The Role of Predictive Analytics in Demand Forecasting and Inventory Optimization
Abstract
Accurate demand forecasting and inventory optimization are critical for supply chain efficiency. This paper explores how predictive analytics, powered by machine learning and statistical models, enhances forecasting accuracy and reduces inventory costs. By analyzing case studies across retail, manufacturing, and logistics sectors, we highlight the impact of predictive models on reducing stockouts, minimizing overstocking, and improving customer satisfaction. The study also discusses the challenges of data quality, model accuracy, and integration with enterprise resource planning (ERP) systems.
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