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The integration of Artificial Intelligence (AI)Artificial Intelligence (AI) and Machine LearningMachine Learning (ML) (ML) is revolutionizing supply chainSupply chain securitySupply chain security by offering innovative solutions to enhance resilience, efficiency, and risk managementRisk management. This chapter explores how AI and ML are transforming supply chainSupply chain operations through predictive analyticsPredictive analytics, anomaly detectionAnomaly detection, and real-time decision-making to mitigate risks such as cyber-threats, operational disruptions, and fraud. We examine key AI/ML-driven applications, including demand forecasting, inventory optimization, and supply chainSupply chain visibility, which enable organizations to proactively respond to market fluctuations and security challenges. Additionally, the chapter discusses the role of deep learning and reinforcement learning in adaptive security models, providing actionable insights for improving logistics performance and ensuring compliance with evolving regulatory frameworks. Case studies are presented to illustrate real-world applications, highlighting best practices and the tangible benefits of AI and ML adoption in supply chainSupply chain securitySupply chain security. Finally, the chapter identifies emerging trends and future research directions to further optimize AI-driven supply chainSupply chain securitySupply chain security strategies, ensuring a robust, intelligent, and secure ecosystem for global trade.

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Artificial Intelligence and Machine Learning: Revolutionizing Supply Chain Security

  • 章节

  • 发表日期：

    2026年

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  • Yulliwas Ameur,
  • Malek Kraiem

摘要

The integration of Artificial Intelligence (AI)Artificial Intelligence (AI) and Machine LearningMachine Learning (ML) (ML) is revolutionizing supply chainSupply chain securitySupply chain security by offering innovative solutions to enhance resilience, efficiency, and risk managementRisk management. This chapter explores how AI and ML are transforming supply chainSupply chain operations through predictive analyticsPredictive analytics, anomaly detectionAnomaly detection, and real-time decision-making to mitigate risks such as cyber-threats, operational disruptions, and fraud. We examine key AI/ML-driven applications, including demand forecasting, inventory optimization, and supply chainSupply chain visibility, which enable organizations to proactively respond to market fluctuations and security challenges. Additionally, the chapter discusses the role of deep learning and reinforcement learning in adaptive security models, providing actionable insights for improving logistics performance and ensuring compliance with evolving regulatory frameworks. Case studies are presented to illustrate real-world applications, highlighting best practices and the tangible benefits of AI and ML adoption in supply chainSupply chain securitySupply chain security. Finally, the chapter identifies emerging trends and future research directions to further optimize AI-driven supply chainSupply chain securitySupply chain security strategies, ensuring a robust, intelligent, and secure ecosystem for global trade.