Machine Learning Techniques for Building Resilient and Adaptive Supply Chain Systems

Pan Li; Jingyi Liu

doi:10.54097/fex6d543

Authors

Pan Li
Jingyi Liu

DOI:

https://doi.org/10.54097/fex6d543

Keywords:

Machine learning, Supply chain resilience, Deep learning, Reinforcement learning, Adaptive systems, Risk management, Demand forecasting, Supply chain optimization

Abstract

Supply chain systems face unprecedented disruptions from global uncertainties, requiring resilient and adaptive management strategies. Machine learning (ML) techniques offer transformative solutions for enhancing supply chain resilience through predictive analytics, real-time optimization, and intelligent decision-making. This review paper examines the state-of-the-art ML approaches applied to building resilient supply chain systems, including deep learning (DL), reinforcement learning (RL), and ensemble methods. The paper explores how ML techniques address critical challenges such as demand forecasting, risk management, inventory optimization, and disruption recovery. By analyzing recent developments in ML-powered supply chain resilience, this review identifies key applications across various industries including manufacturing, retail, and logistics. The synthesis reveals that ML techniques significantly improve supply chain adaptability by enabling proactive risk mitigation, dynamic resource allocation, and rapid response to disruptions. However, challenges remain in data quality, model interpretability, and integration with existing systems. This comprehensive review provides insights for researchers and practitioners seeking to leverage ML for creating more resilient and adaptive supply chain networks in an increasingly volatile business environment.

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