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The impact of supply chain disruption on the closed-loop supply chain configuration profit: a study of sourcing policies

Author

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  • Jighyasu Gaur
  • Mehdi Amini
  • Arza Keshava Rao

Abstract

A closed-loop supply chain configuration (CLSCC) encompasses the decisions related to the optimal selection of options at each stage of a closed-loop supply chain (CLSC) for the introduction and reconstruction of new products. The extant literature ignores the impact of supply chain disruptions on CLSCC. An attempt is made to fill this gap in this study. Thus, an integrated multi-sourcing CLSCC optimisation model for new and reconstructed products is developed. The optimisation model presented is a mixed-integer nonlinear programming (MINLP) model. Based on a real-world case study of an auto-parts manufacturer in India presented, a comprehensive set of computational experiments, scenario analyses are conducted. The key finding/observation that resulted from our computational experiments is that multi-sourcing generates higher net present value of total profit compared to single sourcing under the risk of supply chain disruption. Several other observations and managerial insights are drawn from computational experiments, and scenario analyses. Firms interested in configuring their CLSC under the risk of supply chain disruption may use the study's outcomes to understand the profit impact of various CLSCC parameters, individually and in combination.

Suggested Citation

  • Jighyasu Gaur & Mehdi Amini & Arza Keshava Rao, 2020. "The impact of supply chain disruption on the closed-loop supply chain configuration profit: a study of sourcing policies," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5380-5400, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:17:p:5380-5400
    DOI: 10.1080/00207543.2019.1657244
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    Cited by:

    1. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    2. Berger, Niklas & Schulze-Schwering, Stefan & Long, Elisa & Spinler, Stefan, 2023. "Risk management of supply chain disruptions: An epidemic modeling approach," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1036-1051.
    3. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    4. Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    5. Wissuwa, Florian & Durach, Christian F. & Choi, Thomas Y., 2022. "Selecting resilient suppliers: Supplier complexity and buyer disruption," International Journal of Production Economics, Elsevier, vol. 253(C).
    6. Yanting Huang & Benrong Zheng & Zongjun Wang, 2023. "Supplier–remanufacturing and manufacturer–remanufacturing in a closed-loop supply chain with remanufacturing cost disruption," Annals of Operations Research, Springer, vol. 324(1), pages 61-92, May.
    7. Yazdani, Majid & Aouam, Tarik, 2023. "Shipment planning and safety stock placement in maritime supply chains with stochastic demand and transportation times," International Journal of Production Economics, Elsevier, vol. 263(C).

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