Trends and Future Directions of Artificial Intelligence and Machine Learning in Supply Chain Risk Management
Abstract
This study aims to map development trends, knowledge structures, and future research directions for AI and machine learning in supply chain risk management. This study uses a bibliometric approach with data taken from Scopus for the period 2016–March 2026. Analysis was conducted through keyword co-occurrence, network visualization, overlay visualization, and density visualization using VOSviewer. The results show that publications on artificial intelligence and machine learning in supply chain risk management have increased significantly, especially after 2021. The most dominant core themes include artificial intelligence, machine learning, supply chain, and supply chain resilience.In contrast, more recent emerging themes include predictive analytics, digital transformation, digital twins, big data, blockchain, and federated learning. These findings indicate a shift in research focus from an analytical approach to a more predictive, adaptive, and data-driven approach. This study confirms that the integration of artificial intelligence and machine learning in supply chain risk management still has significant room for improvement, particularly in end-to-end implementation, explainability, and digital technology integration.
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