Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

翻译：图神经网络（GNNs）近期在交通、生物信息学、语言与图像处理领域受到关注，但其在供应链管理中的应用研究仍显不足。供应链本质上具有图结构特性，这使其成为GNN方法的理想应用场景，能够优化并解决复杂问题。当前障碍包括：缺乏适当的概念基础、对图结构在供应链管理中应用的熟悉度不足，以及面向GNN供应链研究的真实世界基准数据集稀缺。为此，我们探讨并将供应链与图结构进行关联以支持有效的GNN应用，提供了详细的建模框架、实例、数学定义及任务指南。此外，我们基于孟加拉国一家领先的快速消费品企业，提出了一个聚焦供应链规划的多视角真实世界基准数据集。我们讨论了使用GNN处理各类供应链任务的方法，并在六项供应链分析任务中，对同质与异质图上的多种前沿模型进行了基准测试。分析表明，基于GNN的模型在指定指标上持续优于统计机器学习及其他深度学习模型：回归任务提升约10-30%，分类与检测任务提升10-30%，异常检测任务提升15-40%。通过本项工作，我们以概念探讨、方法论见解和完整数据集为支撑，为使用GNN解决供应链问题奠定了基础。