An enterprise risk management model for supply chains

John M. Mulvey, Hafize G. Erkan

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


The design of an optimal supply chain rarely considers uncertainty within the modeling framework. This omission is due to several factors, including tradition, model size, and the difficulty in measuring the stochastic parameters. We show that a stochastic program provides an ideal framework for optimizing a large supply chain in the face of an uncertain future. The goal is to reduce disruptions and to minimize expected costs under a set of plausible scenarios. We illustrate the methodology with a global production problem possessing currency movements.

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Number of pages13
StatePublished - 2009

Publication series

NameSpringer Optimization and Its Applications
ISSN (Print)1931-6828
ISSN (Electronic)1931-6836

All Science Journal Classification (ASJC) codes

  • Control and Optimization


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