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Elsevier

AI-Powered Supply Chains

Balancing Risk, Reliability, and Sustainability

  • 1st Edition - May 1, 2027
  • Latest edition
  • Editors: Surya Prakash, Gunjan Soni, Peeyush Vats, Mangey Ram
  • Language: English

AI-Powered Supply Chains: Balancing Risk, Reliability, and Sustainability explores tools and techniques for assessing risk, reliability, resilience, and sustainability in Supply… Read more

Description

AI-Powered Supply Chains: Balancing Risk, Reliability, and Sustainability explores tools and techniques for assessing risk, reliability, resilience, and sustainability in Supply Chain Management. As business operations become more complex and globalized, understanding these factors is crucial for informed decision-making and reducing risk. Recent technological advancements—such as AI, machine learning, data analytics, IoT, and blockchain—offer innovative methods for improving visibility and sustainability in supply chains. Additionally, the COVID-19 pandemic has underscored the importance of effective risk management and resilience strategies.

AI-Powered Supply Chains: Balancing Risk, Reliability, and Sustainability provides a comprehensive overview of measurement techniques, their real-world applications, and insights into future developments in the field.

Key features

  • Provides an in-depth exploration of measuring risk, reliability, resilience, and sustainability in supply chain management
  • Examines various tools and techniques, demonstrating their application in real-world scenarios to aid businesses in informed decision-making and risk reduction
  • Content is tailored for researchers, academics, and practitioners in supply chain management
  • Highlights how emerging technologies, such as AI, machine learning, data analytics, IoT, and blockchain, can be implemented to enhance supply chain resilience and sustainability

Readership

Students, scholars, academicians, and professionals in the business world who are involved in addressing issues related to sustainability, risk, reliability, and resilience in complex business environments, particularly in supply chain management

Table of contents

Section 1: Foundations of AI in Supply Chains

1. Introduction to AI-Powered Supply Chains

1.1. Evolution of supply chain management (SCM)

1.2. Role of AI in modern SCM

1.3. Key drivers: Risk, Reliability, and Sustainability

1.4. AI governance and SDGs


2. Fundamentals of AI and Machine Learning for Supply Chains

2.1. Overview of AI/ML techniques (e.g., predictive analytics, optimization, NLP, computer vision)

2.2. Data requirements and infrastructure for AI in SCM

2.3. Ethical considerations in AI-driven supply chains


3. The Triad of Risk, Reliability, and Sustainability

3.1. Defining and measuring risk, reliability, and sustainability in supply chains o Interplay between the three pillars

3.2. Case for AI as a balancing tool

Section 2: AI Applications in Supply Chain Management

4. Demand Forecasting and Inventory Optimization

4.1. AI-driven demand prediction models

4.2. Inventory management using reinforcement learning and optimization algorithms

4.3. Case studies from retail and manufacturing


5. AI for Supply Chain Visibility and Traceability

5.1. Blockchain and AI for end-to-end visibility

5.2. Real-time tracking and IoT integration

5.3. Applications in sustainable sourcing and ethical supply chains


6. AI in Logistics and Transportation

6.1. Route optimization and fleet management

6.2. Autonomous vehicles and drones in logistics

6.3. Reducing carbon footprint through AI


7. Supplier Selection and Risk Management

7.1. AI for supplier risk assessment and scoring

7.2. Predictive analytics for disruption management

7.3. Dynamic supplier networks

Section 3: Challenges and Risks in AI-Powered Supply Chains

8. Data Challenges in AI-Driven Supply Chains

8.1. Data quality, availability, and integration issues

8.2. Privacy and security concerns

8.3. Overcoming data silos


9. Ethical and Social Implications of AI in SCM

9.1. Bias in AI algorithms and decision-making

9.2. Impact on employment and workforce dynamics

9.3. Regulatory and compliance challenges


10. Managing Risks by AI Implementation

10.1. Cost and ROI of AI adoption

10.2. Change management and organizational resistance

10.3. Ensuring reliability and trust in AI systems

Section 4: The Future of AI-Powered Resilient Supply Chains

11. Emerging Trends in AI and Resilient Supply Chain

11.1. Generative AI and its potential in SCM

11.2. Digital twins and simulation-based decision-making

11.3. AI for circular economy and closed-loop supply chains

11.4. Cases on Resilient Supply Chain


12. Sustainability-Driven AI Solutions

12.1. AI for reducing waste and emissions

12.2. Role of AI in achieving net-zero supply chains

12.3. Case studies of sustainable AI implementations


13. Case Studies and Lessons Learned

13.1. Real-world examples of AI-powered supply chains

13.2. Success stories and failures: Key takeaways

13.1. Industry-specific insights (e.g., healthcare, automotive, e-commerce)


14. Conclusion o Summary of key insights

14.1. Future research directions for academia

14.2. Strategic recommendations for industry practitioners

Product details

  • Edition: 1
  • Latest edition
  • Published: May 1, 2027
  • Language: English

About the editors

SP

Surya Prakash

Dr. Surya Prakash is Associate Professor, Operations Management at Great Lakes Institute of Management, Gurugram, India. He has rich experience of teaching and research at BML Munjal University, IIHMR University Jaipur. He received his Ph.D. in Supply Chain Management from Malaviya National Institute of Technology, Jaipur, India, and master’s in manufacturing systems from the Birla Institute of Technology and Science, Pilani, Rajasthan, India. Dr. Prakash has published research articles in leading OM and SCM journals. He has edited book on risk and reliability in operations management and led FDPs, MDPs and funded research projects. His research interests include supply chain management, network design, robust optimization, Industry 4.0, and decision making in operations management.
Affiliations and expertise
Associate Professor, Operations Management, Great Lakes Institute of Management, Gurugram, Haryana, India

GS

Gunjan Soni

Dr. Gunjan Soni holds B.E. (Mechanical Engineering) from The University of Rajasthan, M. Tech. (Industrial Engineering) from IIT-Delhi and PhD (Industrial Engineering) from Birla Institute of Technology, Pilani. He is having 19 years of experience and is now serving as an Associate professor (Department of Mechanical Engineering along with Joint faculty at Department of Artificial Intelligence and Data Engineering). At MNIT Jaipur he has developed several new courses such as Applied Machine Learning, Six Sigma, Artificial Intelligence in Manufacturing Systems,

Applied Probability and Statistics at UG and PG level. He has also established Intelligent Automation and Robotics Lab in the Department of Mechanical Engineering. He has published more than 120 papers in various international journals. He has guided 12 PhDs and over 24 Masters’ theses. He is doing four research projects in which two are international and other two are at national level. His major research contributions are in the areas of supply chain optimization, predictive maintenance, and AI applications in manufacturing systems.

Affiliations and expertise
Associate Professor, Department of Mechanical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

PV

Peeyush Vats

Dr. Peeyush Vats has completed his PhD from the Malaviya National Institute of Technology (MNIT Jaipur). Currently he is working as full-time faculty in Poornima College of Engineering, Jaipur, Rajasthan India. His area of interest is risk analysis, reliability analysis, inventory management under risk and uncertainty in supply chain. He has more than 23 years of teaching and research experience. He has authored several research articles for national & international conferences and journals. Some of the relevant publications of Dr. Peeyush Vats are as “A demand aggregation approach for inventory control in two echelon supply chain under uncertainty” Risk pooling approach in multi-product multi-period inventory control model under uncertainty”, “Risk-Pooling Approach in Inventory Control Model for Multi-products in a Distribution Network Under Uncertainty”, “Grey-based decision-making approach for the selection of distributor in a supply chain” etc. He has worked as reviewer in many journals like International Journal of Intelligent Enterprises, Elsevier Conferences, Int. J. of Logistics Systems and Management, Operations Management Research, International Journal of Quality & Reliability Management, Chinese Management Studies etc.
Affiliations and expertise
Full-time Faculty, Poornima College of Engineering, Jaipur, Rajasthan, India

MR

Mangey Ram

Prof. Mangey Ram is Professor at Graphic Era (Deemed to be University), Dehradun, India. He is Editor-in-Chief of the International Journal of Mathematical, Engineering, and Management Sciences; Journal of Reliability and Statistical Studies; Journal of Graphic Era University; Series Editor of six book series with Elsevier, CRC Press-A Taylor and Frances Group, Walter De Gruyter Publisher Germany, River Publisher and Guest Editor and Associate Editor for various journals. His fields of research are reliability theory and applied mathematics.

He is Editor-in-Chief of the International Journal of Mathematical, Engineering, and Management Sciences; Journal of Reliability and Statistical Studies; Journal of Graphic Era University; Series Editor of six book series with Elsevier, CRC Press-A Taylor and Frances Group, Walter De Gruyter Publisher Germany, River Publisher and Guest Editor and Associate Editor for various journals.

His fields of research are reliability theory and applied mathematics.

Prof. Ram is a Senior Member of the IEEE, Senior Life Member of the Operational Research Society of India, the Society for Reliability Engineering, Quality and Operations Management in India, the Indian Society of Industrial and Applied Mathematics. He is a member of the organizing committee of several international and national conferences, seminars, and workshops. He was conferred the “Young Scientist Award” by the Uttarakhand State Council for Science and Technology, Dehradun, in 2009, and given the “Best Faculty Award” in 2011; “Research Excellence Award” in 2015; and “Outstanding Researcher Award” in 2018 for his significant contributions in academics and research at Graphic Era (deemed to be University), Dehradun, India. Most recently, he received the "Excellence in Research of the Year-2021 Award” from the Honourable Chief Minister of the Uttarakhand State, India.

Affiliations and expertise
Professor, Department of Mathematics, Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, India