In February, the Marine Corps published its 2026 Aviation Plan (AVPLAN), a 48-page modernization blueprint built around a strategy called Project Eagle. The plan covers platforms, weapons, unmanned systems, training, and more. But in Section 2.3 is a sentence that stands out for its candor:

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That's a frank admission from a planning document. And it sets up everything that follows.

Why the legacy model breaks

Project Eagle's central operational concept is Distributed Aviation Operations (DAO): dispersing aircraft and support assets across multiple austere, expeditionary sites to increase survivability and complicate adversary targeting. It's the right answer to the modern threat environment that punishes concentration and predictability.

But DAO creates a logistics problem that legacy approaches aren’t designed to solve. Traditional aviation sustainment was built around aggregated supply packages and centralized depots. That model works when you know where your aircraft will be and when they'll need support. It falls apart when the Aviation Combat Element (ACE) is dispersed across a nodal web of remote sites under adversary pressure.

You can't sustain distributed aviation with a centralized, reactive logistics model. The AVPLAN is direct about this: the old approach limits readiness and reduces the ability to respond rapidly to changing operational conditions. The future fight demands something different.

Three lines of operation, one underlying requirement

To enable DAO, the AVPLAN lays out three Lines of Operation for what it calls "Transforming Aviation Sustainment for the Future Fight."

1. Dynamic aviation supply calls for redesigning supply packages to support a nodal web of aircraft and support sites, shifting away from the traditional aggregation-based approach. This means anticipating demand before it materializes, not reacting to it after.
2. Predictive maintenance represents, in the AVPLAN's own words, "a fundamental transition from a reactive to a proactive, predictive maintenance culture." The goal: higher mission-capable rates, less unscheduled downtime, and maintainers who know what's coming instead of discovering it at the flightline.
3. Optimized operations expands data-driven decision-making across the full spectrum of aviation activities, fusing data from historically separate systems like NALCOMIS, M-SHARP, and GCSS-MC to automate and optimize scheduling and maintenance planning.

These aren't aspirational. The AVPLAN describes them as active lines of operation already delivering capability. But the underlying requirement across all three is the same: fuse siloed data, apply AI and machine learning to anticipate needs, and surface recommendations fast enough to matter.

What "predictive" actually requires

This is where the operational vision meets hard technical reality. Defense logistics data is notoriously fragmented, spread across dozens of systems with different schemas, different update cycles, and different owners. No single system gives you a unified picture of component health, parts demand, and operational tempo at the same time.

You can't build a predictive maintenance capability without first solving the data unification problem. You can't build dynamic supply without probabilistic demand forecasting that accounts for failure rates, lead times, and operational tempo simultaneously. And you can't optimize flight operations and maintenance scheduling without fusing data from systems that were never designed to talk to each other. 

None of these are dashboard problems. They're machine learning problems.

Where Tagup fits

We built Manifest to solve exactly this. Manifest unifies siloed logistics data across supply, maintenance, and operations into a single world model, then applies our proprietary Generative Reinforcement Learning™ engine to simulate millions of scenarios and surface the best course of action under real constraints.

In practice, that means maintainers who know which components to prioritize before failures occur. Logisticians who can right-size supply packages based on anticipated demand rather than historical averages. Commanders who can wargame sustainment decisions before committing resources.

We've deployed Manifest with the U.S. Marine Corps, and the results are measurable: more than 25% reduction in purchasing costs without compromising readiness, 13% increase in force readiness for the same budget, and decision cycles that move 100x faster.

The forcing function is here

The 2026 AVPLAN isn't just a roadmap for Marine Aviation. It's a forcing function for the defense technology industry. The Marine Corps has named the problem, defined the solution architecture, and committed to execution across three Future Years Defense Programs.

The AVPLAN describes a future desired state where Marine Aviation achieves greater than 85% mission capable rates, a reduced logistics footprint, and a data-enabled culture. 

That future isn't theoretical. It's what Manifest delivers today.

To learn more about how Manifest advances supply and maintenance operations, request a demo today.