Artificial intelligence applications for supply chain risk management considering interconnectivity, external events exposures and transparency: a systematic literature review

Ordibazar, AH; Hussain, OK; Chakrabortty, RK; Irannezhad, E; Saberi, M

Artificial intelligence applications for supply chain risk management considering interconnectivity, external events exposures and transparency: a systematic literature review

Ordibazar, AH Hussain, OK Chakrabortty, RK Irannezhad, E Saberi, M

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Publisher:: Emerald
Publication Type:: Journal Article
Citation:: Modern Supply Chain Research and Applications, 2025, 7, (2), pp. 148-179
Issue Date:: 2025-08-26

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Field	Value	Language
dc.contributor.author	Ordibazar, AH
dc.contributor.author	Hussain, OK
dc.contributor.author	Chakrabortty, RK
dc.contributor.author	Irannezhad, E
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.date.accessioned	2026-04-14T02:47:54Z
dc.date.available	2026-04-14T02:47:54Z
dc.date.issued	2025-08-26
dc.identifier.citation	Modern Supply Chain Research and Applications, 2025, 7, (2), pp. 148-179
dc.identifier.issn	2631-3871
dc.identifier.issn	2631-3871
dc.identifier.uri	http://hdl.handle.net/10453/194693
dc.description.abstract	Purpose – Supply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities. Design/methodology/approach – In the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability. Findings – For the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards. Originality/value – The current SLR found research gaps in AI-based SCRM and proposed directions for future studies.
dc.language	en
dc.publisher	Emerald
dc.relation.ispartof	Modern Supply Chain Research and Applications
dc.relation.isbasedon	10.1108/MSCRA-10-2024-0041
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	3509 Transportation, logistics and supply chains
dc.title	Artificial intelligence applications for supply chain risk management considering interconnectivity, external events exposures and transparency: a systematic literature review
dc.type	Journal Article
utslib.citation.volume	7
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-04-14T02:47:53Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	2

Abstract:

Purpose – Supply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities. Design/methodology/approach – In the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability. Findings – For the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards. Originality/value – The current SLR found research gaps in AI-based SCRM and proposed directions for future studies.

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http://hdl.handle.net/10453/194693