In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations.
From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research.
Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.
Chapter 1. Big Data Analytics in Supply Chain Management: A Scientometric Analysis
Chapter 2. Supply Chain Analytics Technology for Big Data
Chapter 3. Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method
Chapter 4. Big Data in Procurement 4.0: Critical Success Factors and Solutions
Chapter 5. Recommendation Model based on Expiry Date of Product Using Big Data Analytics
Chapter 6. Comparing Company’s Performance To Its Peers: A Data Envelopment Approach
Chapter 7. Sustainability, Big Data, and Consumer Behavior: A Supply Chain Framework
Chapter 8. A Soft Computing Techniques Application of An Inventory Model in Solving Two-Warehouses Using Cuckoo Search Algorithm
Chapter 9. An Overview of the Internet of Things Technologies Focuses on Disaster Response
Chapter 10. Closing the Big Data Talent Gap