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Revisiting rescheduling: MRP nervousness and the bullwhip effect

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  • Qinyun Li
  • Stephen M. Disney

Abstract

We study the material requirements planning (MRP) system nervousness problem from a dynamic, stochastic and economic perspective in a two-echelon supply chain under first-order auto-regressive demand. MRP nervousness is an effect where the future order forecasts, given to suppliers so that they may plan production and organise their affairs, exhibits extreme period-to-period variability. We develop a measure of nervousness that weights future forecast errors geometrically over time. Near-term forecast errors are weighted higher than distant forecast errors. Focusing on replenishment policies for high volume items, we investigate two methods of generating order call-offs and two methods of creating order forecasts. For order call-offs, we consider the traditional order-up-to (OUT) policy and the proportional OUT policy (POUT). For order forecasts, we study both minimum mean square error (MMSE) forecasts of the demand process and MMSE forecasts coupled with a procedure that accounts for the known future influence of the POUT policy. We show that when retailers use the POUT policy and account for its predictable future behaviour, they can reduce the bullwhip effect, supply chain inventory costs and the manufacturer’s MRP nervousness.

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  • Qinyun Li & Stephen M. Disney, 2017. "Revisiting rescheduling: MRP nervousness and the bullwhip effect," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1992-2012, April.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:7:p:1992-2012
    DOI: 10.1080/00207543.2016.1261196
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    1. Li, Qinyun & Gaalman, Gerard & Disney, Stephen M., 2023. "On the equivalence of the proportional and damped trend order-up-to policies: An eigenvalue analysis," International Journal of Production Economics, Elsevier, vol. 265(C).
    2. Klaus Altendorfer & Wolfgang Seiringer & Thomas Felberbauer & Balwin Bokor & Fabian Brockmann, 2024. "How Periodic Forecast Updates Influence MRP Planning Parameters: A Simulation Study," Papers 2403.11010, arXiv.org, revised Nov 2024.
    3. Van Belle, Jente & Crevits, Ruben & Verbeke, Wouter, 2023. "Improving forecast stability using deep learning," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1333-1350.
    4. Ghazi M. Magableh & Mahmoud Z. Mistarihi, 2023. "Global Supply Chain Nervousness (GSCN)," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    5. Dominguez, Roberto & Cannella, Salvatore & Barbosa-Póvoa, Ana P. & Framinan, Jose M., 2018. "OVAP: A strategy to implement partial information sharing among supply chain retailers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 122-136.

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