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This research aims to develop a framework for assessing and mitigating risks in cross-border supply chains using advanced algorithms.

April 12, 2026Scientific ReportsOpen Access

Application of the LightGBM algorithm in cross-border supply chain risk management: prediction and mitigation strategy development

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Dynamic risk assessment framework predicts and mitigates supply chain risks, enhancing operational efficiency.

This research aims to develop a framework for assessing and mitigating risks in cross-border supply chains using advanced algorithms.
Proposed a dynamic risk assessment framework integrating LightGBM, MGAT, and TCN.
Utilized order data from a Brazilian retailer to extract features using an incremental learning mechanism.
Employed hierarchical Shapley value analysis to identify critical risk paths.
Implemented a deep reinforcement learning strategy for optimizing risk mitigation solutions.
Achieved a prediction accuracy of 92.5%, outperforming traditional methods.
Reduced risk response time to 15 minutes while maintaining operational cost control.

Xi et al. (2026) studied this question.

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