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

Abstract

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 performance compared to traditional statistical methods. The integration of DL with reinforcement learning (RL) has enabled adaptive inventory policies that respond dynamically to changing market conditions. Graph neural networks (GNNs) have proven effective in capturing network dependencies across complex supply chain structures. Despite these advances, challenges remain in model interpretability, data quality requirements, computational complexity, and real-time implementation. This paper provides a comprehensive analysis of DL architectures, hybrid approaches, performance metrics, and practical applications while identifying critical research gaps and future directions for advancing intelligent supply chain management systems.