Deep Learning Methods for Demand Forecasting and Inventory Optimization in Modern Supply Chains

Modern supply chain management faces unprecedented challenges in demand forecasting and inventory optimization due to increasing market volatility, consumer behavior complexity, and global disruptions. Deep learning (DL) has emerged as a transformative approach that addresses these challenges by capturing complex nonlinear patterns in demand data and optimizing inventory decisions across multiple echelons. This review examines the current state of DL methods applied to demand forecasting and inventory optimization in supply chains. Recurrent neural networks (RNNs), long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and transformer-based architectures have demonstrated superior…