Predictive Analytics and Machine Learning in Supply Chain Management: A Comprehensive Review and Future Directions

Abstract

This paper comprehensively reviews the current state of predictive analytics and machine learning applications within supply chain management, highlighting their impact on various aspects such as demand forecasting, inventory management, logistics optimization, and risk mitigation. The review synthesizes key methodologies, techniques, and algorithms utilized in predictive analytics and machine learning for supply chain optimization. Furthermore, it explores case studies and real-world applications across different industries to illustrate these technologies' practical implementation and benefits. In addition to reviewing current practices, this paper also outlines future directions and emerging trends in predictive analytics and machine learning within supply chain management. It discusses challenges and opportunities, including data integration, scalability, interpretability, and ethical considerations, and proposes avenues for future research and development. By providing a comprehensive overview and discussing future directions, this paper aims to serve as a valuable resource for researchers, practitioners, and decision-makers seeking to leverage predictive analytics and machine learning to enhance supply chain efficiency, agility, and resilience in an increasingly complex and dynamic business environment.