Eaton Hydraulics has a complex supply chain. There are thousands of suppliers, input products, finished products and customers. Furthermore, the interactions of these elements of the supply chain are even more myriad. Within Eaton Hydraulics, previous analyses on the supply chain have focused on independent interactions such as a customer purchasing a product or a supplier supplying an input product. Rarely is the greater supply chain network analyzed or understood. This study seeks to improve the understanding of the entire supply chain network and provide Eaton Hydraulics with the tools needed. Previous research has been conducted leveraging graph theory to understand these types of supply chains. Graph theory is used to model interactions and connections between actors, very similar to interactions in a supply chain. In this study previous research on using graph theory within the supply chain context is reviewed and the application towards Eaton Hydraulics is discussed. Then, the actual supply chain data is modeled using the graph theory framework and described using social network descriptive analytics. From these descriptive analytics, a metric of Risk Avoidance Rating (RAR) is developed that gives supply chain managers a metric that describes the network complexity of a supply chain actor. Measuring the networks with graph theory and social network analysis gives supply chain managers another tool to help them understand their networks and make better decisions in the future.