Identifying and mitigating supply chain risks using fault tree optimization

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.

Keywords:

• Supply chain management
• risk
• optimization
• fault tree

Additional information

Notes on contributors

Michael D. Sherwin

Michael D. Sherwin earned his Ph.D. in industrial and systems engineering from Mississippi State University. His research and teaching interests include supply chain reliability, machine learning, and optimization of complex systems. Mike has nearly 20 years of industry experience, is a Registered Professional Engineer in the Commonwealth of Pennsylvania, and an ASQ-certified Six Sigma Black Belt. He is also a member of ASQ, a member of INFORMS, and serves on the Board of Directors for the Logistics and Supply Chain Division of IISE.

Hugh R. Medal

Dr. Hugh Medal is an assistant professor in the Department of Industrial and Systems Engineering at University of Tennessee. His research and teaching interests are in mathematical programming, with an emphasis on stochastic programming and bilevel programming. He has published articles on these topics in journals such as the Naval Research Logistics, IIE Transactions, and Networks. His 2016 article titled “Allocating protection resources to facilities when the effect of protection is uncertain” was featured in the Research Highlights section of IISE Magazine. His research has been funded by agencies such as the U.S. Army Corps of Engineers, the U.S. Joint Fire Science Program, and the U.S. Department of Homeland Security. He has been a member of INFORMS since 2005.

Cameron A. MacKenzie

Cameron A. MacKenzie joined the IMSE Department at Iowa State University (ISU) in fall 2015 as an assistant professor. His graduate courses in Decision Analysis, Engineering Risk Analysis, and Total Quality Management satisfy requirements for the Industrial Engineering degree as well as the Systems Engineering and Engineering Management degrees. He frequently teaches Engineering Economic Analysis for undergraduate students. He received the Miller Faculty Award to develop online testing modules for the Engineering Economic Analysis.

Kennedy J. Brown

Kennedy J. Brown earned a Bachelor of Science degree in industrial and systems engineering from Mississippi State University. In addition to earning a number of awards and scholarships during her undergraduate career, she is an IISE-certified Six Sigma Yellow Belt.