Transforming legacy data into a mission-ready future

Challenge

For decades, a U.S. military branch had warehoused transactional data that captured logistics aspects of major weapons systems. Yet the opportunity to use that data for predictive analytics remained elusive due to a combination of data access challenges, antiquated analytics approaches, and a lack of functional knowledge to apply the source data in meaningful ways. 

As the chosen contractor by the military command, we were tasked with demonstrating tangible benefits of real-world predictive analytics capabilities for a major weapons system. Our primary project responsibility was to design, develop, and integrate a predictive maintenance analytics suite prototype that could be applied across tactical to strategic echelons. The project would rely on a military-provided data science environment and the authoritative data sources that existed inside the designated command’s data analytics platform.  

Approach

Over a two-year period, we developed the leading prototype solution for deployment to designated organizations across the military branch in collaboration with the military command. Initially focused on aircraft systems, we used broad domain knowledge of the military branch’s enterprise resource systems, commercial best practices, and operational data application across the business and warfighting domains. We structured an environment that was both capable and cost-efficient, and that could be effectively scaled across the military branch’s large user base inside of their network. The data analytics platform we developed incorporates real-time automated connections to authoritative enterprise data for analysis and presentation, streamlining processes and ensuring data quality. We presented the results of the advanced analytics suite we created using Power BI visualization tools, which exploited platform capabilities and maximized user accessibility to increase the number of authoritative sources. 

Applying machine learning techniques, our team of data science experts generated predictive survival functions for each component and then combined that information with current installation times to predict component additional life and failure probability. We generated additional machine learning algorithms to combine predictions with known maintenance intervals, to forecast ideal candidate aircraft for extended contingency operations. 

Impact

Our work resulted in: 

• 93% accuracy in predicting aircraft downtime
• Concordance index of 85–90%
• Tools to forecast maintenance, supply chain, and readiness
• Prototype deployed in aviation, scalable to ground units
• Enabled real-time, risk-informed decisions across tactical and strategic levels

The military branch now has a scalable, automated data pipeline delivering near real-time predictive insights across weapon systems and units. Our analytics tools provide a unified view of readiness, maintenance, supply, and logistics needs—enabling leaders to make fast, risk-informed decisions in contingency operations.