将图神经网络应用于供应链网络SupplyGraph
Applying graph neural network to SupplyGraph for supply chain network
AI总结：
本研究评估了供应链数据集SupplyGraph，在需求预测任务上对比了MLP、GCN和GAT模型，发现GAT性能最佳且结果具统计学意义，并完善了数据集描述与方法以提升研究可重复性。
摘要：
供应链网络描述了在产品供需背景下产品、制造设施和仓储之间的交互。供应链数据本质上具有图结构；因此，它可以成为图神经网络（GNN）应用的沃土。最近，供应链数据集SupplyGraph已向公众发布。尽管鉴于公开可用数据的稀缺性，SupplyGraph数据集很有价值，但在数据集描述、数据质量保证流程以及所选模型的超参数方面仍不够清晰。此外，为了研究发现的普适性，通过对误差分布进行统计分析来展示结果，比仅展示误差平均值更具说服力。因此，本研究评估了供应链数据集SupplyGraph，在分析流程、数据质量保证和机器学习（ML）模型规格方面提供了更清晰的说明。在执行数据质量保证程序后，本研究在需求预测任务上比较了多层感知机（MLP）、图卷积网络（GCN）和图注意力网络（GAT）的性能，同时尽可能匹配超参数。分析表明，GAT表现最佳，其次是GCN和MLP。在多重比较校正后，这些性能提升在α=0.05水平上具有统计学意义。本研究还讨论了将GNN应用于供应链网络的若干考量。本研究在数据集描述和方法论方面补充了先前关于供应链基准数据集的研究，从而使未来将GNN应用于供应链的研究更具可重复性。
英文摘要：
Supply chain networks describe interactions between products, manufacture facilities, storages in the context of supply and demand of the products. Supply chain data are inherently under graph structure; thus, it can be fertile ground for applications of graph neural network (GNN). Very recently, supply chain dataset, SupplyGraph, has been released to the public. Though the SupplyGraph dataset is valuable given scarcity of publicly available data, there was less clarity on description of the dataset, data quality assurance process, and hyperparameters of the selected models. Further, for generalizability of findings, it would be more convincing to present the findings by performing statistical analyses on the distribution of errors rather than showing the average value of the errors. Therefore, this study assessed the supply chain dataset, SupplyGraph, with better clarity on analyses processes, data quality assurance, machine learning (ML) model specifications. After data quality assurance procedures, this study compared performance of Multilayer Perceptions (MLP), Graph Convolution Network (GCN), and Graph Attention Network (GAT) on a demanding forecasting task while matching hyperparameters as feasible as possible. The analyses revealed that GAT performed best, followed by GCN and MLP. Those performance improvements were statistically significant at $α= 0.05$ after correction for multiple comparisons. This study also discussed several considerations in applying GNN to supply chain networks. The current study reinforces the previous study in supply chain benchmark dataset with respect to description of the dataset and methodology, so that the future research in applications of GNN to supply chain becomes more reproducible.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
