Reviewing predictive analytics in supply chain management: Applications and benefits

Abstract

Supply chain management (SCM) is a critical component of modern business operations, and the integration of predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility. The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key concepts and definitions. Drawing upon existing research, the paper analyzes real-world applications, case studies, and successful implementations in areas such as demand forecasting, inventory management, and supply chain visibility. Methodologically, the paper outlines the criteria for selecting relevant studies, details the search strategies employed, and highlights the sources contributing to the comprehensive understanding of predictive analytics in SCM. The exploration of applications focuses on how predictive analytics is revolutionizing demand forecasting, optimizing inventory levels, and enhancing supply chain visibility. Through case studies and examples, the paper illustrates the practical implications of implementing predictive analytics in these key areas. Real-world examples and data-driven insights underscore the transformative impact of predictive analytics on SCM processes. Despite its numerous advantages, challenges and limitations exist in the implementation of predictive analytics. This paper examines common hurdles and proposes strategies to overcome these challenges, offering a balanced perspective on the practical implications of integrating predictive analytics into supply chain management. Looking towards the future, the paper discusses emerging trends and technologies in predictive analytics, anticipating advancements that will further shape the landscape of SCM analytics. It summarizes key findings, outlines implications for practitioners and researchers, and suggests avenues for future research in the dynamic field of predictive analytics in supply chain management.