Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach
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 re...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier
2023
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| Subjects: | |
| Online Access: | https://repository.londonmet.ac.uk/8426/9/1-s2.0-S0148296323002618-main.pdf https://repository.londonmet.ac.uk/8426/15/Accepted%20Version.pdf |
| _version_ | 1804072903646380032 |
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| author | Kumar, Anil Naz, Farheen Luthra, Sunil Vashistha, Rajat Kumar, Vikas Garza-Reyes, Jose Arturo Chhabra, Deepak |
| author_facet | Kumar, Anil Naz, Farheen Luthra, Sunil Vashistha, Rajat Kumar, Vikas Garza-Reyes, Jose Arturo Chhabra, Deepak |
| author_sort | Kumar, Anil |
| collection | LMU |
| 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. |
| first_indexed | 2024-07-09T04:06:33Z |
| format | Article |
| id | oai:repository.londonmet.ac.uk:8426 |
| institution | London Metropolitan University |
| language | English English |
| last_indexed | 2024-07-09T04:06:33Z |
| publishDate | 2023 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:84262024-01-29T14:32:53Z http://repository.londonmet.ac.uk/8426/ 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 Chhabra, Deepak 650 Management & auxiliary services 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. Elsevier 2023-04-02 Article PeerReviewed text en cc_by_4 https://repository.londonmet.ac.uk/8426/9/1-s2.0-S0148296323002618-main.pdf text en cc_by_nd_4 https://repository.londonmet.ac.uk/8426/15/Accepted%20Version.pdf 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 https://www.sciencedirect.com/science/article/pii/S0148296323002618 10.1016/j.jbusres.2023.113903 |
| spellingShingle | 650 Management & auxiliary services Kumar, Anil Naz, Farheen Luthra, Sunil Vashistha, Rajat Kumar, Vikas Garza-Reyes, Jose Arturo Chhabra, Deepak Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach |
| title | Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach |
| title_full | Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach |
| title_fullStr | Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach |
| title_full_unstemmed | Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach |
| title_short | Digging DEEP: futuristic building blocks of omni-channel healthcare supply chains resiliency using a machine learning approach |
| title_sort | digging deep futuristic building blocks of omni channel healthcare supply chains resiliency using a machine learning approach |
| topic | 650 Management & auxiliary services |
| url | https://repository.londonmet.ac.uk/8426/9/1-s2.0-S0148296323002618-main.pdf https://repository.londonmet.ac.uk/8426/15/Accepted%20Version.pdf |
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