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Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review
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 supp...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524319/ https://www.ncbi.nlm.nih.gov/pubmed/36212520 http://dx.doi.org/10.1007/s10479-022-04983-y |
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author | Zamani, Efpraxia D. Smyth, Conn Gupta, Samrat Dennehy, Denis |
author_facet | Zamani, Efpraxia D. Smyth, Conn Gupta, Samrat Dennehy, Denis |
author_sort | Zamani, Efpraxia D. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9524319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-95243192022-10-03 Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review Zamani, Efpraxia D. Smyth, Conn Gupta, Samrat Dennehy, Denis Ann Oper Res Original Research 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. Springer US 2022-09-30 /pmc/articles/PMC9524319/ /pubmed/36212520 http://dx.doi.org/10.1007/s10479-022-04983-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Zamani, Efpraxia D. Smyth, Conn Gupta, Samrat Dennehy, Denis Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
title | Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
title_full | Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
title_fullStr | Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
title_full_unstemmed | Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
title_short | Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
title_sort | artificial intelligence and big data analytics for supply chain resilience: a systematic literature review |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524319/ https://www.ncbi.nlm.nih.gov/pubmed/36212520 http://dx.doi.org/10.1007/s10479-022-04983-y |
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