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A mathematical model for supply chain management of blood banks in India

Author

Listed:
  • S. Dharmaraja

    (Indian Institute of Technology Delhi)

  • Srijan Narang

    (Indian Institute of Technology Delhi)

  • Vidyottama Jain

    (Central University of Rajasthan)

Abstract

This work begins with the understanding of the fundamentals of blood banking by analyzing various aspects of its supply chain and then examines the current scenario of blood shortage in India. A mathematical model is proposed to curb the mismatch between surplus and shortage of blood units at blood banks. This proposed model has three main echelons: forecast the demand of blood units at the blood bank; determine the optimal allocation of units from blood banks with surplus to a blood bank with shortage; select the optimal route for the delivery of the allocations. Further, it has been shown empirically with the previous years’ data that SARIMA model is a very efficient forecasting methodology in blood supply management.

Suggested Citation

  • S. Dharmaraja & Srijan Narang & Vidyottama Jain, 2020. "A mathematical model for supply chain management of blood banks in India," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 541-552, June.
    • Handle: RePEc:spr:opsear:v:57:y:2020:i:2:d:10.1007_s12597-019-00425-9
    DOI: 10.1007/s12597-019-00425-9
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    References listed on IDEAS

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    1. George M. Frankfurter & Kenneth E. Kendall & C. Carl Pegels, 1974. "Management Control of Blood Through a Short-Term Supply-Demand Forecast System," Management Science, INFORMS, vol. 21(4), pages 444-452, December.
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    5. Andres F. Osorio & Sally C. Brailsford & Honora K. Smith, 2015. "A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7191-7212, December.
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    Cited by:

    1. Asadpour, Milad & Olsen, Tava Lennon & Boyer, Omid, 2022. "An updated review on blood supply chain quantitative models: A disaster perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).

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