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Supply chain risk assessment (SCRA) has witnessed a profound evolutionthrough the integration of artificial intelligence (AI) and machine learning(ML) techniques, revolutionizing predictive capabilities and risk mitigationstrategies. The significance of this evolution stems from the critical role ofrobust risk management strategies in ensuring operational resilience andcontinuity within modern supply chains. Previous reviews have outlinedestablished methodologies but have overlooked emerging AI/ML techniques,leaving a notable research gap in understanding their practical implicationswithin SCRA. This paper conducts a systematic literature review combined with acomprehensive bibliometric analysis. We meticulously examined 1,717 papers andderived key insights from a select group of 48 articles published between 2014and 2023. The review fills this research gap by addressing pivotal researchquestions, and exploring existing AI/ML techniques, methodologies, findings,and future trajectories, thereby providing a more encompassing view of theevolving landscape of SCRA. Our study unveils the transformative impact ofAI/ML models, such as Random Forest, XGBoost, and hybrids, in substantiallyenhancing precision within SCRA. It underscores adaptable post-COVIDstrategies, advocating for resilient contingency plans and aligning withevolving risk landscapes. Significantly, this review surpasses previousexaminations by accentuating emerging AI/ML techniques and their practicalimplications within SCRA. Furthermore, it highlights the contributions througha comprehensive bibliometric analysis, revealing publication trends,influential authors, and highly cited articles.

AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis