Applications of Machine Learning in Supply Chain Management—A Review

Abstract

Machine learning (ML) has emerged as a powerful tool in supply chain management (SCM), enabling organizations to accomplish valuable insights from numerous data and attain informed decisions. This paper presents an inclusive review of the recent advancements and applications of ML in SCM. The objective is to provide a holistic understanding of how ML techniques are being utilized to enhance various aspects of supply chain operations. The review begins by outlining the fundamental concepts of ML and its relevance to SCM. It then discusses the key challenges faced by supply chain professionals and how ML can address these challenges. The paper presents an overview of different ML techniques, including regression analysis, clustering, classification, time series analysis, neural networks, genetic algorithms, reinforcement learning, and ensemble methods, highlighting their specific applications in SCM. Furthermore, the review discusses the recent research trends and developments in the field, focusing on demand forecasting, inventory optimization, supplier selection and risk assessment, logistics optimization, supply chain risk management, and sustainability initiatives. The paper also explores the integration of ML with emergent technologies such as blockchain, IoT, and edge computing in the context of SCM. The findings of the review indicate that ML has demonstrated significant potential in improving decision-making, optimizing operations, enhancing supply chain resilience, and addressing sustainability challenges.