DescriptionDisruptions frequently occur in supply chains and hence pose problems for practitioners of whom seek to design efficient and resilient supply networks. Events that lead to such disruptions are commonly referred to as Supply Chain Risks. While many have studied the effect that risk has on consequences of certain decisions made, few have attempted to explore the effects that the inherent structure of a supply network has on the global and local risk of a supply chain. In addition, few have studied the effects that global and local network structure have on risk propagation to individual firms and connections within the network. In this dissertation, I explore the effects that inherent global and local network structural characteristics, operationalized via graph-theoretic measures as well as local centrality measures, have on global and local risk propagation. I do so by employing a simulation study to generate a very large data set of networks and various risk distributions. The primary risk measurement tool used in this study are Bayesian Networks. Last, I explore the effect that various strategic decisions have on global and local risk, and how such decisions can alter the current state of risk in supply networks.