Inventory Optimization Using Machine Learning: Advanced Forecasting for Multi-Channel Supply Chains

Inventory optimization remains a critical challenge in modern supply chains, where inaccurate demand forecasts lead to either excessive holding costs or costly stockouts. This paper presents a comprehensive machine learning (ML) framework for granular demand forecasting and inventory optimization across regions and sales channels. We integrate hierarchical time series modeling, temporal fusion transformers (TFTs), and spatio-temporal graph neural networks (ST-GNNs) to capture complex demand patterns. Our approach reduces forecast error by 25-38% compared to traditional methods, enabling dynamic safety stock optimization that lowers holding costs by 15-22% and stockout incidents by 30-45%. Implementation challenges including concept drift and explainability are addressed through adaptive retraining and SHAP value analysis. Empirical validation using retail datasets demonstrates 12-18% reduction in total inventory costs, establishing ML as a transformative solution for supply chain resilience.