Journal article
Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization
IEEE transactions on engineering management, Vol.66(1), pp.127-139
02/2019
DOI: 10.1109/TEM.2018.2808331
Abstract
This paper focuses on understanding the robustness of a supply network in the face of a disruption. We propose a decision support system for analyzing the robustness of supply chain networks against disruptions using topological analysis, performance measurement relevant to a supply chain context, and an optimization for increasing supply network performance. The topology of a supply chain network has considerable implications for its robustness in the presence of disruptions. The system allows decision makers to evaluate topologies of their supply chain networks in a variety of disruption scenarios, thereby proactively managing the supply chain network to understand vulnerabilities of the network before a disruption occurs. Our system calculates performance measurements for a supply chain network in the face of disruptions and provides both topological metrics (through network analysis) and operational metrics (through an optimization model). Through an example application, we evaluate the impact of random and targeted disruptions on the robustness of a supply chain network.
Details
- Title: Subtitle
- Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization
- Creators
- Kang Zhao - Management Sciences Department, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA, USAKevin Scheibe - Supply Chain and Information Systems Department, Iowa State University, Ames, IA, USAJennifer Blackhurst - Management Sciences Department, Henry B. Tippie College of Business, University of Iowa, Iowa City, IA, USAAkhil Kumar - Smeal College of Business, Pennsylvania State University, University Park, PA, USA
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on engineering management, Vol.66(1), pp.127-139
- Publisher
- IEEE
- DOI
- 10.1109/TEM.2018.2808331
- ISSN
- 0018-9391
- eISSN
- 1558-0040
- Language
- English
- Date published
- 02/2019
- Academic Unit
- Business Analytics; Bus Admin Graduate Programs
- Record Identifier
- 9984083862202771
Metrics
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