Predictive optimization in automotive supply chains: a BiLSTM-Attention and reinforcement learning approach

Abstract

Effective supply chain management is pivotal for enhancing customer satisfaction and driving competitiveness and profitability in the automotive service and spare parts distribution sector. Our research introduces an innovative approach, integrating game theory, BiLSTM-Attention deep learning, and Reinforcement Learning (RL) to refine supply and pricing strategies within this domain. Focusing on Moroccan automobile companies, we utilized Enterprise Resource Planning (ERP) system data to forecast customer behavior using a BiLSTM model enhanced with an Attention mechanism. This predictive model achieved a Mean Squared Error (MSE) of 0.0525 and an R² value of 0.896, indicating high accuracy and an ability to explain substantial variance in customer behavior. To further our analysis, we incorporated reinforcement learning, evaluating three algorithms: Q-learning, Deep Q-Networks (DQN), and SARSA. Our findings demonstrate SARSA's superior performance in our context, attributed to its adeptness at navigating the dynamic environment of the automotive supply chain. By synergizing the predictive power of the BiLSTM-Attention model with the strategic optimization capabilities of reinforcement learning, particularly SARSA, our study offers a comprehensive framework for automotive companies to enhance their supply chain strategies, balancing profitability and customer satisfaction effectively in a rapidly evolving industry sector