Predictive Maintenance · Real-time Analytics · Supply Chain Optimization
Static flight-hour tables and usage metrics ignored real-time consumption, causing chronic under-resourcing, limited stock effectiveness, and a reactive sustainment supply chain that could not keep pace with modern combat tempos.
Reported impact
Lockheed Martin reports a more proactive logistics pipeline, a live 360-degree sustainment view for the F-35, earlier failure prediction, and faster technology insertion that reduces manual bottlenecks and improves readiness.
Lockheed Martin uses an in-house AI sustainment stack to replace static flight-hour planning with continuously updated logistics intelligence. Telemetry from sensors embedded on airframes and subsystems, mission-specific operational profiles, and strategic inputs such as projected sortie rates and geopolitical risk indicators are ingested into machine learning models that forecast component demand and retrain as new data arrives. Digital twins simulate subsystem behavior and test repair processes, additive manufactured parts, and software patches before field deployment. Forecast outputs, sensor streams, and contract execution data are stitched together in a digital data fabric and served through a web-based LogC2 dashboard for the F-35, with planned extension to F-22, F-16, and C-130 fleets. In the dashboard, reinforcement learning agents recommend replenishment routes and scenario models support what-if planning and actionable logistics orders.
Telemetry and sensor data from airframes and subsystems, plus operational profiles, projected sortie rates, geopolitical risk indicators, and contract execution data, feed Lockheed Martin’s machine learning models and digital twins. Forecast outputs are stitched together in a digital data fabric and surfaced through the LogC2 dashboard, where planners receive recommendations, simulations, confidence intervals, and logistics orders.
"Deep-learning models analyze engine vibration, temperature, and fuel-flow data to predict component failure up to 72 hours before it occurs"
"Lockheed Martin now deploys machine learning models that ingest a richer set of variables"
"The dashboard is already live for the F-35 program"
"The model continuously retrains as new telemetry arrives"
"These autonomous tools compress the acquisition cycle from years to months"
"Those methods ignore real time consumption, leading to chronic under resourcing, limited stock effectiveness, and a reactive supply chain that can’t keep up with modern combat tempos."
"we are building a digital data fabric that stitches together sensor streams, forecast outputs, and contract execution data threads into a single, web based LogC2 dashboard"
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