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Reducing Inventory System Costs by Using Robust Demand Estimators

Author

Listed:
  • Raymond A. Jacobs

    (Department of Management, Radford University, Radford, Virginia 24142)

  • Harvey M. Wagner

    (Graduate School of Business Administration, University of North Carolina, Chapel Hill, North Carolina 27514)

Abstract

Applications of inventory theory typically use historical data to estimate demand distribution parameters. Imprecise knowledge of the demand distribution adds to the usual replenishment costs associated with stochastic demands. Only limited research has been directed at the problem of choosing cost effective statistical procedures for estimating these parameters. Available theoretical findings on estimating the demand parameters for (s, S) inventory replenishment policies are limited by their restrictive assumptions. The impact on total system cost of using the sample mean and standard deviation as compared to robust parameter estimators has not been tested. This paper explores the circumstances under which the cost due to statistical estimation can be substantially reduced by a better choice of estimators. Specifically, an exponentially smoothed average and a modified exponentially smoothed mean absolute deviation are shown to outperform the sample mean and standard deviation for a wide range of computer simulated and U.S. Air Force empirical demands when the (s, S) policies are calculated using Ehrhardt's Power Approximation. Those situations in which the method of demand parameter estimation has negligible impact on total system cost are also indicated.

Suggested Citation

  • Raymond A. Jacobs & Harvey M. Wagner, 1989. "Reducing Inventory System Costs by Using Robust Demand Estimators," Management Science, INFORMS, vol. 35(7), pages 771-787, July.
    • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:7:p:771-787
    DOI: 10.1287/mnsc.35.7.771
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    Citations

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    Cited by:

    1. Strijbosch, L. W. G. & Moors, J. J. A., 2005. "The impact of unknown demand parameters on (R,S)-inventory control performance," European Journal of Operational Research, Elsevier, vol. 162(3), pages 805-815, May.
    2. L W G Strijbosch & R M J Heuts & E H M van der Schoot, 2000. "A combined forecast—inventory control procedure for spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(10), pages 1184-1192, October.
    3. Anne E. Lordahl & James H. Bookbinder, 1994. "Order‐statistic calculation, costs, and service in an (s, Q) inventory system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 81-97, February.
    4. Harvey M. Wagner, 2002. "And Then There Were None," Operations Research, INFORMS, vol. 50(1), pages 217-226, February.
    5. Hasni, M. & Aguir, M.S. & Babai, M.Z. & Jemai, Z., 2019. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 216(C), pages 145-153.
    6. Refik Güllü, 1996. "On the value of information in dynamic production/inventory problems under forecast evolution," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(2), pages 289-303, March.
    7. Strijbosch, L.W.G. & Moors, J.J.A., 1998. "Inventory Control : The Impact of Unknown Demand Distribution," Other publications TiSEM bf5529df-b993-4816-9839-0, Tilburg University, School of Economics and Management.

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