With deregulation, utilities in the power sector face a much more urgent imperative to emphasize cost efficiencies as compared to the days of regulation. One major opportunity for cost savings is through reductions in spare parts inventory. Most utilities are accustomed to carrying large volumes of expensive, relatively slow-moving units because of a high degree of risk-averseness. This attitude towards risk is rooted in the days of regulation. Under regulation, companies recovered capital inventory costs by incorporating them into the base rate charged to its customers. In a deregulated environment, cost recovery is no longer guaranteed. Companies must therefore reexamine their risk profile and develop policies for spare parts inventory that are appropriate for a competitive business environment.This research studies the spare parts inventory management problem in the context of electric utilities, with a focus on nuclear power. It addresses three issues related to this problem: criticality, risk, and policy. With respect to criticality and risk, a methodology is presented that incorporates the use of influence diagrams and the Analytic Hierarchy Process (AHP). A new method is developed for group aggregation in the AHP when Saaty and Vargas' (2007) dispersion test fails and decision makers are unwilling or unable to revise their judgments. With respect to policy, a quantitative model that ranks the importance of keeping a part in inventory and recommends a corresponding stocking policy through the use of numerical simulation is developed. This methodology and its corresponding models will enable utilities that have transitioned from a regulated to a deregulated environment become more competitive in their operations while maintaining safety and reliability standards. Furthermore, the methodology developed is general enough so that other utility plants, especially those in the nuclear sector, will be able to use this approach. In addition to regulated utilities, other industries, such as aerospace and the military, can also benefit from extensions to these models, as risk profiles and subsequent policies can be adjusted to align with the business environment in which each industry or company operates
