AI-POWERED DEMAND FORECASTING AND SLOTTING OPTIMIZATION IN WAREHOUSE OPERATIONS

In the evolving landscape of supply chain management, warehouse operations are increasingly adopting artificialintelligence (AI) to enhance efficiency and responsiveness. This article explores how AI-powered solutions aretransforming two critical aspects of warehouse logistics: demand forecasting and slotting optimization. Demandforecasting enables organizations to predict future inventory requirements more accurately, thereby reducingstockouts and overstocking. Slotting optimization, on the other hand, enhances storage efficiency by assigningoptimal locations for inventory based on AI-generated insights. By integrating machine learning, deep learning,and predictive analytics, warehouses can automate decisions that traditionally required manual input and intuition.This convergence of AI and logistics not only reduces operational costs but also improves service levels andcustomer satisfaction. The study highlights the methodologies, benefits, and challenges of implementing AI inwarehouse settings, supported by real-world applications and research-based insights.