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 manufacturing sectors.
This book:
- Discusses smart manufacturing implementation and integration with advanced production processes.
- Focuses on advanced technologies such as logistics 4.0, warehousing 4.0, Industry 4.0, big data analytics, machine learning, blockchain, automation, smart factory, and operation management 4.0.
- Explores digital supply chain for smart operation and logistics systems.
- Showcases the benefits and applications of digitalization such as supply chain visibility, data-driven decision-making, cybersecurity and data protection, smart contracts, agile supply chains, and automation of processes.
- Provides insights on intelligent decision-making in supply chain and operation management.
It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of industrial engineering, production engineering, manufacturing engineering, management, product design, automotive engineering, and mechanical engineering.
Table of Contents:
1. The Era of Digitalization: A Systematic Examination of Emerging Trends and Research Directions for Supply Chain 4.0. 2. Application of Artificial Intelligence in supply chain 4.0. 3. Resilience in Supply Chains 4.0. 4. Modelling Supply Chain Resilience in Manufacturing: A Fuzzy Logic Approach. 5. “Polycrisis” to Predictive Resilience using AI, Machine Learning, and Data Analytics for pharmaceutical supply chain (PSC) in the North America. 6. Blockchain-Enabled Supply Chain 4.0: Enhancing Transparency, Sustainability, and Circularity. 7. Blockchain Integrated Factoring Solution for the Industrial Sector: A Multi-Criteria Decision Approach. 8. Improving Omnichannel Supply Chains Using Artificial Intelligence, Internet of Things, and Blockchain Technologies. 9. An IoT integrated Framework for Predictive Maintenance of Spur Gearbox and Supply Chain Management Optimization. 10. Quality 4.0 as a Digital Quality Management System in Manufacturing. 11. A Framework to Measure Readiness for the Implementation of Industry 4.0. 12. Role of Intellectual Property Rights in Supply Chain 4.0 based innovations. 13. A deep learning-based image classification approach for remaining shelf life prediction of heterogeneous fruit mixture.
About the Author :
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.
