Machine Learning in Supply Chain Optimization: Insights and Trends

Abstract

Machine learning (ML) has emerged as a transformative tool in optimizing supply chain operations, offering intelligent automation, predictive analytics, and real-time decision-making capabilities. By integrating ML algorithms into supply chain management (SCM), businesses can reduce costs, enhance demand forecasting, manage risks, and improve logistics and inventory operations. This article explores current trends, applications, and challenges of ML in supply chain optimization. Key topics include predictive analytics, anomaly detection, and end-to-end automation, supported by case studies from global industries. The study concludes by highlighting the future outlook and the importance of data-driven models in achieving resilient supply chains.