Overview
- Focuses on network structure evaluation of supply chains with data envelopment models
- Presents the basic definitions in supply chain and supply chain management
- Explains data envelopment analysis and classical DEA models and network models without prior knowledge
Part of the book series: Studies in Big Data (SBD, volume 122)
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Table of contents (10 chapters)
Keywords
About this book
The authors of this book tried to make these experiences available to those interested, considering the experience of several years of training, research, and implementation of projects in the supply chain performance evaluation field.
This book intends to identify the current performance and competitive position of that supply chain compared to other supply chains by presenting and reviewing the techniques and models for measuring the efficiency and performance of the supply chain. Determining the performance of a supply chain is a good description of the status quo (what is). Determining the performance of a supply chain is useful for describing the past and present of supply chain processes, and on the other hand, it can be used to set performance goals and initiate the improvement process. To realize this, a strategic framework or model is needed to be able to extract indicators related to the efficiency of the supply chain and design the appropriate model.
Authors and Affiliations
Bibliographic Information
Book Title: Supply Chain Performance Evaluation
Book Subtitle: Application of Data Envelopment Analysis
Authors: Farhad Hosseinzadeh Lotfi, Tofigh Allahviranloo, Morteza Shafiee, Hilda Saleh
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-031-28247-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-28246-1Published: 03 May 2023
Softcover ISBN: 978-3-031-28249-2Published: 04 May 2024
eBook ISBN: 978-3-031-28247-8Published: 02 May 2023
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XIV, 442
Number of Illustrations: 39 b/w illustrations, 50 illustrations in colour
Topics: Data Engineering, Supply Chain Management, Computational Intelligence, Big Data