Due to the nature of the manufacturing and support activities associated with long life cycle products, parts need to be dependably and consistently available. However, the parts that comprise long life cycle products are susceptible to a variety of supply chain disruptions. In order to minimize the impact of these unavoidable disruptions to product production and support, manufacturers can implement proactive mitigation strategies. Careful selection of the mitigation strategy (second sourcing and/or buffering) is key, as it can dramatically impact the part total cost of ownership. This thesis developed a simulation model that performs tradeoff analyses and identifies a near-optimal combination of second sourcing and buffering for specific part and product scenarios. In addition, this thesis explores the effectiveness of traditional analytical models when compared to a simulation-based approach for the selection of an effective optimal disruption mitigation strategy. Several case studies were performed that: 1) tested the impact of popular analytical limiting assumptions, and 2) implemented realistic disruption data in the context of real part management. The first set of case studies demonstrated that the simulation model is capable of overcoming significant scenario restrictions prevalent within traditional analytical models: finite horizon (including non-zero WACC), fixed support costs, and unreliable backup suppliers are essential components for determining the effective optimal disruption mitigation strategy for a given disruption scenario. The second set of case studies demonstrates the importance of proper mitigation strategy selection in real electronic part supply chain scenarios. The results from the case studies not only justified the need for a simulation-based approach to disruption modeling, but also helped to cement the simulation model as an effective decision making tool for electronic part distributors.