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Safety stock reduction by order splitting

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  • Péter Kelle
  • Edward A. Silver

Abstract

In this article we consider an item for which a continuous review, reorder point, order quantity inventory control system is used. The amount of safety stock required depends upon, among other factors, the average value and variability of the length of the replenishment lead time. One way to reduce these quantities is to split orders among two or more vendors. In this article the random lead times are assumed to have Weibull distributions. This permits the development of analytic expressions for the reduction in the expected value and variability of total demand until the critical first (earliest) delivery received from a vendor. An expression is also obtained for the reorder point that provides a given probability of no stockout prior to the first delivery. Lower bounds are given on the order quantity so as to ensure that the probability of a stockout before any one of the later (second, third, etc.) deliveries is sufficiently small to be considered negligible. The analytic and tabular results can be used to estimate the benefits (reduced carrying costs and/or increased service level) of order splitting.

Suggested Citation

  • Péter Kelle & Edward A. Silver, 1990. "Safety stock reduction by order splitting," Naval Research Logistics (NRL), John Wiley & Sons, vol. 37(5), pages 725-743, October.
  • Handle: RePEc:wly:navres:v:37:y:1990:i:5:p:725-743
    DOI: 10.1002/1520-6750(199010)37:53.0.CO;2-O
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    Cited by:

    1. Narendra Agrawal & Steven Nahmias, 1997. "Rationalization Of The Supplier Base In The Presence Of Yield Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 6(3), pages 291-308, September.
    2. Adhikary, Anirban & Sharma, Amalesh & Diatha, Krishna Sundar & Jayaram, Jayanth, 2020. "Impact of buyer-supplier network complexity on firms’ greenhouse gas (GHG) emissions: An empirical investigation," International Journal of Production Economics, Elsevier, vol. 230(C).
    3. Chatfield, Dean C. & Pritchard, Alan M., 2018. "Crossover aware base stock decisions for service-driven systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 312-330.
    4. Svoboda, Josef & Minner, Stefan & Yao, Man, 2021. "Typology and literature review on multiple supplier inventory control models," European Journal of Operational Research, Elsevier, vol. 293(1), pages 1-23.
    5. Janssen, F.B.S.L.P. & de Kok, T., 1997. "The fill rate service measure in an (s,Q) inventory system with order splitting," Other publications TiSEM c77ce6c1-a172-446f-8d1b-f, Tilburg University, School of Economics and Management.
    6. Chi Chiang & W. C. Benton, 1994. "Sole sourcing versus dual sourcing under stochastic demands and lead times," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(5), pages 609-624, August.
    7. James Flynn, 2000. "Selecting T for a periodic review inventory model with staggered deliveries," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(4), pages 329-352, June.
    8. Raik Özsen & Ulrich W. Thonemann, 2015. "Determining Optimal Parameters for Expediting Policies," Manufacturing & Service Operations Management, INFORMS, vol. 17(1), pages 120-133, February.
    9. Gonçalves, João N.C. & Sameiro Carvalho, M. & Cortez, Paulo, 2020. "Operations research models and methods for safety stock determination: A review," Operations Research Perspectives, Elsevier, vol. 7(C).
    10. Pushpesh Pant & Hari Vishal & S. P. Sarmah, 2020. "Does Disruptive Event Affect Supply Base Size: Evidence from an Emerging Economy," Foreign Trade Review, , vol. 55(4), pages 496-510, November.
    11. Jean‐François Angers & Duncan K. H. Fong, 1994. "Estimating moments of the minimum order statistic from normal populations—a bayesian approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(7), pages 1007-1017, December.

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