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Blood Bank Inventory Control

Author

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  • John B. Jennings

    (The New York City-Rand Institute)

Abstract

Essentially, blood banks are facilities which procure, store, process, and dispense blood. To operate effectively in the face of both random supply and random demand, sizable buffer stocks of blood are maintained. The resulting inventory control problem is an extremely complex one for several reasons: (1) both supply and demand are random; (2) approximately 50 percent of all bloods demanded, "crossmatched," and held for a particular patient are eventually found not to be required for that patient; (3) blood is perishable, the present legal lifetime being 21 days in most areas; and (4) each blood bank typically interacts with a number of other banks. This paper presents a framework for the analysis of the whole blood inventory problem at the individual hospital as well as at the regional level, presents a realistic model of blood inventories for both the individual and regional cases, and analyzes the effects of several alternative inventory policies.

Suggested Citation

  • John B. Jennings, 1973. "Blood Bank Inventory Control," Management Science, INFORMS, vol. 19(6), pages 637-645, February.
    • Handle: RePEc:inm:ormnsc:v:19:y:1973:i:6:p:637-645
    DOI: 10.1287/mnsc.19.6.637
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    Cited by:

    1. Soumya Ranjan Pathy & Hamed Rahimian, 2024. "Value of risk aversion in perishable products supply chain management," Computational Optimization and Applications, Springer, vol. 89(2), pages 517-552, November.
    2. Jittamai, Phongchai & Boonyanusith, Wijai, 2014. "Risk Assessment in Managing the Blood Supply Chain," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Next Generation Supply Chains: Trends and Opportunities. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 18, volume 18, pages 447-468, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Elena Katok & Andrew Lathrop & William Tarantino & Susan H. Xu, 2001. "Jeppesen Uses a Dynamic-Programming-Based DSS to Manage Inventory," Interfaces, INFORMS, vol. 31(6), pages 54-65, December.
    4. Beliën, Jeroen & Forcé, Hein, 2012. "Supply chain management of blood products: A literature review," European Journal of Operational Research, Elsevier, vol. 217(1), pages 1-16.
    5. Wang, Ke-Ming & Ma, Zu-Jun, 2015. "Age-based policy for blood transshipment during blood shortage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 166-183.
    6. Pahl, Julia & Voß, Stefan, 2014. "Integrating deterioration and lifetime constraints in production and supply chain planning: A survey," European Journal of Operational Research, Elsevier, vol. 238(3), pages 654-674.
    7. K Katsaliaki & S C Brailsford, 2007. "Using simulation to improve the blood supply chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 219-227, February.

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