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Scenario-Based Optimization of Supply Chain Performance under Demand Uncertainty

Author

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  • Asrat Mekonnen Gobachew

    (Chair of Maritime Business and Logistics, University of Bremen, Bibliothekstrasse 1, 28359 Bremen, Germany)

  • Hans-Dietrich Haasis

    (Chair of Maritime Business and Logistics, University of Bremen, Bibliothekstrasse 1, 28359 Bremen, Germany)

Abstract

This study presents a comprehensive supply chain performance optimization model that addresses the trade-off between supply chain cost and customer service level in the distribution network. The model incorporates both deterministic and scenario-based approaches, allowing for a more realistic representation of supply chain operations. The model is applied to a case company operating in the pharmaceutical supply chain in Ethiopia. The goal is to improve the company’s supply chain performance by optimizing various factors such as establishment costs, handling costs, transportation costs, and demand satisfaction. The study considers both financial measures (supply chain cost) and non-financial measures (customer service level) to evaluate the performance of the supply chain. The results of the study demonstrate the effectiveness of the proposed model in identifying the optimal trade-off between supply chain costs and customer service levels. By comparing the results of the model with the current situation of the case company, it is determined that the company can achieve significant cost reductions of up to 25.26% while still meeting customer demands. The model also takes into account the uncertainty in demand, providing more realistic recommendations for distribution center locations, transportation planning, and order fulfillment. The implications of using this optimization model include the potential for cost savings, improved decision-making, enhanced customer satisfaction, and, ultimately, a more successful supply chain. However, the study has some limitations, including a need for further research on other objectives and considerations, such as environmental impacts and disruptions, which could be addressed in future research directions.

Suggested Citation

  • Asrat Mekonnen Gobachew & Hans-Dietrich Haasis, 2023. "Scenario-Based Optimization of Supply Chain Performance under Demand Uncertainty," Sustainability, MDPI, vol. 15(13), pages 1-32, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10603-:d:1187479
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    References listed on IDEAS

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    1. Mohamadreza Fazli-Khalaf & Soheyl Khalilpourazari & Mohammad Mohammadi, 2019. "Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design," Annals of Operations Research, Springer, vol. 283(1), pages 1079-1109, December.
    2. Georgiadis, Michael C. & Tsiakis, Panagiotis & Longinidis, Pantelis & Sofioglou, Maria K., 2011. "Optimal design of supply chain networks under uncertain transient demand variations," Omega, Elsevier, vol. 39(3), pages 254-272, June.
    3. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.
    4. Ata Allah Taleizadeh & Ali Ghavamifar & Amir Khosrojerdi, 2022. "Resilient network design of two supply chains under price competition: game theoretic and decomposition algorithm approach," Operational Research, Springer, vol. 22(1), pages 825-857, March.
    5. Rui Fragoso & José Rui Figueira, 2021. "Sustainable supply chain network design: An application to the wine industry in Southern Portugal," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(6), pages 1236-1251, June.
    6. Yasmine Sabri & Guido J.L. Micheli & Cali Nuur, 2017. "How do different supply chain configuration settings impact on performance trade-offs?," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 26(1), pages 34-56.
    7. Mosahar Tarimoradi & M. H. Fazel Zarandi & Hosain Zaman & I. B. Turksan, 2017. "Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1551-1579, October.
    8. Schildbach, Georg & Morari, Manfred, 2016. "Scenario-based model predictive control for multi-echelon supply chain management," European Journal of Operational Research, Elsevier, vol. 252(2), pages 540-549.
    9. Varsei, Mohsen & Polyakovskiy, Sergey, 2017. "Sustainable supply chain network design: A case of the wine industry in Australia," Omega, Elsevier, vol. 66(PB), pages 236-247.
    10. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    11. Zahiri, Behzad & Zhuang, Jun & Mohammadi, Mehrdad, 2017. "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 109-142.
    12. Jian Wang & Xueyan Wang & Mingzhu Yu, 2020. "Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, February.
    13. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
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