Beyond Predictive Accuracy: Artificial Intelligence in Supply Chain Risk Management: A Systematic Integrative Review and Resilience Framework

Abstract

AI is becoming more of an integral part of supply chain planning, monitoring, forecasting, supplier management, logistics, and disruption response. However, as the number of technically complex applications has grown so quickly, there is an important conceptual challenge: increased prediction doesn't necessarily mean increased supply chain resilience. This research systematically synthesizes empirical studies, systematic review, analytical frameworks, and application-oriented research on artificial intelligence (AI) for supply chain risk management (SCRM) published between 2020 and 2026 in a bounded corpus of 23 publications. The review considers the role of Machine Learning, Deep Learning, Predictive Analytics, Explainable Artificial Intelligence, Internet of Things enabled systems, and Reinforcement Learning in the processes of Risk Identification, Assessment, Mitigation and Monitoring. There are four key findings in the synthesis. AI has driven SCRM to be more anticipatory and data driven, especially regarding demand forecasting, evaluating supplier risk, forecasting, logistics control and operational optimization. Second, empirical evidence suggests that although AI can help to predict, it can also mitigate supply chain risks on the firm level in ways other than prediction, like better resource allocation. Thirdly, organizational value is dependent upon data quality, technological maturity, explainability, managerial control and ability to translate analysis findings into action. Fourth, the literature is still divided between technically oriented publications which focus on the performance of models and management-oriented publications whose focus is on resilience, implementation and governance. On the basis of these findings, the paper proposes an integrative AI-to-resilience translation framework that incorporates data infrastructure, AI analytical capability, strengthening of SCRM processes, and the strengthening of visibility, agility, adaptability, and resilience by SCRM processes. Explainability, organizational readiness, and governance are key enabling factors. The review challenges the main research question from the perspective of when, how, and under what organizational conditions predictive intelligence becomes resilience – instead of the question of whether it does or does not.

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