Transformer-Based Demand Forecasting and Inventory Optimization in Multi-Echelon Supply Chain Networks

Abstract

Multi-echelon supply chain networks face increasing complexity in demand forecasting and inventory optimization due to volatile market conditions and dynamic customer preferences. Traditional forecasting methods often struggle to capture long-range dependencies and complex temporal patterns inherent in supply chain data. Transformer-based architectures, originally developed for natural language processing (NLP), have emerged as powerful tools for time series forecasting in supply chain management (SCM). These models leverage self-attention mechanisms to process sequential data and capture intricate relationships across multiple time steps. This review examines the application of transformer models in demand forecasting and inventory optimization within multi-echelon supply chain networks. The paper analyzes how transformer architectures address challenges such as bullwhip effect amplification, demand volatility, and coordination across supply chain tiers. Key findings indicate that transformer-based approaches outperform conventional methods including autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) networks, and traditional machine learning (ML) algorithms in forecast accuracy and computational efficiency. The review synthesizes recent developments in transformer architectures specifically adapted for supply chain contexts, including modifications for handling sparse data, incorporating external factors, and enabling real-time decision support. Furthermore, the paper explores integration strategies between demand forecasting and inventory optimization, examining how transformer predictions inform safety stock calculations, reorder point determination, and dynamic replenishment policies. Emerging trends such as attention mechanism interpretability, federated learning (FL) for collaborative forecasting, and hybrid models combining transformers with reinforcement learning (RL) are discussed. The review identifies critical gaps in current research, including limited validation in real-world multi-echelon settings, computational scalability challenges, and the need for robust frameworks addressing demand uncertainty propagation across supply chain levels.