COVID-19 and global supply chain risks mitigation: Systematic review using a scientometric technique

Fernando, Yudi and Mohammed Hammam, Mohammed Al-Madani and Muhammad Shabir, Shaharudin (2023) COVID-19 and global supply chain risks mitigation: Systematic review using a scientometric technique. Journal of Science and Technology Policy Management. pp. 1-26. ISSN 2053-4620. (In Press) (In Press)

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Abstract

Purpose: This paper aims to investigate how manufacturing firms behave to mitigate business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global supply chain. Design/methodology/approach: A systematic literature review for data mining was used to address the research objective. Multiple scientometric techniques (e.g. bibliometric, machine learning and social network analysis) were used to analyse the Lens.org, Web of Science and Scopus databases’ global supply chain risk mitigation data. Findings: The findings show that the firms’ manufacturing supply chains used digitalisation technologies such as Blockchain, artificial intelligence (AI), 3D printing and machine learning to mitigate COVID-19. On the other hand, food security, government incentives and policies, health-care systems, energy and the circular economy require more research in the global supply chain. Practical implications: Global supply chain managers were advised to use digitalisation technology to mitigate current and upcoming disruptions. The manufacturing supply chain has high uncertainty and unpredictable global pandemics. Manufacturing firms should consider adopting Blockchain technology, AI and machine learning to mitigate the epidemic risk and disruption. Originality/value: This study found the publication trend of how manufacturing firms behave to mitigate the global supply chain disruptions during the global pandemic and business uncertainty. The findings have contributed to the supply chain risk mitigation literature and the solution framework.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: COVID-19; Machine learning; Social network analysis; Supply chain; Systematic literature review; Technology
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HF Commerce
R Medicine > RA Public aspects of medicine
T Technology > T Technology (General)
Faculty/Division: Faculty of Industrial Management
Institute of Postgraduate Studies
Depositing User: PM. Dr. Yudi Fernando
Date Deposited: 09 Aug 2023 01:39
Last Modified: 09 Aug 2023 01:39
URI: http://umpir.ump.edu.my/id/eprint/38174
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