The U.S. war-fighting leadership faces two real and growing threats — one external, one internal. We can’t control or fully predict the external threat but, internally, there is much we can do.

Lethality, agility, speed and technology: These words describe our capacity to fight. Yet, much of our latent capacity is held back by reliability issues — the bane of all war fighters.

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.

Equipped with simulation-based digital twins for every asset as well as field insights on operational performance, system fatigue and failures can be efficiently leveraged to help engineers redesign key parts, completing the digital thread.

Combining physics with data-driven simulations delivers actionable understanding in much less time with less data from the field. With simulation embedded in CBM/predictive maintenance processes, one can improve prediction for near- to long-term performance and for the life of the assets by component and aircraft tail number.

What’s the end result? Sustainment organizations can schedule maintenance rather than react to unplanned component and system failures while consuming less resources. Additionally, units can optimize how they operate and sustain the force to ensure war-fighting readiness.

Fifty percent of large industrial companies are planning to use hybrid digital twins to develop predictive, data-informed, decision-ready actionable insights. With digital twins, an asset’s health and performance data digitally connect to its virtual equivalent and reveal whether it requires maintenance now or later, or if it can be extended in the field to increase mission readiness. Industry can expect a 30 percent increase in cycle times of key processes, including maintenance, to improve operational availability.

Lufthansa Technik leverages digital twins to perform predictive maintenance of jet engine turbine blades. This helps synchronize blade life to improve reliability and reduce maintenance costs based on real-world operating conditions.

In the same way that every person is unique, the operational history of every defense system, traceable through its serial number, is likewise unique. This history helps forecast its future reliability outlook. But how can engineering teams take advantage of this data granularity?

By leveraging an open and interoperable simulation platform architecture, engineers can readily merge authoritative sources of truth together within a sustainment digital thread. This helps them perform simulation and process data management, and it enables data traceability.

Simulation for sustainment leads to a greater proportion of the defense budget being available for new weapons, enables greater weapons availability and greater predictability, empowers a greater force lethality, and plays a critical role in safeguarding and supporting war fighters around the world.

Retired U.S. Air Force Brig. Gen. Steve Bleymaier is the vice president for global strategy and government programs, federal aerospace, and defense at simulation specialist Ansys.