Nearly two-fifths of the Department of Defense budget — $290 billion — focuses on operations and maintenance to ensure weapon system readiness. This means less money for new equipment, less equipment availability and less fighting capacity. Those liabilities represent the core internal threat.
If the DoD can reduce the money spent on sustainment, it can increase the money available for new procurement. This statement sounds easy but is hard to execute.
Seventy percent of a system’s life-cycle cost is set during the materiel solution analysis phase before Milestone A and during the technology maturation and risk reduction phase before Milestone B. Decisions made during these phases disproportionately affect reliability. However, there is no requirement for original equipment manufacturers, or OEM, to include design for sustainment data as part of their proposals.
What if OEMs could increase a system’s reliability and decrease its life-cycle cost early in the development process by creating physics-based simulations and digital twins calibrated with operational data? Engineers could foresee sustainment issues during product design, increasing product quality and readiness while lessening operational cost and risk.
In simple terms, development time and costs go down while system reliability goes up.
Condition-based maintenance, or CBM, is the current approach to improve the reliability and availability of fielded weapon systems. But today’s CBM methods fall short.
CBM acts purely on a data-based approach, fitting algorithms to large, streaming data sets, thus relying heavily on data quality, velocity, veracity and frequency — with data interdependencies often left for experts to deduce. System technicians normally receive a few days’ notice when a component might fail. Lacking a root cause analysis, these reports cannot address why a component fails.
There’s a better way. Physics-based simulation models can, based on the operational envelope, represent the behavior of a component, system, and system of systems at various degrees. Hybrid digital twins, calibrated with real-time data from the field, can deliver a higher-quality-of-failure mode and root-cause predictions.
https://www.defensenews.com/opinion/com ... readiness/