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An Analysis of Air Force EOQ Data with an Application to Reorder Point Calculation

Author

Listed:
  • C. R. Mitchell

    (U.S. Air Force Logistics Management Center, Gunter Air Force Station, Alabama)

  • R. A. Rappold

    (Titan Systems Inc., Colorado Springs, Colorado)

  • W. B. Faulkner

    (U.S. Air Force Logistics Management Center, Gunter Air Force Station, Alabama)

Abstract

One of the important uses of an EOQ item's distribution of lead time demand is to set its reorder point. This paper shows that a realistic model of observed demand patterns can be chosen from the compound Poisson family of distributions. Actual historical data from several U.S. Air Force bases are analyzed using the geometric-Poisson and constant-Poisson distributions. The control discipline is order quantity, reorder point with continuous review. The service level is based on percent of demand supplied during lead time and is consistent with current USAF methodology. The reorder point is based on independent calculations and no attempt is made to jointly optimize the order quantity and reorder point. Lead time is assumed to be known and constant.

Suggested Citation

  • C. R. Mitchell & R. A. Rappold & W. B. Faulkner, 1983. "An Analysis of Air Force EOQ Data with an Application to Reorder Point Calculation," Management Science, INFORMS, vol. 29(4), pages 440-446, April.
    • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:4:p:440-446
    DOI: 10.1287/mnsc.29.4.440
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    Citations

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    Cited by:

    1. Lau, Amy Hing Ling & Lau, Hon-Shiang, 2008. "An improved (Q, R) formulation when the stockout cost is incurred on a per-stockout basis," International Journal of Production Economics, Elsevier, vol. 111(2), pages 421-434, February.
    2. Uttarayan Bagchi, 1987. "Modeling lead‐time demand for lumpy demand and variable lead time," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(5), pages 687-704, October.
    3. Willemain, Thomas R. & Smart, Charles N. & Schwarz, Henry F., 2004. "A new approach to forecasting intermittent demand for service parts inventories," International Journal of Forecasting, Elsevier, vol. 20(3), pages 375-387.
    4. Cobb, Barry R. & Johnson, Alan W. & Rumí, Rafael & Salmerón, Antonio, 2015. "Accurate lead time demand modeling and optimal inventory policies in continuous review systems," International Journal of Production Economics, Elsevier, vol. 163(C), pages 124-136.
    5. Lengu, D. & Syntetos, A.A. & Babai, M.Z., 2014. "Spare parts management: Linking distributional assumptions to demand classification," European Journal of Operational Research, Elsevier, vol. 235(3), pages 624-635.
    6. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.

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