The problem of effectively integrating condition based maintenance and spare part inventory control is studied and a solution methodology demonstrated. Degradation modeling, Bayesian analysis, and optimization techniques are utilized to define a condition based maintenance model for a single production system under spare part inventory constraints. Specifically, the gamma process is used to model the degradation process for a system that has a monotonically increasing degradation behavior. The initial gamma process parameters are inferred during product testing and utilized to define a spare part optimization model. This optimization model is used to ascertain the stockout probability to support the system. To address the uncertainty in parameter estimates, the gamma process parameters are updated through a Bayesian updating technique as more degradation data is collected over time for a real time remaining useful life prediction of the component. Finally, condition based maintenance and spare part inventory control are tied together into a overall production decision model. The production decision model generates an optimal degradation limit maintenance policy which provides a means to make component replacement decisions while addressing the relationship among outstanding orders, the number of spares, and the degradation state. One can see that the methodology developed in this thesis effectively ties together condition based maintenance, production, and spare parts inventory control. This body of work is important in the area of reliability and maintenance engineering since it provides a way of controlling spare parts in conjunction with condition based maintenance and production. This concept addresses a relationship which is not well developed in the literature, yet has a significant practical value.