Graph data science is being applied to analyze a logistics network using Neo4j Graph Data Science (GDS) and Bloom. The goal is to gain insights into operational load, flow control, and regional patterns in the supply chain. The analysis starts with visualizing the raw data in its tabular form, which is difficult to parse, but becomes more transparent when ingested into a graph model. A graph data model is created to represent nodes and relationships, including transportation services, airports, and stages. The Louvain algorithm is used to identify regional interdependence within the network, and community detection reveals distinct regions or "communities" of interconnected stages. Degree centrality measures operational load, while betweenness centrality evaluates flow control and bottleneck risk. These algorithms are applied using Neo4j GDS and Bloom, providing an intuitive no-code interface for analysis. The study demonstrates that graph data science can be used to analyze complex supply chain networks without relying on geographical identifiers.