Defense fleet managers require maintenance strategies that deliver high readiness, reliable and sustainable combat equipment in the face of operational uncertainty and chaotic tactical environments. Shaping depot maintenance strategy is complex: aircraft, vehicles, and weapons systems operate in unpredictable and dynamic environments while component aging, convoluted maintenance practices, and overlapping sustainment programs all influence requirements. Yet, most predictive analytics efforts are focused on short-term tactics and historical data. As a result, these models cannot deliver the needed long-run precision suitable for depot strategies. Despite new big-data feeds, cloud applications, and innovative visualizations, most underlying predictive models are not suited for the challenge due to a simple reason: The past does not represent the future. Without the appropriate predictive tools, fleet managers lean heavily and cautiously towards doing more maintenance. The underlying assumption is that more maintenance yields more readiness. Four case studies, show successful predictive modeling of depot maintenance complexities. An advanced, approach towards predictive analysis across the lifecycle of defense programs can accurately shape strategies and identify cases where too much maintenance is scheduled.