IDEAS home Printed from https://ideas.repec.org/a/wly/navres/v41y1994i1p81-97.html
   My bibliography  Save this article

Order‐statistic calculation, costs, and service in an (s, Q) inventory system

Author

Listed:
  • Anne E. Lordahl
  • James H. Bookbinder

Abstract

A retailer or distributor of finished goods, or the manager of a spare‐parts inventory system, must generally forecast the major portion of demand. A specific customer‐service level p (fraction of replenishment intervals with no stockout) implies two challenges: achieve the service within a small interval plus or minus, and do so with a minimum‐cost investment in inventory. The pth fractile of lead‐time demand (LTD) is the reorder point (ROP) for this service measure, and is often approximated by that fractile of a normal distribution. With this procedure, it is easy to set safety stocks for an (s, Q) inventory system. However, Bookbinder and Lordahl [2] and others have identified cases where the normal approximation yields excessive costs and/or lower service than desired. This article employs an order‐statistic approach. Using available LTD data, the ROP is simply estimated from one or two of the larger values in the sample. This approach is sufficiently automatic and intuitive for routine implementation in industry, yet is distribution free. The order‐statistic method requires only a small amount of LTD data, and makes no assumptions on the form of the underlying LTD distribution, nor even its parameters μ and ρ. We compare the order‐statistic approach and the normal approximation, first in terms of customer service and then using a model of expected annual cost. Based upon characteristics of the available LTD data, we suggest a procedure to aid a practitioner in choosine between the normal and order‐statistic method. © 1994 John Wiley & Sons, Inc.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:navres:v:41:y:1994:i:1:p:81-97
    DOI: 10.1002/1520-6750(199402)41:13.0.CO;2-9
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/1520-6750(199402)41:13.0.CO;2-9
    Download Restriction: no

    File URL: https://libkey.io/10.1002/1520-6750(199402)41:13.0.CO;2-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tadikamalla, Pandu R, 1984. "A comparison of several approximations to the lead time demand distribution," Omega, Elsevier, vol. 12(6), pages 575-581.
    2. Eliezer Naddor, 1978. "Note--Sensitivity to Distributions in Inventory Systems," Management Science, INFORMS, vol. 24(16), pages 1769-1772, December.
    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.
    4. Helmut Schneider & Jeffrey L. Ringuest, 1990. "Power Approximation for Computing (s, S) Policies Using Service Level," Management Science, INFORMS, vol. 36(7), pages 822-834, July.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Janssen, E. & Strijbosch, L.W.G. & Brekelmans, R.C.M., 2006. "Assessing the Effects of using Demand Parameters Estimates in Inventory Control," Discussion Paper 2006-90, Tilburg University, Center for Economic Research.
    2. Hon‐Shiang Lau & Amy Hing‐Ling Lau, 2003. "Nonrobustness of the normal approximation of lead‐time demand in a (Q, R) system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(2), pages 149-166, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vernimmen, Bert & Dullaert, Wout & Willemé, Peter & Witlox, Frank, 2008. "Using the inventory-theoretic framework to determine cost-minimizing supply strategies in a stochastic setting," International Journal of Production Economics, Elsevier, vol. 115(1), pages 248-259, September.
    2. John E. Tyworth & Liam O'Neill, 1997. "Robustness of the normal approximation of lead‐time demand in a distribution setting," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(2), pages 165-186, March.
    3. Daniela Favaretto & Alessandro Marin & Marco Tolotti, 2023. "A theoretical validation of the DDMRP reorder policy," Computational Management Science, Springer, vol. 20(1), pages 1-28, December.
    4. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 131-153, January.
    5. 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.
    6. Hon‐Shiang Lau & Amy Hing‐Ling Lau, 2003. "Nonrobustness of the normal approximation of lead‐time demand in a (Q, R) system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(2), pages 149-166, March.
    7. John P. Saldanha & Bradley S. Price & Douglas J. Thomas, 2023. "A nonparametric approach for setting safety stock levels," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1150-1168, April.
    8. 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.
    9. 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.
    10. Rossetti, Manuel D. & Yasin Ünlü, 2011. "Evaluating the robustness of lead time demand models," International Journal of Production Economics, Elsevier, vol. 134(1), pages 159-176, November.
    11. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    12. Strijbosch, L.W.G. & Moors, J.J.A., 1999. "Simple Expressions for Safety Factors in Inventory Control," Discussion Paper 1999-112, Tilburg University, Center for Economic Research.
    13. Georgia Perakis & Guillaume Roels, 2008. "Regret in the Newsvendor Model with Partial Information," Operations Research, INFORMS, vol. 56(1), pages 188-203, February.
    14. Mario Guajardo & Mikael Rönnqvist & Ann Mari Halvorsen & Svein Inge Kallevik, 2015. "Inventory management of spare parts in an energy company," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 331-341, February.
    15. Scott Webster & Z. Kevin Weng, 2001. "Improving Repetitive Manufacturing Systems: Model and Insights," Operations Research, INFORMS, vol. 49(1), pages 99-106, February.
    16. Kumar, Anupam & Evers, Philip T., 2015. "Setting safety stock based on imprecise records," International Journal of Production Economics, Elsevier, vol. 169(C), pages 68-75.
    17. Chan, Chi Kin & Fang, Fei & Langevin, André, 2018. "Single-vendor multi-buyer supply chain coordination with stochastic demand," International Journal of Production Economics, Elsevier, vol. 206(C), pages 110-133.
    18. Chen, Youhua Frank, 2005. "Fractional programming approach to two stochastic inventory problems," European Journal of Operational Research, Elsevier, vol. 160(1), pages 63-71, January.
    19. Dev, Navin K. & Shankar, Ravi & Swami, Sanjeev, 2020. "Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system," International Journal of Production Economics, Elsevier, vol. 223(C).
    20. Riezebos, Jan, 2006. "Inventory order crossovers," International Journal of Production Economics, Elsevier, vol. 104(2), pages 666-675, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:navres:v:41:y:1994:i:1:p:81-97. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6750 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.