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Forecasting Error Evaluation in Material Requirements Planning (MRP) Production-Inventory Systems

Author

Listed:
  • T. S. Lee

    (Department of Management, University of Utah, Salt Lake City, Utah 84112)

  • Everett E. Adam, Jr.

    (College of Business and Public Administration, University of Missouri, Columbia, Missouri 65211)

Abstract

The impact of forecasted demand and forecast error, introduced in the Master Production Schedule, upon Material Requirements Planning (MRP) Systems is investigated. A computerized simulation was built to examine several questions. Results indicate that forecasting error, especially the mean error, does impact MRP system inventory costs and shortages; the greater the forecast error the greater the shortages. An exception to this general relationship was that a slight forecast BIAS may improve MRP system performance, which was the case for systems studied herein. Lot-sizing rules and product structure (bill of material structure) were also found to impact total MRP system inventory costs and shortages. The more complicated the MRP structure, the greater the differentiation among lot-sizing rules and the greater the cost impact of forecast errors. A good lot-sizing rule appears to be the period order quantity rule. However, as the forecast error level gets higher, it becomes difficult to select the better lot-sizing rule. Based on this study, suggestions are presented for the production manager's consideration, especially the inventory-production control manager.

Suggested Citation

  • T. S. Lee & Everett E. Adam, Jr., 1986. "Forecasting Error Evaluation in Material Requirements Planning (MRP) Production-Inventory Systems," Management Science, INFORMS, vol. 32(9), pages 1186-1205, September.
    • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:9:p:1186-1205
    DOI: 10.1287/mnsc.32.9.1186
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    Citations

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    Cited by:

    1. Kerkkänen, Annastiina & Korpela, Jukka & Huiskonen, Janne, 2009. "Demand forecasting errors in industrial context: Measurement and impacts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 43-48, March.
    2. Alex Bangash & Ramesh Bollapragada & Rachelle Klein & Narayan Raman & Herbert B. Shulman & Donald R. Smith, 2004. "Inventory Requirements Planning at Lucent Technologies," Interfaces, INFORMS, vol. 34(5), pages 342-352, October.
    3. Danese, Pamela & Kalchschmidt, Matteo, 2011. "The role of the forecasting process in improving forecast accuracy and operational performance," International Journal of Production Economics, Elsevier, vol. 131(1), pages 204-214, May.
    4. Andrew F. Siegel & Michael R. Wagner, 2021. "Profit Estimation Error in the Newsvendor Model Under a Parametric Demand Distribution," Management Science, INFORMS, vol. 67(8), pages 4863-4879, August.
    5. Xie, Jinxing & Zhao, Xiande & Lee, T. S., 2003. "Freezing the master production schedule under single resource constraint and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 83(1), pages 65-84, January.
    6. Mohebbi, E. & Choobineh, F., 2005. "The impact of component commonality in an assemble-to-order environment under supply and demand uncertainty," Omega, Elsevier, vol. 33(6), pages 472-482, December.
    7. Van den Broeke, Maud & De Baets, Shari & Vereecke, Ann & Baecke, Philippe & Vanderheyden, Karlien, 2019. "Judgmental forecast adjustments over different time horizons," Omega, Elsevier, vol. 87(C), pages 34-45.
    8. Enns, S. T., 2002. "MRP performance effects due to forecast bias and demand uncertainty," European Journal of Operational Research, Elsevier, vol. 138(1), pages 87-102, April.
    9. R Fildes & B Kingsman, 2011. "Incorporating demand uncertainty and forecast error in supply chain planning models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 483-500, March.
    10. Klaus Altendorfer & Thomas Felberbauer & Herbert Jodlbauer, 2018. "Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand," Papers 1812.00773, arXiv.org.
    11. Sanders, Nada R. & Graman, Gregory A., 2009. "Quantifying costs of forecast errors: A case study of the warehouse environment," Omega, Elsevier, vol. 37(1), pages 116-125, February.

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