SupplyGraph
SupplyGraph is a comprehensive benchmark dataset designed to advance the application of Graph Neural Networks in supply chain planning. Recognizing that supply networks are inherently graph-structured, this resource addresses the critical lack of real-world data and conceptual frameworks for GNN-driven supply chain solutions. Developed from data provided by a leading Fast-Moving Consumer Goods company in Bangladesh, the dataset supports research into optimizing complex problems such as production planning, demand forecasting, and anomaly detection. The project evaluates state-of-the-art GNN models across six distinct analytics tasks on both homogeneous and heterogeneous graph structures. Experimental results demonstrate that GNN-based approaches significantly outperform traditional statistical machine learning and standard deep learning models. Specifically, these graph methods achieve performance gains of 10 to 30 percent in regression and classification tasks, with anomaly detection improvements ranging bet