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

翻译：供应链风险评估（SCRA）通过整合人工智能（AI）与机器学习（ML）技术，在预测能力和风险缓解策略方面经历了深远的变革。这一演变的重要性源于稳健风险管理策略在保障现代供应链运营韧性和连续性中的关键作用。过往综述虽已概述了既定方法论，却忽略了新兴的AI/ML技术，这导致在理解其于SCRA中的实践意义方面存在显著研究空白。本文在开展系统性文献综述的同时，结合了综合性的文献计量分析。我们细致审查了1,717篇论文，并从2014年至2023年间发表的48篇精选文章中提炼出核心见解。该综述通过探讨关键研究问题、梳理现有AI/ML技术、方法论、研究发现及未来发展方向，填补了这一研究空白，从而为SCRA不断演进的格局提供了更全面的视角。本研究揭示了Random Forest、XGBoost及其混合模型等AI/ML模型在显著提升SCRA精准度方面的变革性影响，强调了后疫情时代的灵活策略，倡导构建韧性应急预案并适应不断变化的风险格局。值得注意的是，本综述通过突出新兴AI/ML技术及其在SCRA中的实践意义，超越了以往研究。此外，我们通过全面的文献计量分析展示了研究贡献，揭示了出版趋势、高影响力作者及高被引论文。