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Proactive cost-effective identification and mitigation of supply delay risks in a low volume high value supply chain using fault-tree analysis

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  • Sherwin, Michael D.
  • Medal, Hugh
  • Lapp, Steven A.

Abstract

In this paper we use a well-accepted methodology, fault-tree analysis, to identify delay risks and proactively propose a cost-effective mitigation strategy within a low volume high value supply chain. The basis for the assessment is the bill of materials of the product being studied. The top-level event of interest represents the delay in delivering a product to a customer and lower-level events represent the probabilities associated with delays caused by quality and capability deficiencies within the supply chain of the product being studied. Supply chain risk mitigation strategies have been well documented in academic literature. However, much of what has been documented addresses such topics as facility location, inventory buffers, and is generally focused on response strategies once the risk has been realized. This paper presents a robust method to reduce the likelihood of delays in material flow by representing the system of suppliers within a supply chain as a fault-tree and proactively determining the optimum mitigation strategy for the portfolio. The approach is illustrated via real-world numerical scenarios based on hypothetical data sets and the results are presented.

Suggested Citation

  • Sherwin, Michael D. & Medal, Hugh & Lapp, Steven A., 2016. "Proactive cost-effective identification and mitigation of supply delay risks in a low volume high value supply chain using fault-tree analysis," International Journal of Production Economics, Elsevier, vol. 175(C), pages 153-163.
    • Handle: RePEc:eee:proeco:v:175:y:2016:i:c:p:153-163
    DOI: 10.1016/j.ijpe.2016.02.001
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    Cited by:

    1. Yongtao Tan & Chenyang Shuai & Liyin Shen & Lei Hou & Guomin Zhang, 2020. "A study of sustainable practices in the sustainability leadership of international contractors," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 697-710, July.
    2. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    3. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    4. Diedrich, Katharina, 2017. "Framework for digitalized proactive supply chain risk management," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg Inter, volume 23, pages 381-403, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    5. Zhou, Rui & Bhuiyan, Tanveer Hossain & Medal, Hugh R. & Sherwin, Michael D. & Yang, Dong, 2022. "A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk," Omega, Elsevier, vol. 107(C).

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