Naz, Farheen, Kumar, Anil, Agrawal, Rohit, Garza-Reyes, Jose Arturo, Majumdar, Abhijit and Chokshi, Hemakshi (2023) Artificial Intelligence as an enabler of quick and effective production repurposing manufacturing: an exploratory review and future research propositions. Production Planning & Control. pp. 1-24. ISSN 0953-7287
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Abstract / Description
The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development.
Item Type: | Article |
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Additional Information: | Copyright © 2023 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning & Control on 25 August 2023, available at: http://www.tandfonline.com/10.1080/09537287.2023.2248947. |
Uncontrolled Keywords: | Repurposing manufacturing; Artificial intelligence; Flexible; Structural topic modelling; Adaptable and reconfigurable manufacturing; Text mining; Bibliometric |
Subjects: | 000 Computer science, information & general works 600 Technology > 650 Management & auxiliary services |
Department: | Guildhall School of Business and Law |
Depositing User: | Anil Kumar |
Date Deposited: | 09 Aug 2023 08:23 |
Last Modified: | 15 Sep 2023 14:39 |
URI: | https://repository.londonmet.ac.uk/id/eprint/8673 |
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