Slide deck prepared for a post-graduate course ' ESM 600 - Research Methodology', introducing the research methodology and plan. Program: Masters in Engineering Systems Management Affiliation: American University of Sharjah, College of Engineering, Department of Industrial Engineering

1. DIGITAL TWIN TECHNOLOGY FOR COLD SUPPLY CHAIN OPTIMIZATION:
A THEORETICAL FRAMEWORK
RESEARCH METHODOLOGY PRESENTATION
BY: GROUP D

2. • THE IDEAL: Cold supply chains should be optimized to deliver perishable products with adequate
freshness, shelf-life, and least possible waste due to spoilage en-route to the final customer. Hence,
decision-makers should always be ready to make optimal decisions on the spot.
• THE PROBLEM: Optimization techniques require data which is complete, comprehensive, accurate
and timely, but in real-life business scenarios, the required parameters are either unknown, uncertain,
or transient. Moreover, cold SC products should not suffer unnecessary delays.
• SOLUTION: Digital twin technology can be used to optimize the cold SC network, which can then be
used for monitoring, risk prediction, diagnostics, and simulation of contingency plans. Hence, this
research presents a theoretical framework for implementing IoT, blockchain and artificial intelligence
to create a digital twin of an exemplary cold supply chain network and addresses the concerns that
hinder SC companies from adopting digital twins.
PROBLEM STATEMENT

3. RESEARCH AIM & OBJECTIVES
Develop a framework for applying Digital Twin (DT) technology to the Food Supply
Chain in order to improve its efficiency and resilience without compromising
product quality.
1. Develop an understanding of Cold Supply Chain (CSC) concepts and challenges
2. Identify and evaluate the current solutions to CSC challenges
3. Explore the role of technology in CSC, with focus on Digital Twin (DT) Technology
4. Identify the requirements specifications for DT in Food Supply Chain (FSC) application
5. Propose a strategic framework to the integration of IoT, Blockchain and AI to underpin a DT for a
global FSC network

4. RESEARCH QUESTIONS
 What are the current and major challenges that face supply chains in general, and global, cold
supply chains in particular?
 How have digital technologies been used so far to ameliorate the challenges of global cold
supply chains?
 How can Digital Twin (DT) technology contribute to global cold supply chains?
 How ready are the targeted beneficiaries for adopting DT ?
 What are the requirements specifications needed for DT application?
 How can IoT, Blockchain and AI satisfy the requirements of a supply chain digital twin?

5. MAIN FINDINGS FROM LITERATURE: RQ1
Current and Major Challenges in CSC include:
• Bullwhip Effect (Recurrent)
• Ripple Effect (Exceptional)
• Paper-based Controls and Monitoring
• Outdated Facilities and poorly maintained equipment
• Lack of Communication & Awareness in FSC Network
• Non-supportive Government Policies
Figure 1: Performance Impact VS Disruption frequencies
effects [3]

6. MAIN FINDINGS FROM LITERATURE: RQ2
• Optimization
• Heuristics, Metaheuristics, &
Hyper Heuristics
• Simulation
• System Dynamics
• Discrete-Event
• Agent-Based
• ERP & EDI
• SCM Suite
• APS
• RFID
• Blockchain
• Big Data Analytics
• Digital Twin
• Strategic fit:
• Competition strategy
• SC Strategy (Responsiveness-
Efficiency)
• Vendor-Managed Inventory
• Collaborative Planning,
Forecasting & Replenishment
Operations Research IT Technologies Strategies
Best Practices in SC can be classified into three themes:

7. MAIN FINDINGS FROM LITERATURE: RQ3
Digital Twins in
Logistics
Anticipated Contributions of DT to the CSC include enabling SC partners to:
Packaging & Container
DT
- Choose appropriate packaging materials for a shipment
- Manage container fleets (routing, checking for damage e.g. dents, cracks, hazardous contamination, predictive
maintenance or replacement, redesign, rectify wrong handling practices)
- Improvement in volume utilization
- Optimize fleet sizing & distribution
DTs of Shipments - Optimize product protection
- Track product origin and inspection reports
- Predict remaining shelf-life and route vehicles accordingly to salvage fresh produce
DTs of warehouses,
distribution centers,
cross-dock facilities
- Optimize space utilization
- Reduce waste, congestion, picking errors
- Increase productivity of warehouse personnel
- Automate mundane tasks e.g. stock-counting, picking & storing
- Quickly redesign warehouse layouts with confidence
DTs of logistics
Infrastructure
- Optimize operations of airports, ports, ships, trucks, and people
DTs of Global logistics
Networks
- Plan distribution and delivery routes
- Make informed decisions regarding the location of new warehouses, distribution centers, and production
facilities
Table 1: Summary of DT anticipated contributions to logistics [3]

8. RESEARCH METHOD
• Embedded Mixed Method
RQ4: Population Profiling
Questionnaires
Companies
involved with all
stages of the
FSC
RQ5: Collect Requirements Specifications
Semi-Structured
Interviews/
Surveys
Companies that
are ‘eReady’
RQ6: Create Implementation Plans
Focus Groups
Experts in IoT, AI, Blockchain
Networks, Logistics

9. RESEARCH METHODOLOGY
Pragmatism
Inductive
Exploratory,
Descriptive
Surveys
Mixed
Methods
Philosophy
Approach
Strategy
Choice
Purpose

10. RQ4: POPULATION PROFILING
• Quantitative Approach
• Send out questionnaire to measure e-readiness of Food SC
organizations
• Assess the maturity level of organizations to adopt DTs.
• Purposive Sampling: Filter out companies that are not ready for
DT adoption. Reach out to those that are ‘eReady’
• Targeted research subjects: Middle & Senior Managers,
Directors, Vice Presidents, CEOs, COOs of companies involved
with different stages of the Food SC (Producers, Distributors,
Retailers)

11. INDICATORS & MEASURES OF E-READINESS

12. RQ4: EXEMPLARY QUESTIONNAIRE[1][2]

13. RQ4: DATA ANALYSIS
• Bar Charts
• Pie Charts
• Histograms
Results of Analysis can be used for:
• Identifying the stage(s) in the FSC involving
the least tech-enabled entities (this is likely
where most food is wasted)
• Profiling the organization ready for Digital
Twin
• Evaluate the level of IoT involvement is
CSC Networks

14. RQ5: REQUIREMENTS SPECIFICATIONS
 Qualitative Approach
 Count companies with the readiness and technology to
implement digital twins. (Result of the previous step).
 If the e-Ready respondents are small in number (< 25)  Semi-
Structured Interviews, otherwise: Questionnaires
 Probing Questions are based on the customers’ needs, and also
inquire on what they’ll be expecting from the implementation of
digital twins.

15. RQ5: EXEMPLARY QUESTIONS / INTERVIEW GUIDE

16. RQ5: ANTICIPATED OUTCOMES
• RQ5: Requirements
Specifications
• Anticipated outcomes:
• What parameters the
users want to
measure
• What decisions
(about resource
allocation) they want
to have automated or
optimized
Fig. 2 Example of anticipated outcomes from RQ4 Interviews. Taken from [5]

17. RQ5: ANTICIPATED OUTCOMES
Blockchain
Origin
Tracking
Automatic
Payment
Replace
paperwork
processing
&
Attestation
Fees
Detect
Food
Fraud
Relieve
the IoT
bottleneck
[6]
AI
Forecasting
Product
Demand
Supply
Plannin
g
Predict
break-
down
Predict
Shelf-Life
Risk
Manag-
ement
**Size of bubble is
proportional to
perceived necessity of
the feature

18. RQ6: DEVISE IMPLEMENTATION PLANS
• Devise Integrated implementation plans for each of:
a) IoT
b) Blockchain
c) AI
d) Governance: Economic, strategic and policy concerns
• Partly informed by the literature & RQ5, but expert input is needed
• Targeted Subjects: Experts in IoT, Blockchain, AI, computer networks, logistics
• Method: Focus groups
• Sampling Method: Snowballing
• Stopping criteria: Saturation

19. RQ6: DEVISE IMPLEMENTATION PLANS
• Focus groups preferable here
because it is easier to reach a
design that gets consensus from all
angles (IoT, Blockchain,
Networking, AI, Governance)
• A design which satisfies the needs
of some technologies but not others
can be weeded out as soon as it is
suggested
• Possible issue is group dynamics:
Dominance
Fig. 1 Difference between Focus Group & Group
Interview [4]

20. RQ6 (A) IOT IMPLEMENTATION
• What types of sensors are required at each stage and for each parameter?
• How can faulty sensors be detected?
• What are the most suitable hardware, software & communication platforms
for these sensors?
• How often should readings be taken?
• Which readings must be read in real-time and which can be batch-processed?
• How to handle the large amount of readings generated?
• Is it better to monitor conditions and send alerts only or to rectify deviations
automatically with controllers and actuators?

21. RQ6 (B) BLOCKCHAIN IMPLEMENTATION
• What can blockchain offer for food logistics that a centralized real-time database
cannot?
• What type of blockchain network would be the most appropriate (Public, Private,
Federated?)
• How to prevent collusion on the private network?
• How can Smart Contracts be implemented?
• How can food origins be tracked with blockchain?
• How can food inspection reports be protected from tampering?
• What are the hardware, software, and networking requirements for implementing
blockchain in food logistics?

22. RQ6 (C) AI IMPLEMENTATION
• How can Machine Learning be applied practically to:
• Predict & manage risks
• Demand forecasting
• Supply planning…
• How can data be ‘cleaned’ automatically for training neural networks?
• How can all partners participate in building big datasets without
compromising their privacy?
• How not to send shockwaves through the Supply Chain due to short-sighted
AI optimization?

23. RQ6 (D) STRATEGIC CONCERNS
• What is the estimated Total Cost of Ownership (TOC)?
• Who will own and manage the DT system?
• How can the many beneficiaries share the cost of running the system?
• Can DT still be viable if one or more entities along the SC opt out or are not
e-Ready?
• How can participants expose their operations while maintaining
confidentiality and competitive edge?
• Will DT integrate with or replace current supply chain management systems?

24. DATA COLLECTION & ANALYSIS
• Data Collection:
Preferably recording
(with written
permission). If not,
then note-taking of
the conversations
Data Processing
Organize &
Summarize
Additional
Requirements
Specs
Facts & Opinions on
each suggested
design
Raised but
Unexplored
discussion threads
Visualize
Tree Diagrams
Mind Maps
Analyze
Technical Feasibility
SWOT & FMEA for
each suggested
Design
Best course of
action by
majority/consensus

25. RESEARCH METHODOLOGY
Pragmatism
Inductive
Exploratory,
Descriptive
Surveys
Mixed
Methods
Philosophy
Approach
Strategy
Choice
Purpose

27. REFERENCES
[1]P. Neelam, V. Natarajan, V. Diwanji, and C. T. Solutions, “Is Your Organization Ready to Embrace a
Digital Twin?,” p. 24.
[2]“Are You Ready? Accelerate Your Manufacturing Digital Transformation,” Sight Machine. .
[3] K. Dohrmann, B. Gesing, and J. Ward, “Digital Twins in Logistics,” DHL Customer Solutions &
Innovation, p. 39, 2017.
[4] “2. Differences between Group Interview and Focus Group | Download Scientific Diagram,”
ResearchGate. [Online]. Available: https://www.researchgate.net/figure/Differences-between-Group-
Interview-and-Focus-Group_fig1_259032188. [Accessed: 27-Nov-2019].
[5] R. Accorsi, M. Bortolini, G. Baruffaldi, F. Pilati, and E. Ferrari, “Internet-of-things Paradigm in
Food Supply Chains Control and Management,” Procedia Manufacturing, vol. 11, pp. 889–895, 2017.
[6] K. Christidis and M. Devetsikiotis, “Blockchains and Smart Contracts for the Internet of Things,”
IEEE Access, vol. 4, pp. 2292–2303, 2016.