Hybrid Predictive Modeling for Automotive After-Sales Pricing: Integrating BiLSTM-Attention and Fuzzy Logic

In the automotive service and spare parts distribution sector, effective supply chain management is paramount as it directly impacts customer satisfaction, profits, and overall competitiveness. To optimize these crucial aspects, a robust fuzzy logic framework has been developed, taking into account the intricate connections between customer demand, customer sentiment, product availability, and pricing strategy. This study focuses on after-sales services provided by Moroccan automobile companies, utilizing workshop entry data from Enterprise Resource Planning (ERP) systems across multiple cities in the country. To accurately capture customer sentiment, a Bidirectional Long Short-Term Memory (BiLSTM) with attention model, a deep learning strategy, is employed to complete any missing customer sentiment data. Leveraging the power of neural networks, this approach effectively analyzes and predicts sentiment patterns. Once the sentiment data is completed, a Fuzzy Logic model is utilized to determine the most optimal pricing strategy. By considering various factors through fuzzy sets and rules, this model allows informed decision-making to strike the right balance between profitability and customer satisfaction. The employed models demonstrated strong performance, as evidenced by metrics such as MSE and R2. Specifically, the BiLSTM-attention model achieved an R2 of 0.87, while the fuzzy logic model registered an impressive R2 of 0.91. Furthermore, when juxtaposed with alternative models, our framework’s superior efficacy became evident.