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Impacts of Reducing the Shelf Life of Red Blood Cells: A View from Down Under

Author

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  • Babak Abbasi

    (School of Business IT and Logistics, RMIT University, Melbourne, VIC 3000, Australia)

  • Golnaz Vakili

    (School of Science, RMIT University, Melbourne, VIC 3000, Australia)

  • Stuart Chesneau

    (National Supply Chain Management Department, Australian Red Cross Blood Service, Melbourne, VIC 3004, Australia)

Abstract

Red blood cells (RBCs) are the most commonly used blood product. A body of literature is available to both support and oppose the concept that patient outcomes are negatively impacted when older blood is transfused. This has led to further research to analyze the impacts of reducing the shelf life of RBCs through the lenses of wastage and cost, and most importantly, sufficiency of supply. In this study, we look at the implications of decreasing the shelf life of RBCs in the state of New South Wales (NSW). We develop a discrete-event simulation model that considers the complexities of a two-echelon inventory system, as characterized by the blood product supply chain in Australia. The results show that reducing the shelf life of RBCs would, as we expect, result in fresher blood being transfused to patients. However, a reduction of 21 days would not be tolerated by that system based on current policy and practices; would result in a loss of control, vastly increased shortages, and (or) outdated RBCs; and would result in greater risk to patient care. Our model also evaluates the impact of operational policies on the NSW blood supply chain.

Suggested Citation

  • Babak Abbasi & Golnaz Vakili & Stuart Chesneau, 2017. "Impacts of Reducing the Shelf Life of Red Blood Cells: A View from Down Under," Interfaces, INFORMS, vol. 47(4), pages 336-351, August.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:4:p:336-351
    DOI: 10.1287/inte.2017.0899
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    References listed on IDEAS

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    Cited by:

    1. Dehghani, Maryam & Abbasi, Babak, 2018. "An age-based lateral-transshipment policy for perishable items," International Journal of Production Economics, Elsevier, vol. 198(C), pages 93-103.
    2. Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
    3. Dehghani, Maryam & Abbasi, Babak & Oliveira, Fabricio, 2021. "Proactive transshipment in the blood supply chain: A stochastic programming approach," Omega, Elsevier, vol. 98(C).
    4. Nagurney, Anna & Dutta, Pritha, 2019. "Competition for blood donations," Omega, Elsevier, vol. 85(C), pages 103-114.

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