Lockheed Martin Uses AI and Digital Twins to Overhaul Aircraft Sustainment
The defense giant is replacing flight-hour maintenance with proactive, AI-driven forecasting across multiple aircraft fleets.
Lockheed Martin is integrating artificial intelligence and digital twins into its sustainment operations to replace traditional maintenance schedules with dynamic forecasting. This shift aims to increase operational readiness by automating diagnostics and accelerating logistics decision-making for U.S. and allied aircraft.
The company is transitioning from a static "flight hour" maintenance approach—where parts are replaced based on fixed time intervals—to a proactive model powered by AI-driven forecasts. As part of this rollout, Lockheed Martin has launched an AI-enabled dashboard for the F-35 program, providing a 360-degree view of logistics orders and the health of individual parts. To scale these capabilities, the company is extending these tools to the F-22, F-16, and C-130 fleets, creating a unified operational picture across diverse platforms. This technical architecture is being bolstered by partnerships with external innovators, including the integration of Rune Technologies' Adaptive Logistics Intelligent Decision System (ALIDS).
The Push for Decision Advantage
This strategic pivot comes as Lockheed Martin emphasizes that decision-making speed has become a primary guarantor of success in contested global environments. By fusing systems engineering with AI, the company seeks to process massive data streams and identify patterns more rapidly than human operators can alone. According to Nick Smythe, Vice President of Sustainment Campaigns at Lockheed Martin, AI provides a "decision advantage" by recommending actions faster than manual analysis allows. Smythe emphasized that the technology is designed to protect and empower the warfighter rather than replace them.
Strategic Implications for Readiness
By transforming battlefield logistics into a strategic asset, Lockheed Martin intends to build a leaner, more responsive sustainment architecture. The primary goal is to reduce the overall logistics footprint, allowing service members to pivot away from routine diagnostics and focus on mission-critical tasks. In practice, this means aircraft spend less time in hangars for scheduled maintenance and more time in the air, as digital twins allow engineers to predict failures before they occur.
Future Integration
As the company expands AI tools across its heterogeneous fleets, the focus remains on creating a common operating picture that spans different aircraft types. The ongoing integration of third-party systems like ALIDS suggests a move toward an open, modular sustainment ecosystem. Observers will be watching to see how these predictive tools perform in real-world deployment and whether the transition to dynamic forecasting significantly lowers the long-term cost of fleet ownership.