1st Edition

Supply Chain Evolution in the Digital Era Navigating the Future

Edited By Sonu Rajak, Lohithaksha Maniraj Maiyar Copyright 2027
368 Pages 73 B/W Illustrations
by CRC Press

The reference text provides a holistic insight into critical areas of the supply chain and improves them by utilizing advanced technologies of Industry 4.0. It highlights the implementation of advanced technologies, such as Industry 4.0, digital supply chain, blockchain technology, smart factory, and circular economy. The book presents applications and case studies in the field of service and... Read more

1.       The Era of Digitalization: A Systematic Examination of Emerging Trends and Research Directions for Supply Chain 4.0

Satchidananda Tripathy

2.       Application of Artificial Intelligence in supply chain 4.0

Abhijeet Bajpai

3.       Resilience in Supply Chains 4.0

Veronika Jayakumar, L. Aravindh Kumaran, Sivakumar K

4.       Modelling Supply Chain Resilience in Manufacturing: A Fuzzy Logic Approach

Ajeet Kumar Yadav, Cherian Samuel

5.       “Polycrisis” to Predictive Resilience using AI, Machine Learning, and Data Analytics for pharmaceutical supply chain (PSC) in the North America

Divanshu Mittal

6.       Blockchain-Enabled Supply Chain 4.0: Enhancing Transparency, Sustainability, and Circularity

Abarnakani M, Harihara Sudhan B, Infant Joseph Theodric A, Deepak Mathivathanan

7.       Blockchain Integrated Factoring Solution for the Industrial Sector: A Multi-Criteria Decision Approach

Shaik Nasreen, P.S. Biswa Bhusan Sahoo, M I Nafeesathul Basariya, Satchidananda Tripathy

8.       Improving Omnichannel Supply Chains Using Artificial Intelligence, Internet of Things, and Blockchain Technologies

Tarun Madan Kanade, Radhakrishna Batule, Ashima Varghese, Kanchan Tolani, Janmejay Vijaykumar Shukla

9.       An IoT integrated Framework for Predictive Maintenance of Spur Gearbox and Supply Chain Management Optimization

Ravikiran, Yogesha K K, Akhil V M, Rakesh D, Rahul N Murthy, Dore Prasad

10.   Quality 4.0 as a Digital Quality Management System in Manufacturing

Dilip Kushwaha and Faisal Talib

11.   A Framework to Measure Readiness for the Implementation of Industry 4.0

Hariom

12.   Role of Intellectual Property Rights in Supply Chain 4.0 based innovations

I G Rathish

13.   A deep learning-based image classification approach for remaining shelf life prediction of heterogeneous fruit mixture

Lohithaksha M Maiyar, Riya Sanjay Dhawas, Indira Roy

 

Biography

Sonu Rajak is an Assistant Professor in the Department of Mechanical Engineering at the National Institute of Technology Patna, India. He received his Ph.D. and M.Tech. Degree from the National Institute of Technology Tiruchirappalli, India. He has received his bachelor’s degree in production engineering from the B.I.T. Sindri, Vinoba Bhave University India. He has more than fifteen years of experience in both the academic and industrial sectors. He has published more than 60 technical papers in peer-reviewed international journals, four edited books, twelve book chapters, and 15 articles presented in conferences at international levels. His research interests are in broad areas of Manufacturing and Production with a specific interest in, Supply chain and operation management, Industry 4.0, Circular Economy, Optimisation, and Additive Manufacturing.

Lohithaksha Maniraj Maiyar is an Assistant Professor and Head of the Department of Entrepreneurship and Management at Indian Institute of Technology Hyderabad. He received his Ph.D. in 2019 from the Department of Industrial & Systems Engineering, Indian Institute of Technology Kharagpur. Prior to joining IIT Hyderabad, Dr. Lohithaksha was a Research Fellow in the Business and Management Research Institute, University of Bedfordshire, United Kingdom. He was a Research Associate in the Department of Automatic Control and Systems Engineering, The University of Sheffield, prior to joining University of Bedfordshire. During his post-doc, he was working in pan-European projects which involved partners from academia and industry to solve problems related to aircraft ground transportation and fresh food wastages. He has applied supervised and unsupervised machine learning, evolutionary optimisation and multi-criteria techniques for solving problems from a wide range of applications such as aerospace, manufacturing, online fashion markets, and food supply chains. His work focusses on developing Operations Research based mathematical models to support cost-effective, sustainable and resilient decisions.