AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis
It addresses the gap in reviews on AI-driven supply chain risk assessment for researchers and practitioners, though it is incremental as it synthesizes existing studies without proposing new methods.
This study conducted a systematic literature review and bibliometric analysis of 1,903 articles (2015-2025) to explore AI and ML applications in supply chain risk assessment, finding that models like Random Forest and XGBoost significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts.
Supply chain risk assessment (SCRA) is pivotal for ensuring resilience in increasingly complex global supply networks. While existing reviews have explored traditional methodologies, they often neglect emerging artificial intelligence (AI) and machine learning (ML) applications and mostly lack combined systematic and bibliometric analyses. This study addresses these gaps by integrating a systematic literature review with bibliometric analysis, examining 1,903 articles (2015-2025) from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines. Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts. The bibliometric analysis identifies key trends, influential authors, and institutional contributions, highlighting China and the United States as leading research hubs. Practical insights emphasize the integration of explainable AI (XAI) for transparent decision-making, real-time data utilization, and blockchain for traceability. The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability. By synthesizing AI-driven methodologies with resilience frameworks, this review provides actionable guidance for optimizing supply chain risk management, fostering adaptability, and informing future research in evolving risk landscapes.