An analytical framework for supply network risk propagation: A Bayesian network approach

Myles D. Garvey, Steven Carnovale, Sengun Yeniyurt

Research output: Contribution to journalArticlepeer-review

182 Scopus citations

Abstract

There are numerous examples of supply chain disruptions that have occurred which have had devastating impacts not only on a single firm but also on various other firms in the supply network. We utilize a Bayesian Network (BN) approach and develop a model of risk propagation in a supply network. The model takes into account the inter-dependencies among different risks, as well as the idiosyncrasies of a supply chain network structure. Specific risk measures are derived from this model and a simulation study is utilized to illustrate how these measures can be used in a supply chain setting.

Original languageEnglish (US)
Pages (from-to)618-627
Number of pages10
JournalEuropean Journal of Operational Research
Volume243
Issue number2
DOIs
StatePublished - Jun 1 2015

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Keywords

  • Networks
  • Risk analysis
  • Risk management
  • Supply chain management
  • Uncertainty modeling

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