Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

7-29-2019

Journal or Book Title

IISE Transactions

Research Focus Area(s)

​Operations Research

DOI

10.1080/24725854.2019.1630865

Abstract

Although supply chain risk management and supply chain reliability are topics that have been studied extensively, a gap exists for solutions that take a systems approach to quantitative risk mitigation decision making and especially in industries that present unique risks. In practice, supply chain risk mitigation decisions are made in silos and are reactionary. In this article, we address these gaps by representing a supply chain as a system using a fault tree based on the bill of materials of the product being sourced. Viewing the supply chain as a system provides the basis to develop an approach that considers all suppliers within the supply chain as a portfolio of potential risks to be managed. Next, we propose a set of mathematical models to proactively and quantitatively identify and mitigate at-risk suppliers using enterprise available data with consideration for a firm’s budgetary constraints. Two approaches are investigated and demonstrated on actual problems experienced in industry. The examples presented focus on Low-Volume High-Value (LVHV) supply chains that are characterized by long lead times and a limited number of capable suppliers, which make them especially susceptible to disruption events that may cause delays in delivered products and subsequently increase the financial risk exposure of the firm. Although LVHV supply chains are used to demonstrate the methodology, the approach is applicable to other types of supply chains as well. Results are presented as a Pareto frontier and demonstrate the practical application of the methodology.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis as Sherwin, Michael D., Hugh R. Medal, Cameron A. MacKenzie, and Kennedy J. Brown. "Identifying and mitigating supply chain risks using fault tree optimization." IISE Transactions (2019). DOI: 10.1080/24725854.2019.1630865. Posted with permission.

Copyright Owner

IISE

Language

en

File Format

application/pdf

Available for download on Friday, June 19, 2020

Published Version

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