The defense enterprise faces mounting pressure in adapting to aging fleets and equipment, supply chain volatility, mission requirements, and enemy tactics, including the broader introduction of non-state forces. These demands require zero downtime, as lives are at stake. The traditional approach to maintenance considerations as part of the manufacturing process simply cannot keep pace.
Unplanned failures have a ripple effect across the acquisition and operational ecosystems. A part failure here leads to mission delays, higher sustainment costs, ineffective performance in the field, and ultimately, reduced dominance and deterrence. Reliability is critically important to the ultimate success of a military mission.
AI-powered predictive maintenance has the potential to turn this challenge into a strength. It introduced new advanced tools to bring dependability to an area known for uncertainty. With AI’s prediction ability, leaders can plan more proactively, keep equipment in the field longer, and send forces into missions with unprecedented readiness across platforms and infrastructure.
Predictive analytics is an emerging tool that helps planners, mechanics, and others in the joint force see proactive forecasts of wear, stress, and failure patterns. The insights are invaluable, as they lessen surprise downtime that often results in chaos that cascades across the enterprise. In securing predictability, predictive analytics can reduce costs and minimize disruptions.
Predictive analysis offers several essential benefits that acquisition and program offices should be aware of as the technology continues to evolve. A main advantage is the ability to reduce over-servicing. Data captured in predictive maintenance enables targeted service and repairs, particularly in contrast to calendar- or usage-based inspections that may unnecessarily pull well-operating equipment out of service, with no guarantee that mechanics will spot potential problems.
Predictive maintenance in manufacturing also enables the earlier detection of stress and failure signals through the analysis of temperature data, usage logs, and other sensor data. Such a scenario is not hypothetical, with systems operating in several branches of the DoD. For example, the Air Force and its Rapid Sustainment Office have established the Predictive Analytics and Decision Assistant, or PANDA. This is an integrated AI and machine learning tool that analyzes aircraft maintenance data. This is meant to “increase the operational reliability of our weapons systems before we project them forward when those aircraft are used in their operations,” said Lt. Col Michael Lasher, an aircraft maintenance specialist in the Rapid Sustainment Office, as reported by DefenseScoop. “And so what we’re trying to do is take advantage of all the data that we have, whether that be historical maintenance data or onboard sensor data, telemetry data—really anything that we have that’s useful for the purpose of formulating that evidence of need to perform the maintenance.”
The Navy is also using the technology, deploying AI on the USS Fitzgerald for real-time predictive maintenance. The system has already scored a major win, according to Marine Insight: It flagged for commanders that a part was about to fail, and the crew was able to replace it before the ship was left stranded.
In the big picture, real-time maintenance insights significantly enhance military readiness because they provide commanders and their forces with extreme confidence that their systems will function as needed. The move toward data-driven, resilient sustainment in a way that extends the service life of mission-critical assets is real and happening.
Establishing a predictive maintenance system in manufacturing provides benefits that extend across the kill web. By enabling the capability to forecast parts demand, synchronize with suppliers, and minimize production delays, analytics and AI help improve global supply chains, leading to better mission planning and execution. Legacy supply chain challenges sometimes have a cascading effect, where long lead times, unexpected breakdowns, and changing mission requirements make it hard to obtain the necessary components.
The DoD is also seeing pockets of innovation aimed at enhancing readiness and parts availability. For example, the Defense Logistics Agency has introduced a unique AI tool designed to improve assurance in the complex military supply chain. It enables the military to “drill down” and obtain information about a shrinking supplier base. The technology also enables planners to see “different segments of all of our supply chains and to look at individual suppliers and performance and to forecast some challenges based upon data that’s available in the public sector,” said DLA Director Army Lt. Gen. Mark Simerly.
In addition, this technology saves money by eliminating waste and reducing the need for emergency procurement. Streamlined supply chains enable mission-critical systems to remain operational for longer periods.
Taking a broader view, predictive analytics offers intangible benefits—namely, providing commanders with the confidence that they need to plan and operate in theater. For example, it helps them know that the materiel will be there when the warfighter needs it. In a defense context, even minor equipment downtime can result in delayed missions and gaps in deterrence. Planning depends on reliability for strategic success.
Commander confidence goes beyond just knowing that equipment will function. It also provides knowledge of system health, eliminating other unknowns from the equation. This is especially critical as DoD systems age; greater insight into supply and demand factors helps extend lifespan.
The key is being proactive, which leads to equipment reliability, in turn enabling stronger decision-making under pressure and ultimately, providing commanders with more flexibility to respond to changing circumstances. The ability to predict can also enable the military to adjust in real time, as a 2022 incident involving an Apache helicopter demonstrated. Product health sensors detected an impending failure of a critical component, which could have prevented an accident. But the benefits don’t necessarily have to be as dramatic. As the Government Accountability Office framed it: “Preventive maintenance may require replacing a tire every 30,000 miles or 3 years regardless of use or condition.” However, “predictive maintenance may use data from electronic sensors combined with known historical data to anticipate the optimal time to replace the tire—not too early as to waste money, but also not too late as to let that tire fail unexpectedly.”
Predictive analytics is no longer a matter of wishful thinking. Real-world deployments in the military already demonstrate measurable reductions in downtime, maintenance hours, and supply disruptions.
The value is twofold, including operational and financial benefits. AI-driven predictive maintenance enhances mission readiness while simultaneously lowering costs and reducing the need for emergency responses within the industrial base. It lowers the threshold burden on what is already a busy sector.
Data-driven maintenance is more than a technical upgrade; it’s also a strategic enabler for flexible planning and long-term force readiness. As an experienced IT provider, Sumaria Systems collaborates with the acquisition community and the defense industrial base to define requirements, develop necessary systems, and integrate them through testing, validation, and fielding.
Digital twins and AI bring real-time foresight and control into defense manufacturing. Whether building new systems or sustaining legacy platforms, these technologies empower DoD leaders to reduce cost, mitigate risk, and ensure production resilience at scale. Contact us for help with the strategic integration of advanced technologies and methods to streamline and optimize the development, maintenance, and operation of systems and infrastructures.