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Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda

Conn Smyth, Denis Dennehy Orcid Logo, Samuel Fosso Wamba, Murray Scott, Antoine Harfouche

International Journal of Production Research, Pages: 1 - 25

Swansea University Author: Denis Dennehy Orcid Logo

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DOI (Published version): 10.1080/00207543.2024.2341415

Abstract

Artificial Intelligence (AI) and prescriptive analytics are increasingly being reported as having transformative powers to enable resilient supply chains (SC). Despite such a benefit, and the increase in popularity of AI and analytics in general, research is largely fragmented into streams based on...

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Published in: International Journal of Production Research
ISSN: 0020-7543 1366-588X
Published: Informa UK Limited 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65919
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first_indexed 2024-03-26T22:11:31Z
last_indexed 2024-03-26T22:11:31Z
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spelling v2 65919 2024-03-26 Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda ba782cbe94139075e5418dc9274e8304 0000-0001-9931-762X Denis Dennehy Denis Dennehy true false 2024-03-26 CBAE Artificial Intelligence (AI) and prescriptive analytics are increasingly being reported as having transformative powers to enable resilient supply chains (SC). Despite such a benefit, and the increase in popularity of AI and analytics in general, research is largely fragmented into streams based on different types of AI technologies across several SC contexts and through varying disciplinary perspectives. In response, we curate and synthesise this fragmented body of knowledge by conducting a systematic literature review of AI research in supply chains that have been published in 3* and 4* Chartered Association of Business Schools (CABS) ranked journals between 2000 and 2023. The search strategy retrieved 5, 293 studies, of which 76 were identified as primary papers relevant to this study. The study contributes to the accumulative building of knowledge by extending theoretical discourse about the specificities of AI for prescriptive analytics to enable SC resilience. This study proposes a strategic AI resilience framework to support SC decision-makers enhance the use and value of prescriptive analytics as an enabler to developing resilient SC. We make the call to action for an orchestrated effort within and between academic disciplines and organisations that are guided by a research agenda to guide future research initiatives. Journal Article International Journal of Production Research 0 1 25 Informa UK Limited 0020-7543 1366-588X Artificial intelligence; analytics; supply chains; resilience; literature review 23 4 2024 2024-04-23 10.1080/00207543.2024.2341415 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-06-03T10:43:38.2666073 2024-03-26T22:08:14.1351146 Faculty of Humanities and Social Sciences School of Management - Business Management Conn Smyth 1 Denis Dennehy 0000-0001-9931-762X 2 Samuel Fosso Wamba 3 Murray Scott 4 Antoine Harfouche 5 65919__30517__e13b2b1f3c58494189b10cfbb2346cbb.pdf 65919_VoR.pdf 2024-06-03T10:42:10.4263458 Output 4209026 application/pdf Version of Record true © 2024 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
spellingShingle Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
Denis Dennehy
title_short Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
title_full Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
title_fullStr Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
title_full_unstemmed Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
title_sort Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda
author_id_str_mv ba782cbe94139075e5418dc9274e8304
author_id_fullname_str_mv ba782cbe94139075e5418dc9274e8304_***_Denis Dennehy
author Denis Dennehy
author2 Conn Smyth
Denis Dennehy
Samuel Fosso Wamba
Murray Scott
Antoine Harfouche
format Journal article
container_title International Journal of Production Research
container_volume 0
container_start_page 1
publishDate 2024
institution Swansea University
issn 0020-7543
1366-588X
doi_str_mv 10.1080/00207543.2024.2341415
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description Artificial Intelligence (AI) and prescriptive analytics are increasingly being reported as having transformative powers to enable resilient supply chains (SC). Despite such a benefit, and the increase in popularity of AI and analytics in general, research is largely fragmented into streams based on different types of AI technologies across several SC contexts and through varying disciplinary perspectives. In response, we curate and synthesise this fragmented body of knowledge by conducting a systematic literature review of AI research in supply chains that have been published in 3* and 4* Chartered Association of Business Schools (CABS) ranked journals between 2000 and 2023. The search strategy retrieved 5, 293 studies, of which 76 were identified as primary papers relevant to this study. The study contributes to the accumulative building of knowledge by extending theoretical discourse about the specificities of AI for prescriptive analytics to enable SC resilience. This study proposes a strategic AI resilience framework to support SC decision-makers enhance the use and value of prescriptive analytics as an enabler to developing resilient SC. We make the call to action for an orchestrated effort within and between academic disciplines and organisations that are guided by a research agenda to guide future research initiatives.
published_date 2024-04-23T10:43:37Z
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score 11.012924

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