Predictive Analytics for Efficient and Smart Supply Chain Optimization

Abstract

Predictive analytics is changing the LSCM in that it allows making decisions based on data, enhances efficiency, and minimizes operational expenses. The models of machine learning and data analysis on historical data help organizations predict demand, optimize inventory, and automate procurement and logistics. Predictive tools are effective in managing risks because they detect the possibility of disruption and allow proactive plans, and assist in making real-time operational changes. The present review examines the use of predictive analytics in the optimization of the supply chain and the role it plays in enhancing resilience, agility, and customer satisfaction. The results imply that the implementation of predictive analytics in supply chains is crucial to making the supply chain more intelligent, efficient, and competitive.