Enhancing Supply Chain Management through Graph Analytics

Supply chain management (SCM) is a critical component of modern businesses, ensuring the efficient movement of goods, services, and information. However, traditional SCM approaches often struggle with complexity, inefficiencies, and disruptions. This research explores the application of graph analytics to enhance supply chain performance by leveraging network-based insights. By modeling supply chain entities as graph structures, we analyze relationships, detect bottlenecks, and optimize logistics through graph-based algorithms such as shortest path analysis, community detection, and centrality measures. Using real-world datasets and graph neural networks (GNNs), we demonstrate how graph analytics improves demand forecasting, risk assessment, and supplier relationships. The findings highlight that graph-based models outperform traditional approaches in identifying vulnerabilities and enhancing decision-making. This research contributes to the growing field of AI-driven supply chain optimization, paving the way for more resilient and data-driven logistics operations.