A RAMS Life Support Model Through Design Optimization

Abstract

This paper presents a partial solution to how the Department of Defense (DoD) should face acquisition challenges caused by increasingly constrained budgets for weapon systems. The early life cycle decisions of current acquisition programs inaccurately quantify Life Cycle Cost (LCC) impacts as a function of preliminary system design and support system design. Cost As an Independent Variable (CAIV), the underlying philosophy that guides the defense acquisition trade-space, emphasizes keeping the LCC within an established cost range by trading cost with performance metrics and schedule variables. Major system design decisions are made without a holistic understanding of system sustainability impacts when programs are forced to prioritize the three high-level program variables of cost, performance, and schedule. The disconnect between acquisition objectives and sustainability objectives results in poorly defined sustainment Key Performance Parameters (KPPs), ultimately ensuring the acquisition of systems that meet technical performance KPPs yet fail to meet sustainment KPPs. Rather than limiting programs to the CAIV trade-space, programs should decompose LCC into manageable components such as Life Acquisition Cost (LAC), Life Support Cost (LSC), and Life Operating Cost (LOC). Programs should then optimize each LCC component for system sustainability over a trade-space consisting of the operational concept, the technical system design, and the support system design. This analytical RAMS process optimizes LSC over a trade-space of sustainment variables, such as system performance, system readiness, operational capability, and operational readiness. Since the LSC traditionally comprises 60%-80% of a weapon system's LCC, this process enables programs to find support solutions that optimize sustainability by maximizing the efficiency and effectiveness of a system. Analysts first model a DoD weapon system using commercial modeling and simulation tools. This easily manipulated system model is a vehicle for design optimization as a means of controlling future LSC. Then, once the system is fielded, analysts can validate and verify the predictive power of a model built in the early acquisition phase against actuals as part of a life cycle Failure Reporting and Corrective Action System (FRACAS) process. Applications include, but are not limited to, asset availability prediction, probability of mission success estimation, and optimal preventative maintenance or tech refresh scheduling across the DoD enterprise.