SG-Net: A Spatiotemporal Graph Neural Network for Next-Step Supply-Chain Risk Monitoring via Demand Forecasting, Anomaly Detection, and Product-Relation Classification

Abstract

Modern supply chains are highly interconnected, allowing local disruptions to spread across products, facilities, and distribution channels. This study introduces SG-Net, a spatiotemporal graph neural network that combines a relation-aware spatial encoder, a memory-augmented temporal encoder, and attention-based fusion. The model is evaluated on the SupplyGraph dataset using two daily next-step forecasting tasks—sales orders and production—and two classification tasks—anomaly detection and product-relation classification. Under the reported protocol, SG-Net achieves the strongest mean performance across the four benchmark tasks. The conclusions are restricted to daily next-step prediction and the evaluated SupplyGraph setting; no claims of long-horizon warning, empirical scalability, or demonstrated interpretability are made.