Implementing Prognostic and Predictive Maintenance (PPMx) for the U.S. Army’s ground vehicle fleet requires the design and integration of on-platform predictive analytics. To support the design process, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC) and Applied Research Laboratory (ARL) Penn State researchers are developing a systematic approach that uses reliability modeling in a guiding role. The key steps of the process are building the initial reliability model from available data (e.g., system diagrams and physical layouts), augmenting with information on observed states and failure modes via subject matter experts, and then conducting trades on additional sensors and algorithms to determine a suitable predictive analytics capability. In this paper we provide an example of this process as applied to an Army ground vehicle, first focusing on a simplified sub-problem to demonstrate the technique, then providing statistics on the large scale process