Big Data Analytics in Supply Chain Management
CRC Press (Verlag)
978-0-367-40717-9 (ISBN)
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.
Iman Rahimi, B.Sc. (Applied Mathematics), M.Sc. (Applied Mathematics – Operations Research) earned his PhD in the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia in 2017. He is now a research scholar at the Faculty of Engineering &Information Technology, University of Technology Sydney, Australia. His research interests include machine learning, optimization, and supply chain management. He has edited a book entitled: "Evolutionary Computation in Scheduling" with Wiley. He has acted as an editor for journals of: "Computational Research Progress in Applied Science & Engineering (CRPASE)", "International Journal Renewable Energy Technology (IJRET)", and "International Journal of Advanced Heuristic and Meta-Heuristic Algorithms". Also, Iman has acted as an editor and co-editor of the books for some prestige publishers ("Elsevier and Taylor & Francis"). Amir H. Gandomi is a Professor of Data Science at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over one hundred and eighty journal papers and seven books which collectively have been cited more than 16,000 times (H-index = 58). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 1%) for three consecutive years, 2017 to 2019. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular. Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr. Fong has published over 500 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SCIE-indexed journals. Simon is also an active researcher with leading positions such as Vice-chair of IEEE Computational Intelligence Society (CIS) Task Force on "Business Intelligence & Knowledge Management", and Vice-director of International Consortium for Optimization and Modelling in Science and Industry (iCOMSI). M. Ali Ülkü, Ph.D., is a Full Professor of Supply Chain and Decision Sciences, and the Director of the Centre for Research in Sustainable Supply Chain Analytics-CRSSCA, in the Rowe School of Business at Dalhousie University, Halifax, NS, Canada. Dr. Ülkü is also cross appointed with the Department of Industrial Engineering, and the School for Resource and Environmental Studies. He received his Ph.D. in Management Sciences from the University of Waterloo, M.Sc. in Operations Research from Çukurova University, and B.Sc. in Industrial Engineering from Bilkent University. Prior to his academic career, he worked as a productivity consultant in the largest international brewery in Turkey. Dr. Ülkü’s research thrusts include the theoretical modeling of sustainable supply chain and logistics systems, operations-marketing interface, and mathematical modeling of consumer behaviour and societal problems. He published in such journals as Annals of Operations Research, European Journal of Operational Research, International Journal of Production Economics, Journal of Business Research, Journal of Cleaner Production, and Service Science. His research funding includes those from The Natural Sciences and Engineering Research Council of Canada, The Scientific and Technological Research Council of Turkey, and The United States National Science Foundation. A recipient of the Exceptional Teaching Award from the University of Waterloo, Dr. Ülkü has taught operations management, business analytics, logistics, and supply chain management courses at various universities in Canada, Turkey, and the USA. He served as the Program Chair for the 2018 Canadian Operational Research Society Conference. The IEOM Society International honoured him with the 2019 Distinguished Professor Award.
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
| Erscheinungsdatum | 15.01.2021 |
|---|---|
| Zusatzinfo | 33 Tables, black and white; 43 Line drawings, black and white; 2 Halftones, black and white; 45 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 548 g |
| Themenwelt | Kunst / Musik / Theater ► Design / Innenarchitektur / Mode |
| Mathematik / Informatik ► Informatik ► Datenbanken | |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Technik ► Bauwesen | |
| Wirtschaft ► Betriebswirtschaft / Management ► Logistik / Produktion | |
| ISBN-10 | 0-367-40717-5 / 0367407175 |
| ISBN-13 | 978-0-367-40717-9 / 9780367407179 |
| Zustand | Neuware |
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