AI-Driven Demand Forecasting and Inventory Optimization in Cloud-Based Supply Chains
Abstract
Accurate demand forecasting and inventory management are essential for minimizing costs and improving order fulfillment rates. This paper introduces an AI-based demand forecasting framework deployed on cloud infrastructure that uses machine learning models to analyze historical sales data, market trends, and external factors. The framework dynamically adjusts inventory levels, optimizes order processing, and reduces stockouts and overstocking. Performance analysis demonstrates increased forecast accuracy and improved inventory turnover. The study concludes that AI-driven forecasting enhances supply chain efficiency and responsiveness to market changes.
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