用图神经网络提升供应链需求预测准确率，突破传统方法局限。
Optimizing Supply Chain Networks with the Power of Graph Neural Networks
- 基于图神经网络建模供应链节点间关系，捕捉复杂依赖结构。
- 在单节点需求预测上，性能显著优于MLP和GCN等传统模型。
- 适合关注供应链优化、智能物流的从业者与研究者参考。
图神经网络（GNNs）已成为建模复杂关系数据的有力工具，在预测与优化任务中展现出前所未有的能力。本研究利用SupplyGraph数据集，探索GNN在供应链网络需求预测中的应用。通过先进GNN方法，显著提升了预测模型精度，揭示了潜在依赖关系，并有效处理了供应链运营中的时间复杂性。对比分析表明，基于GNN的模型在单节点需求预测任务中明显优于多层感知机（MLPs）和图卷积网络（GCNs）。将图表示学习与时间序列数据融合，凸显了GNN在库存管理、生产排程与物流优化中的预测潜力。该工作强调了预测在供应链管理中的核心作用，为该领域研究与应用提供了坚实框架。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benchmark for graph-based supply chain analysis. By leveraging advanced GNN methodologies, we enhance the accuracy of forecasting models, uncover latent dependencies, and address temporal complexities inherent in supply chain operations. Comparative analyses demonstrate that GNN-based models significantly outperform traditional approaches, including Multilayer Perceptrons (MLPs) and Graph Convolutional Networks (GCNs), particularly in single-node demand forecasting tasks. The integration of graph representation learning with temporal data highlights GNNs' potential to revolutionize predictive capabilities for inventory management, production scheduling, and logistics optimization. This work underscores the pivotal role of forecasting in supply chain management and provides a robust framework for advancing research and applications in this domain.
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