GL-MHSA:A Demand Forecasting Method for Related Products in Parts Supply Chain System

Accurate demand forecasting for products in the parts supply chain system (PSCS) is critical for enterprises to optimize production and inventory operations. To address the challenges of inadequate modeling of complex inter-product relationships and the limited accuracy of existing forecasting methods, this paper proposes a demand forecasting method based on a Graph Convolutional and LSTM Network with Embedded Multi-Head Self-Attention (GL-MHSA). The method first extracts hybrid distance features, including Euclidean and pattern distances, from product sales data in the PSCS to mine product associations and construct graph-structured relational data. A Graph Convolutional Network (GCN) is then used to capture structural association features among products, while an LSTM network models the temporal dependencies in the demand sequences. The extracted features are fused through a Multi-Head Self-Attention (MHSA) mechanism to obtain a comprehensive feature representation. This representation is concatenated with other auxiliary features to form the final input for demand prediction. Experimental results on an automotive PSCS dataset show that the proposed GL-MHSA model achieves more accurate modeling of product associations and significantly improves demand forecasting performance compared to existing approaches.