Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of factory issues. By utilizing this dataset, researchers can employ GNNs to address numerous supply chain problems, thereby advancing the field of supply chain analytics and planning. Source: https://github.com/CIOL-SUST/SupplyGraph

翻译：图神经网络（GNN）已在交通、生物信息学、语言处理和计算机视觉等多个领域获得广泛关注。然而，将GNN应用于供应链网络的研究明显缺失。供应链网络本质上具有图结构特征，使其成为应用GNN方法的理想对象，为优化、预测和解决最复杂的供应链问题开辟了全新可能。这一研究方向的主要障碍在于缺乏真实世界的基准数据集，以促进利用GNN开展供应链问题的研究与解决。为解决该问题，我们提出了一个面向时序任务的真实世界基准数据集，该数据集来自孟加拉国一家领先的快消品公司，专注于生产目的的供应链规划。数据集包含作为节点特征的时序数据，可实现销售预测、生产规划及工厂问题识别。研究者可利用该数据集通过GNN解决众多供应链问题，从而推动供应链分析与规划领域的发展。来源：https://github.com/CIOL-SUST/SupplyGraph