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A stochastic optimisation approach to maintain supply chain viability under the ripple effect

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  • Tadeusz Sawik

Abstract

This paper presents a novel quantitative approach and stochastic quadratic optimisation model to maintain supply chain viability under the ripple effect. Instead of viability kernel commonly used in the viability theory, this paper establishes the boundaries on acceptable production states for which the production can be continued under the ripple effect, with no severe losses. For a given implementable portfolio of controls, the boundaries on acceptable production trajectories associated with the two conflicting objectives, cost and customer service level are determined. The decision maker selects a viable production trajectory in-between the two boundary trajectories: the cost-optimal and the service-optimal. The selection depends on the decision maker preference, represented by a chosen weight factor in the optimised quadratic objective function that minimises weighted deviations from the cost-optimal and from the service-optimal production schedules under the ripple effect. The findings indicate that for the extreme values of the weight factor, the viable production trajectory is inclined toward the corresponding boundary trajectory and remains in-between the two boundaries, when both objectives are equally important. Keeping production trajectory in-between the two boundaries makes the supply chain more resilient to disruption risks, while the supply chain resilience diminishes as the production trajectory approaches a boundary trajectory. Then a more severe disruption may push the production outside the viability region and cause greater losses.

Suggested Citation

  • Tadeusz Sawik, 2023. "A stochastic optimisation approach to maintain supply chain viability under the ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2452-2469, April.
  • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:8:p:2452-2469
    DOI: 10.1080/00207543.2023.2172964
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    Cited by:

    1. Brusset, Xavier & Ivanov, Dmitry & Jebali, Aida & La Torre, Davide & Repetto, Marco, 2023. "A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic," International Journal of Production Economics, Elsevier, vol. 263(C).
    2. Bartosz Sawik, 2023. "Space Mission Risk, Sustainability and Supply Chain: Review, Multi-Objective Optimization Model and Practical Approach," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    3. Ivanov, Dmitry, 2024. "Cash flow dynamics in the supply chain during and after disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    4. Chervenkova, Tanya & Ivanov, Dmitry, 2023. "Adaptation strategies for building supply chain viability: A case study analysis of the global automotive industry re-purposing during the COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Khadija Echefaj & Abdelkabir Charkaoui & Anass Cherrafi & Dmitry Ivanov, 2024. "Design of resilient and viable sourcing strategies in intertwined circular supply networks," Annals of Operations Research, Springer, vol. 337(1), pages 459-498, June.
    6. Ivanov, Dmitry, 2024. "Supply chain resilience: Conceptual and formal models drawing from immune system analogy," Omega, Elsevier, vol. 127(C).
    7. Mehmet Fatih Acar & Alev Özer Torgalöz & Enes Eryarsoy & Selim Zaim & Salomée Ruel, 2024. "The effect of organizational culture, supplier trust and information sharing on supply chain viability," Operations Management Research, Springer, vol. 17(3), pages 1058-1077, September.
    8. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    9. Sawik, Tadeusz, 2023. "Reshore or not Reshore: A Stochastic Programming Approach to Supply Chain Optimization," Omega, Elsevier, vol. 118(C).

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