leader in Production 

Operations, 2020

 ALM Intelligence ranks EY a 

Leader in Procurement 

Consulting, 2020

2019 analyst reviews

 ALM Intelligence ranks EY a 

Leader in Production 

Operations Consulting

 ALM Vanguard: Supply 

Chain Planning Consulting. 

ALM Intelligence, January 

2019

 IDC MarketScape: Worldwide 

Business Consulting Services 

2019 ~4,000 Global Supply 

Chain Headcount ~1,000 Americas Supply 

Chain Headcount

Page 16
2A. Clients are beginning to tap into years of data logs and telematics data to 

enable AI-driven maintenance capabilities

Maintenance Maturity Model

Predictive

Maintain on early warnings derived from historical data and 

asset condition

Prescriptive

Maintain on asset specialized, optimal maintenance recommendations

Condition-based

Maintain on asset conditions exceeding acceptable 

tolerances

Preventive

Maintain on defined time or meter intervals

Reactive

Repair when broken

Equipment reliability level

Levels of maturity

Adding predictive 

and ultimately 

prescriptive 

capabilities to 

operational arsenal 

unlocks savings, 

drives quality, and 

enables 

accomplishment of 

the mission.

Page 17
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