Zamani, E. orcid.org/0000-0003-3110-7495, Smyth, C., Gupta, S. et al. (1 more author) (2023) Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327 (2). pp. 605-632. ISSN 0254-5330
Abstract
Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. This is an author-produced version of a paper subsequently published in Annals of Operations Research. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | artificial intelligence; supply chain resilience; big data analytics; systematic literature review; emerging technologies; supply chain disruptions |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 Sep 2022 16:24 |
Last Modified: | 25 Sep 2024 13:50 |
Status: | Published |
Publisher: | Springer (part of Springer Nature) |
Refereed: | Yes |
Identification Number: | 10.1007/s10479-022-04983-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190770 |