As the complexity of global supply chains increases, supply chain risk management becomes increasingly important. The development of artificial intelligence (AI) technology provides new tools and methods for managing supply chain risks. This paper aims to review the application of AI in supply chain risk management, exploring current research progress, challenges, and future development trends. Through a systematic literature search and analysis, this paper examines relevant papers from databases such as Google Scholar, Web of Science, EI, and Scopus, discussing the specific applications of machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms in supply chain risk management. The study finds that these AI technologies demonstrate significant advantages in risk prediction, anomaly detection, image recognition, text mining, supply chain optimization, and emergency strategy formulation. However, issues related to data privacy and security, technical complexity, and implementation difficulties remain major challenges in current applications. In the future, as AI technology advances and interdisciplinary integration develops, supply chain risk management will have more opportunities. This study provides specific application suggestions for enterprises and decision-makers and points the way for future research.
Read full abstract