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Article
Supply chain vulnerability assessment: A network based visualization and clustering analysis approach
Journal of Purchasing and Supply Management
  • Jennifer Blackhurst, University of Iowa
  • M. Johnny Rungtusanatham, Ohio State University
  • Kevin P Scheibe, Iowa State University
  • Saurabh Ambulkar, Northeastern University
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
11-3-2017
DOI
10.1016/j.pursup.2017.10.004
Abstract

Supply chains are large, complex, and often unpredictable. Purchasing and supply managers and supply chain risk managers need methods and tools to enable them to quickly understand how unexpected disruptions in the supply chain start and grow and to what extent will they negatively impact the flow of goods and services. This paper introduces a methodological approach that can be used by both researchers and managers to quickly visualize a supply chain, map out the propagation path of disruptive events from the supply side to the end customer and understand potential weaknesses in the supply chain design; taking into account the structure, connectivity, and dependence within the supply chain. The approach incorporates a Petri net and Triangularization Clustering Algorithm to offer insights into a supply chain network's vulnerabilities and can be used to efficiently assess supply chain disruption mitigation strategies, especially in complex and difficulty to analyze supply chain systems.

Comments

This article is published as Blackhurst, J., Rungtusanatham, M.J., Scheibe, K.P., Ambulkar, S., Supply chain vulnerability assessment: A network based visualization and clustering analysis approach. Journal of Purchasing and Supply Management, Nov 3 2017, 24(1); 21-30. DOI: 10.1016/j.pursup.2017.10.004. Posted with permission.

Copyright Owner
Elsevier Ltd
Language
en
File Format
application/pdf
Citation Information
Jennifer Blackhurst, M. Johnny Rungtusanatham, Kevin P Scheibe and Saurabh Ambulkar. "Supply chain vulnerability assessment: A network based visualization and clustering analysis approach" Journal of Purchasing and Supply Management Vol. 24 Iss. 1 (2017) p. 21 - 30
Available at: http://works.bepress.com/kevin_scheibe/16/