Neo4j Graph Data Science enables organizations to analyze and optimize their supply chain networks by applying pathfinding algorithms to identify new and faster shipment routes, facilitating downstream optimization problems for freight forwarding recommendations, and conducting what-if scenario simulations to understand alternative routing and conduct risk assessment. The analysis leverages the Cargo 2000 Case Study dataset to demonstrate the application of graph data science techniques in supply chain management. By using Neo4j's Graph Data Science implementation of Yen's pathfinding algorithm, organizations can solve complex optimization problems efficiently and gain insights into their supply chain performance, such as identifying alternative routes and assessing risks associated with high centrality airports.