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How to protect against demand and yield risks in MRP systems

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  • Inderfurth, Karl

Abstract

Traditional MRP systems suffer from several weaknesses, one of them being the disregard of uncertainties like those referring to demand and supply quantities. Advanced MRP concepts handle these uncertainties by incorporating safety stocks and scrap allowances into order release calculations. However, they fail to address how these measures of risk protection might interact. Here, it is shown by using analyses from stochastic inventory control, how the performance of MRP-generated control rules can be assessed and how appropriate control parameters for MRP can be determined.

Suggested Citation

  • Inderfurth, Karl, 2009. "How to protect against demand and yield risks in MRP systems," International Journal of Production Economics, Elsevier, vol. 121(2), pages 474-481, October.
    • Handle: RePEc:eee:proeco:v:121:y:2009:i:2:p:474-481
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    Cited by:

    1. Gansterer, Margaretha, 2015. "Aggregate planning and forecasting in make-to-order production systems," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 521-528.
    2. Faicel Hnaien & Alexandre Dolgui & Desheng Dash Wu, 2016. "Single-period inventory model for one-level assembly system with stochastic lead times and demand," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 186-203, January.
    3. 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).
    4. Hu, Chaoming & Wan, Zhao Man & Zhu, Saihua & Wan, Zhong, 2022. "An integrated stochastic model and algorithm for constrained multi-item newsvendor problems by two-stage decision-making approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 280-300.
    5. Gansterer, Margaretha & Almeder, Christian & Hartl, Richard F., 2014. "Simulation-based optimization methods for setting production planning parameters," International Journal of Production Economics, Elsevier, vol. 151(C), pages 206-213.
    6. Inderfurth, Karl & Vogelgesang, Stephanie, 2013. "Concepts for safety stock determination under stochastic demand and different types of random production yield," European Journal of Operational Research, Elsevier, vol. 224(2), pages 293-301.
    7. 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.
    8. Shaofu Du & Yujiao Zhu & Tengfei Nie & Haisuo Yu, 2018. "Loss-averse preferences in a two-echelon supply chain with yield risk and demand uncertainty," Operational Research, Springer, vol. 18(2), pages 361-388, July.
    9. Inderfurth, K. & Kiesmüller, G.P., 2015. "Exact and heuristic linear-inflation policies for an inventory model with random yield and arbitrary lead times," European Journal of Operational Research, Elsevier, vol. 245(1), pages 109-120.
    10. Karl Inderfurth & Gudrun Kiesmüller, 2013. "Exact and heuristic linear-inflation policies for an inventory model with random yield and arbitrary lead times," FEMM Working Papers 130007, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    11. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).
    12. 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).
    13. Louly, Mohamed-Aly & Dolgui, Alexandre, 2013. "Optimal MRP parameters for a single item inventory with random replenishment lead time, POQ policy and service level constraint," International Journal of Production Economics, Elsevier, vol. 143(1), pages 35-40.

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