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Applying a bootstrap approach for setting reorder points in military supply systems

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  • Ronald D. Fricker
  • Christopher A. Goodhart

Abstract

This paper develops and applies a nonparametric bootstrap methodology for setting inventory reorder points and a simple inequality for identifying existing reorder points that are unreasonably high. We demonstrate that an empirically based bootstrap method is both feasible and calculable for large inventories by applying it to the 1st Marine Expeditionary Force General Account, an inventory consisting of $20–30 million of stock for 10–20,000 different types of items. Further, we show that the bootstrap methodology works significantly better than the existing methodology based on mean days of supply. In fact, we demonstrate performance equivalent to the existing system with a reduced inventory at one‐half to one‐third the cost; conversely, we demonstrate significant improvement in fill rates and other inventory performance measures for an inventory of the same cost. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 459–478, 2000

Suggested Citation

  • Ronald D. Fricker & Christopher A. Goodhart, 2000. "Applying a bootstrap approach for setting reorder points in military supply systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(6), pages 459-478, September.
    • Handle: RePEc:wly:navres:v:47:y:2000:i:6:p:459-478
    DOI: 10.1002/1520-6750(200009)47:63.0.CO;2-C
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    References listed on IDEAS

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    1. Wang, Min-Chiang & Subba Rao, S., 1992. "Estimating reorder points and other management science applications by bootstrap procedure," European Journal of Operational Research, Elsevier, vol. 56(3), pages 332-342, February.
    2. Tijms, H. C. & Groenevelt, H., 1984. "Simple approximations for the reorder point in periodic and continuous review (s, S) inventory systems with service level constraints," European Journal of Operational Research, Elsevier, vol. 17(2), pages 175-190, August.
    3. Gary D. Eppen & R. Kipp Martin, 1988. "Determining Safety Stock in the Presence of Stochastic Lead Time and Demand," Management Science, INFORMS, vol. 34(11), pages 1380-1390, November.
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    Cited by:

    1. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    2. 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.
    3. Boylan, John E. & Babai, M. Zied, 2022. "Estimating the cumulative distribution function of lead-time demand using bootstrapping with and without replacement," International Journal of Production Economics, Elsevier, vol. 252(C).
    4. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    5. Saldanha, John P., 2022. "Estimating the reorder point for a fill-rate target under a continuous review policy in the presence of non-standard lead-time demand distributions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    6. Turrini, Laura & Meissner, Joern, 2019. "Spare parts inventory management: New evidence from distribution fitting," European Journal of Operational Research, Elsevier, vol. 273(1), pages 118-130.

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