Supply chains in the world today face an increasing exposure to low-likelihood, high-impact disruptions. One goal of supply chain management is to increase resilience, or the ability to recover normal operational levels after one of these major disruptions occurs. Careful management of supplier relationships can increase resilience, but it can be difficult to strategically manage each connection especially when the supply base is very complex. Therefore, it is necessary to identify an appropriate management strategy for each supplier based on some identifying characteristics such as risk rating and potential disruption impacts. In the literature this characterization is known as supplier segmentation. The purpose of this work is to study means of considering disruption impact in the supplier segmentation processes. Agent-based simulation is proposed as an appropriate method for studying the effects of supplier relationship strategies on supply chain performance. Information from such models would be used to better-inform segmentation processes. Furthermore, the paper briefly discusses ways to improve consideration of operational risks and risk interdependencies in segmentation using Bayesian Belief Networks (BBN). Together these models for operational risk and disruption impact would allow a comprehensive risk exposure profile to be created for the supply base.