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Safety stock placement in supply chains with demand forecast updates

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  • Boulaksil, Youssef

Abstract

Supply chains are exposed to many types of risks and it may not be obvious where to keep safety stocks in the supply chain to hedge against those risks, while maintaining a high customer service level. In this paper, we develop an approach to determine the safety stock levels in supply chain systems that face demand uncertainty. We model customer demand following the Martingale Model of Forecast Evolution (MMFE). An extensive body of literature discusses the safety stock placement problem in supply chains, but most studies assume independent and identically distributed demand. Our approach is based on a simulation study in which mathematical models are solved in a rolling horizon setting. It allows determining the safety stock levels at each stage of the supply chain. Based on a numerical study, we find that a big portion of the safety stocks should be placed downstream in the supply chain to achieve a high customer service level.

Suggested Citation

  • Boulaksil, Youssef, 2016. "Safety stock placement in supply chains with demand forecast updates," Operations Research Perspectives, Elsevier, vol. 3(C), pages 27-31.
    • Handle: RePEc:eee:oprepe:v:3:y:2016:i:c:p:27-31
    DOI: 10.1016/j.orp.2016.07.001
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    References listed on IDEAS

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    Cited by:

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    2. Zied Bahroun & Nidhal Belgacem, 2019. "Determination of dynamic safety stocks for cyclic production schedules," Operations Management Research, Springer, vol. 12(1), pages 62-93, June.
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    4. Barros, Júlio & Cortez, Paulo & Carvalho, M. Sameiro, 2021. "A systematic literature review about dimensioning safety stock under uncertainties and risks in the procurement process," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Simon Thevenin & Yossiri Adulyasak & Jean‐François Cordeau, 2021. "Material Requirements Planning Under Demand Uncertainty Using Stochastic Optimization," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 475-493, February.
    6. Chiang, Nai-Yuan & Lin, Yiqing & Long, Quan, 2020. "Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods," Operations Research Perspectives, Elsevier, vol. 7(C).
    7. Luther Yuong Qai Chong & Thien Sang Lim, 2022. "Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    8. Riyadh Jamegh & AllaEldin Kassam & Sawsan Sabih, 2019. "Employment of advanced approach to control inventory level by monitoring Safety Stock in Supply Chain under Uncertain environment," Proceedings of International Academic Conferences 8711585, International Institute of Social and Economic Sciences.
    9. Nielsen, Izabela Ewa & Saha, Subrata, 2018. "Procurement planning in a multi-period supply chain: An epiphany," Operations Research Perspectives, Elsevier, vol. 5(C), pages 383-398.
    10. Díaz, Ronald David Suárez & Paternina-Arboleda, Carlos D. & Martínez-Flores, José Luis & Jimenez-Barros, Miguel A., 2020. "Economic order quantity for perishables with decreasing willingness to purchase during their life cycle," Operations Research Perspectives, Elsevier, vol. 7(C).
    11. Gonçalves, João N.C. & Sameiro Carvalho, M. & Cortez, Paulo, 2020. "Operations research models and methods for safety stock determination: A review," Operations Research Perspectives, Elsevier, vol. 7(C).

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