Overview
- Provides advanced analytical solutions for managing supply chain in the era of Industry 4.0
- Integrates machine learning and operations research models for faster and smarter decisions
- Illustrates key concepts using real-life case studies
Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 304)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
About this book
Management of supply chains has been evolving rapidly over the last few years due to the inception of Industry 4.0, where businesses adopt automation technologies and data exchanges leading to dynamic and interconnected supply chain systems. Emphasizing on analytical approaches such as predictive and prescriptive modeling, this book presents state-of-the-art original research work dealing with advanced analytical models for the design, planning, and operation of the supply chain to provide faster and smarter decisions in the era of digitization.
In particular, the book integrates machine learning and operations research models for faster and smarter decisions, presents prescriptive analytics models for strategic, tactical, and operational decision making in the supply chain, and addresses recent challenges such as sustainability in the supply chain, supply chain visibility, and supply chain digitalization. Key concepts are illustrated using real-life case studies, making thebook a valuable reference for researchers, technical professionals, and students.
Similar content being viewed by others
Keywords
Table of contents (9 chapters)
Editors and Affiliations
About the editors
Dr. Sharan Srinivas is an Assistant Professor with a joint appointment in the Department of Industrial & Manufacturing Systems Engineering and the Department of Marketing at the University of Missouri. Dr. Srinivas received his Ph.D. in industrial engineering and operations research from Pennsylvania State University. He holds a Bachelor’s degree in industrial engineering from College of Engineering, Guindy, Anna University, India, a MS in industrial and systems engineering from Binghamton University, State University of New York (SUNY), and a MEng. in industrial engineering and operations research from the Pennsylvania State University.
Dr. Srinivas' area of specialization is data analytics and operations research with research interests in healthcare operations management, logistics, smart service systems, and supply chain. He has been an investigator on industry-based research projects. He has published over 20 scholarly articles in journals and his research work hasappeared in leading journals such as Computers and Industrial Engineering, Expert Systems with Applications, Transportation Research Part C: Emerging Technologies, Transportation Research Part E: Logistics and Transportation Review, International Journal of Medical Informatics. Dr. Srinivas has taught undergraduate, graduate, and MBA level courses that include topics pertaining to data analytics, machine learning, simulation, service systems, and supply chain optimization. He is also an active member of INFORMS and IISE professional societies, and has served numerous times as a session chair in their annual conferences. Dr. Srinivas is a certified six sigma black belt and recipient of multiple awards (INFORMS Koopman prize, Winemiller Excellence Award, Richard Wallace Faculty Grant, Penn State Doctoral Fellowship, Service Enterprise Engineering Fellowship).
Dr. Suchithra Rajendran is an Assistant Professor with a joint appointment in the Departmentof Industrial and Manufacturing Systems Engineering and the Department of Marketing at the University of Missouri, Columbia, USA. Prior to that, she served as a consultant for many private and public organizations on various collaborative projects. She holds a Bachelor's degree in industrial engineering from Anna University in India. Her graduate degrees are from the Pennsylvania State University, where she received a M.S. and a Ph.D. in industrial engineering and operations research.
Dr. Rajendran's research interests include healthcare systems engineering, big data analytics, multiple criteria decision-making, and quality assurance. She is a Penn State National Science Foundation Center for Health Organization Transformation (NSF-CHOT) scholar, Service Enterprise Engineering fellow and also a recipient of the Richard Wallace Faculty Incentive Grant and DAAD-WISE Fellowship.
Prof. Dr. Hans Ziegler held the Chair for Production, Operations and Logistics Management in the School of Business, Economics and Information Systems at the University of Passau, Germany. He received a diploma in industrial engineering from the University of Karlsruhe (TH), Germany (now called Karlsruhe Institute of Technology), a doctoral degree in business and economics and a post-doctoral habilitation in business, both from the University of Paderborn, Germany. He had been on the faculty of the University of Paderborn and the Technical University of Darmstadt, Germany, before moving to the University of Passau. He has research interests in production, operations and logistics management. Professor Ziegler has published over 60 articles in peer-reviewed journals, conference proceedings and books.
Bibliographic Information
Book Title: Supply Chain Management in Manufacturing and Service Systems
Book Subtitle: Advanced Analytics for Smarter Decisions
Editors: Sharan Srinivas, Suchithra Rajendran, Hans Ziegler
Series Title: International Series in Operations Research & Management Science
DOI: https://doi.org/10.1007/978-3-030-69265-0
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-69264-3Published: 25 June 2021
Softcover ISBN: 978-3-030-69267-4Published: 26 June 2022
eBook ISBN: 978-3-030-69265-0Published: 25 June 2021
Series ISSN: 0884-8289
Series E-ISSN: 2214-7934
Edition Number: 1
Number of Pages: XVIII, 278
Number of Illustrations: 20 b/w illustrations, 79 illustrations in colour
Topics: Supply Chain Management, Operations Research, Management Science, Big Data/Analytics, Industrial and Production Engineering, Production, Services