Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach

Kumar, Anil, Naz, Farheen, Luthra, Sunil, Vashistha, Rajat, Kumar, Vikas, Garza-Reyes, Jose Arturo and Chhabra, Deepak (2023) Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach. Journal of Business Research, 162 (113903). pp. 1-14. ISSN 0148-2963

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Abstract / Description

There is a lack of studies which have explored the factors of omni-channel healthcare supply chain resiliency (OHSCR). Thus, the current study explores the resiliency factors of healthcare supply chains (HSCs) and the development of futuristic blocks of OHSCR. In the first phase of the study, the resiliency factors of HSCs were identified through an extensive literature review and expert interviews. In the second phase, a machine learning approach, i.e., K-means clustering, was used to develop the futuristic blocks of OHSCR. Lastly, in the third phase, implications and future research propositions were discussed. The findings of this study suggest that the healthcare sector evaluating OHSCR should focus on six key building blocks: data-driven management and transformative technological adoption, flexible and transparent organisational management system, robust and diversified supply chain system, responsible and customer-centric supply chain, information sharing and knowledge management, and strategic alignment and network ecosystem. A conceptual research framework is also proposed to support future research.

Item Type: Article
Uncontrolled Keywords: Healthcare supply chains; Omni-channel; Resilience; Omni-channel Healthcare Supply Chains Resiliency; Machine learning
Subjects: 600 Technology > 650 Management & auxiliary services
Department: Guildhall School of Business and Law
Depositing User: Anil Kumar
Date Deposited: 27 Mar 2023 08:48
Last Modified: 29 Jan 2024 14:32
URI: https://repository.londonmet.ac.uk/id/eprint/8426

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