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Inventory control for point-of-use locations in hospitals

Author

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  • M Bijvank

    (Université de Montréal, Montréal, Québec, Canada)

  • I F A Vis

    (University of Groningen, Groningen, The Netherlands)

Abstract

Most inventory management systems at hospital departments are characterised by lost sales, periodic reviews with short lead times, and limited storage capacity. We develop two types of exact models that deal with all these characteristics. In a capacity model, the service level is maximised subject to a capacity restriction, and in a service model the required capacity is minimised subject to a service level restriction. We also formulate approximation models applicable for any lost-sales inventory system (cost objective, no lead time restrictions etc). For the capacity model, we develop a simple inventory rule to set the reorder levels and order quantities. Numerical results for this inventory rule show an average deviation of 1% from the optimal service levels. We also embed the single-item models in a multi-item system. Furthermore, we compare the performance of fixed order size replenishment policies and (R, s, S) policies.

Suggested Citation

  • M Bijvank & I F A Vis, 2012. "Inventory control for point-of-use locations in hospitals," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(4), pages 497-510, April.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:4:p:497-510
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    Citations

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    Cited by:

    1. Esha Saha & Pradip Kumar Ray, 2019. "Modelling and analysis of healthcare inventory management systems," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1179-1198, December.
    2. Kouki, Chaaben & Legros, Benjamin & Zied Babai, M. & Jouini, Oualid, 2020. "Analysis of base-stock perishable inventory systems with general lifetime and lead-time," European Journal of Operational Research, Elsevier, vol. 287(3), pages 901-915.
    3. Kouki, Chaaben & Babai, M. Zied & Minner, Stefan, 2018. "On the benefit of dual-sourcing in managing perishable inventory," International Journal of Production Economics, Elsevier, vol. 204(C), pages 1-17.
    4. Moons, Karen & Waeyenbergh, Geert & Pintelon, Liliane, 2019. "Measuring the logistics performance of internal hospital supply chains – A literature study," Omega, Elsevier, vol. 82(C), pages 205-217.
    5. Volland, Jonas & Fügener, Andreas & Schoenfelder, Jan & Brunner, Jens O., 2017. "Material logistics in hospitals: A literature review," Omega, Elsevier, vol. 69(C), pages 82-101.
    6. Visentin, Andrea & Prestwich, Steven & Rossi, Roberto & Tarim, S. Armagan, 2021. "Computing optimal (R,s,S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 91-99.
    7. Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
    8. Rosales, Claudia R. & Magazine, Michael & Rao, Uday, 2015. "The 2Bin system for controlling medical supplies at point-of-use," European Journal of Operational Research, Elsevier, vol. 243(1), pages 271-280.
    9. Carlos Franco & Edgar Alfonso-Lizarazo, 2017. "A Structured Review of Quantitative Models of the Pharmaceutical Supply Chain," Complexity, Hindawi, vol. 2017, pages 1-13, December.

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