Graph Neural Networks for Modeling Complex Dependencies in Global Supply Chain Networks

Global supply chain networks exhibit intricate dependencies characterized by multi-tier supplier relationships, dynamic demand propagation, cascading disruption effects, and complex material flows across geographically distributed entities. Traditional analytical approaches struggle to capture these interdependencies due to their reliance on simplified assumptions about network structure and information flow. Graph neural networks (GNN) have emerged as a powerful framework for modeling complex relational data by learning representations that encode both node attributes and graph topology through message passing mechanisms. This review examines the application of GNN to supply chain network modeling, focusing…