Big Data and Predictive Analytics for Optimized Supply Chain Management and Logistics

Abstract

Supply chain management and logistics are vital to business success in today's globalized and technology-driven world. The advent of big data and predictive analytics provides unprecedented opportunities to optimize supply chain operations, reduce costs, and gain competitive advantage. This paper examines the role of big data and predictive analytics in supply chain management and logistics optimization. It reviews relevant technologies and analytical techniques, key application areas, implementation challenges, and ethical considerations. Three illustrative case studies demonstrate measurable benefits from using big data analytics, such as improved demand forecasting, optimized inventory and production levels, reduced supply chain risks, and enhanced logistics network design. Tables and quantitative results are provided to highlight the positive impacts. The paper concludes with a discussion of emerging trends, best practices, and a future outlook for leveraging big data analytics further as a key driver of supply chain excellence.