AI-Driven Demand Forecasting & Inventory Optimization for Multi-Region Dealer and Retail Distribution Network

Accurate demand forecasting and efficient inventory management are critical challenges in multi-region dealer and retail distribution networks, particularly in geographically large and heterogeneous markets such as the United States. Traditional forecasting and inventory control methods often struggle to handle demand uncertainty, regional variability, and the lack of integration between predictive and operational decision-making processes. To address these challenges, this paper proposes an AI-driven framework that integrates hybrid demand forecasting with forecast-driven inventory optimization for multi-region distribution networks. The proposed approach employs a hybrid forecasting model that combines Long Short-Term Memory networks with Gradient Boosting to capture both temporal demand patterns and regional heterogeneity. Probabilistic demand forecasts generated by the hybrid model are then incorporated into a stochastic inventory optimization framework to determine optimal reorder points and safety stock levels for each region. A simulated multi-region U.S. dataset is used to evaluate the effectiveness of the proposed framework, enabling controlled analysis of demand variability and lead-time uncertainty. Experimental results demonstrate that the proposed framework significantly improves forecasting accuracy compared to conventional time-series methods and leads to substantial reductions in inventory holding costs while maintaining higher service levels. Statistical significance testing confirms the robustness of these improvements across all evaluated regions. The findings highlight the importance of integrating AI-based forecasting with inventory optimization and explicitly modeling regional demand uncertainty. This study contributes to the literature on intelligent supply chain management by providing a unified, reproducible, and scalable framework for AI-driven decision support in multi-region distribution networks.