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The COVID-19 pandemic and the performance of healthcare supply chains

  • Special: OR in Medicine/Ed. Lee
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Abstract

Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs’ weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach.

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Authors and Affiliations

  1. Operations and Information Management Department, Aston Business School, Aston University, Birmingham, UK

    Majid Azadi

  2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    T. C. E. Cheng

  3. Department of Mathematics, Karaj Branch, Islamic Azad University, Karaj, Iran

    Reza Kazemi Matin

  4. Department of Operations Management and Business Statistics, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman

    Reza Farzipoor Saen

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  1. Majid Azadi
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Azadi, M., Cheng, T.C.E., Matin, R.K. et al. The COVID-19 pandemic and the performance of healthcare supply chains. Ann Oper Res 335, 535–562 (2024). https://doi.org/10.1007/s10479-023-05502-3

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