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Article
Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization
IEEE Transactions on Engineering Management
  • Kang Zhao, University of Iowa
  • Kevin P Scheibe, Iowa State University
  • Jennifer Blackhurst, University of Iowa
  • Akhil Kumar, Pennsylvania State University
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
4-1-2018
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.
Comments

This article is published as Zhao,K., Scheibe,K.P., Blackhurst, J., Kumar, A.; Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization. IEEE transactions on Engineering Management. April 2018, 99; 1-13. DOI: 10.1109/TEM.2018.2808331. Posted with permission.

Copyright Owner
IEEE
Language
en
File Format
application/pdf
Citation Information
Kang Zhao, Kevin P Scheibe, Jennifer Blackhurst and Akhil Kumar. "Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization" IEEE Transactions on Engineering Management Vol. 99 (2018) p. 1 - 13
Available at: http://works.bepress.com/kevin_scheibe/10/