Slide deck prepared for a post-graduate course ' ESM 600 - Research Methodology', introducing the research problem, aim, and objectives. 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 Problem
Presentation
By: Group D

2. Outline
INTRODUCTION,
BACKGROUND &
RATIONALE
THE RESEARCH GAP STATEMENT OF THE
RESEARCH PROBLEM
AIM, OBJECTIVES &
ANTICIPATED
RESEARCH OUTCOMES
RESEARCH QUESTIONS
LIMITATIONS,
ASSUMPTIONS &
DELIMITATIONS
PLAN OF WORK &
TIME SCALE

3. Introduction & Background
• Supply Chain
• Network connecting (Company, suppliers, buyers).
• Entities include: Producers, Vendors, Warehouses, Transportation companies, Distribution centers, and
Retailers.
• Examples: Amazon, DHL, Nike etc.…
• Cold Supply Chain
• Same concept & network.
• Temp. controlled Products
• Examples: fruits, milk, meat, vaccines.

4. Introduction & Background
• Digital Twins
• Digital replica of a physical object or system.
• First implemented by NASA.
• Implemented in Automotive,
Manufacturing, & Healthcare Industries.
• Digital twins are NOT Simulations!
• Provide real time data.
• Predict In-efficiencies & issues.

5. Rationale
• The Ripple Effect
• Caused by a disruption or issue in 1 part of the
supply chain.
• Lower revenues
• Delivery delays
• Market share losses
• Drops in stock returns
• Main source of Ripple Effect:
• Lack of end-to-end visibility.

6. Solution
Information Sharing!
Applying IoT, Big Data, & Deep
Learning.
Create a Digital Twin of the Supply
Chain.
Benefits:
Predictive Maintenance
Contingency Plan Simulations
Lower Financial Risk

7. The Research Gap
• A global logistics leader (DHL) is already developing:
• Material Digital Twins for packaging and containers
• Shipments
• Warehouses & Distribution Centers
• Logistics Infrastructures
DT of Global Supply Chain Network? Still largely an aspiration!
“In logistics, the ultimate digital twin would be a model of an entire network
including not just logistics assets but also oceans, railway lines, highways, streets,
and customer homes and workplaces.”
- Gesing, B., Kückelhaus, M., in DHL Trend Research, 2019

8. Statement of the Research Problem
Optimization of Cold Supply Chains (SCs) is of utmost importance to manufacturers, distributors, retailers and
end-consumers, as it results in delivering goods affordably to the market on time and in acceptable quality as
well as creating profit for the companies along the SC. However, for optimization techniques to yield
meaningful results, they require data which is complete, comprehensive, accurate and timely. Such high-quality
data as well as high-processing power have become accessible in the recent times with the advent of new
technologies, namely: IoT, Big Data Analytics, Artificial Intelligence, and Blockchain. These technologies can
be leveraged to create a Digital Twin of a global 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 integrating the said technologies to create a digital twin of an exemplary cold supply chain
network and addresses the concerns that hinder SC companies from adopting Digital Twins.

9. The Research Aim & Objectives
Develop a framework for applying Digital Twin (DT) technology to Cold Supply
Chain in order to improve its efficiency and resilience without compromising
product quality.
 Explore the applications of IoT sensing in Cold Supply Chain
 Investigate the applications of Blockchain Technology in Cold Supply Chain
 Identify appropriate Analytics, AI tools and algorithms to optimize the Cold Supply Chain
 Integrate proposed systems of IoT, Blockchain and AI into unified framework
 Critically examine the literature concerning the drawbacks of DT for a global supply chain network
 Suggest solutions to such drawbacks that hinder SC companies from adopting DT technology globally

10. Research Questions
• Four Research Question
1. What data will be needed in order to implement DT in Cold SC? (Waffa)
2. How can Blockchain Technology be of use to Cold SCs? (Mahmoud)
3. How can Artificial Intelligence (AI) be used to handle risks and disruptions
in the SC? (Arwa)
4. What are the drawbacks that hinder SC companies from adopting DT and the
possible solutions to mitigate them? (Raed)

11. Research Questions
• IoT (Sensors,
electronic devices,
controllers)
Data
Collection
• Algorithms
• Big Data analytics
• Block Chain
Data Analysis
• Artificial
Intelligence
• Machine Learning
Data
Computation
• Adoption
Concerns
• Solutions
Evaluations

12. Research Focus
• Integration of digital twin with emerging technologies
• A theoretical framework to develop a digital twin of Supply Chain
• Adaption Concerns
• Address the issues regarding adaption of SC Digital Twin by Professional
World
• Propose solutions to eliminate or mitigate those issues

13. Preliminary Assumptions
• Prices of Cloud storage and Cloud computing capabilities will
continue to decrease
• Blockchain technology will become accessible to enterprises in the
next 5-10 years
• Agents along the supply chain can (and would be willing to)
collaborate and share necessary information and data for the DT to
work
• Agents will be willing to set up a shared fund to finance this project
and share the installation, maintenance and operational costs

14. • Time
• Resource
• Expertise
• Type of supply chain
• Simple consumer products
• Not consider of after-sales
maintenance or reverse logistics
• Not consider limitations of the IT
infrastructure in storage and
streaming
Limitations & Delimitations
Limitations Delimitations

15. Targeted Audience
• Companies with huge SC network
• Large Logistics Companies
• Hospital Management
• Pharmaceutical Companies

16. Anticipated Research Outcomes
• Double shelf life for fresh agricultural produce, seafood, frozen food,
etc.
• Enforcing effective cold chain logistics
• Reduce food spoilage by 30%-50%
• Improve food distribution efficiency by 30%-50%

17. Beneficiaries
Catering Companies
Fresh Vegetable Companies
Bakery Products companies
Chicken, meat and fish providers
Pharmaceuticals
Floristry Companies

18. Time Scale & Plan of Work

19. Thank you!
Any Questions?