Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

9-18-2019

Journal or Book Title

Computers & Industrial Engineering

First Page

106061

Research Focus Area(s)

​Operations Research

DOI

10.1016/j.cie.2019.106061

Abstract

Supply chain risk analysis is an important field in operations management and logistics. Identifying those risks, assessing the probability of those risks, and understanding how those risks change if mitigation strategies are implemented contribute to better supply chain risk management. Reliability analysis has a long tradition of assessing the probability of failure, and fault trees are typically used to understand how the failure of individual components can lead to system failure within an engineered system. More recently, fault trees have been proposed to assess the probability of a supply chain failure. Dynamic fault trees, which are relatively new in reliability analysis, model the dependency among possible component failure and how these probabilities change over time. This paper applies dynamic fault trees to model supply chain risk for different types of supply chains. The dynamic fault tree allows a firm to model complex interactions among suppliers and understand how those interactions impact its risk. The model incorporates an information system that relays information about the status of suppliers to the firm, and this information system could also fail. A Markov chain model and Monte Carlo simulation are used to numerically assess supply chain risk as modeled by these dynamic fault trees.

Comments

This is a manuscript of an article published as Lei, Xue, and Cameron MacKenzie. "Assessing Risk in Different Types of Supply Chains with a Dynamic Fault Tree." Computers & Industrial Engineering (2019): 106061. DOI: 10.1016/j.cie.2019.106061. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Available for download on Sunday, September 18, 2022

Published Version

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