Supply Forecasting for High-Tech Components via Regional Substitution Strategy with Graph Networks and Temporal Reinforcement
DOI:
https://doi.org/10.53469/wjimt.2025.08(05).08Keywords:
Component Substitution, Graph Neural Networks, Time Series Forecasting, Supply Stability, Technical Compatibility AnalysisAbstract
With the increasing instability of the global economic and political landscape, maintaining the continuity of high-tech component supplies has become a major challenge. This study proposes a forecasting model for high-tech component supply disruptions based on a regional substitution strategy, combining Graph Attention Networks (GAT) with Long Short-Term Memory (LSTM) sequence learning. The model builds a weighted graph using four key factors: component characteristics, technical compatibility, logistics distance, and supply-demand variability, while LSTM is employed to analyze order fluctuation trends over time. Data from 92 types of component supply chains across 142 manufacturing enterprises over a 36-month period were used for training and testing. Results show that the model achieves a candidate node prediction accuracy of 91.2%, a recall rate of 88.5%, and an F1 score of 89.8%, outperforming conventional regression models and models using only GAT or LSTM separately. Simulated disruption scenarios indicate that selected substitute nodes can maintain over 80% of the original supply capacity with a cost increase of less than 9%. In addition, a node substitution difficulty coefficient is introduced to assist in planning cross-regional redundancy, helping companies better manage supply risks. The approach presented in this study contributes to strengthening supply chain resilience for high-tech industries and provides practical insights for enterprise decision-making and policy planning.
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