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

Amir Hossein Ordibazar, Omar K. Hussain, Ripon Kumar Chakrabortty, Elnaz Irannezhad and Morteza Saberi

Modern Supply Chain Research and Applications, 2025, vol. 7, issue 2, 148-179

Abstract: PurposeSupply 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/approachIn 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.FindingsFor 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/valueThe current SLR found research gaps in AI-based SCRM and proposed directions for future studies.

Keywords: Supply chain risk management; Artificial intelligence; Systematic literature review; PRISMA (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:mscrap:mscra-10-2024-0041

DOI: 10.1108/MSCRA-10-2024-0041

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