NGC2 at the Tactical Edge

Enabling Predictive Logistics for Decision Dominance

By COL Tyler D. Olsen

Article published on: January 29, 2026 in the Winter 2026 edition of Army Sustainment

Read Time: < 6 mins

At Project Convergence Capstone 5, Soldiers and leaders experimented with a new way of fighting, one where decisions were not driven by lagging reports but by real-time data flowing across a digital backbone known as Next Generation Command and Control (NGC2).

The Army’s latest doctrine makes clear why this matters. Field Manual 4-0, Sustainment Operations, identifies predictive logistics as a doctrinal imperative, as essential to precision sustainment, decision dominance, and resilience in contested environments. The manual calls for a shift from reactive resupply to anticipatory sustainment, where commanders and sustainers use integrated data and forecasting tools to maintain tempo and operational reach. In the context of large-scale combat operations (LSCO), this shift is decisive because forces that can anticipate requirements and act more quickly than the enemy will maintain momentum.

NGC2, first developed as an experimental initiative under Army Futures and Concepts Command, is now progressing into prototyping and acquisition as the core architecture to realize that vision. Its aim is to integrate data, artificial intelligence (AI), and resilient communications into a single decision-support framework. For logistics commanders, this means moving from fragmented, delayed unit-based reporting to a common operating picture that is timely, accurate, and actionable. It is important to note that NGC2’s role in predictive logistics is still in development. The Army is actively testing the system with the 4th Infantry Division (4ID), refining its capabilities and concepts as it prepares for Project Convergence Capstone 6.

From Data to Decisions

Sustainment remains the Army’s warfighting function that ensures operational reach, freedom of action, and endurance. Predictive logistics is not a separate function but a capability nested within sustainment. It is an approach that applies data integration, forecasting, and AI and machine learning (ML) tools to enable anticipatory sustainment. In other words, sustainment is the doctrinal umbrella. Predictive logistics is the method that makes sustainment more precise, proactive, and resilient in contested environments. NGC2 is the enabler that allows predictive logistics to operate at scale, turning sustainment data into decision advantage.

NGC2 is built on four foundational layers: application, data, infrastructure, and transport. For logisticians, understanding these layers is key. They are not abstract information technology concepts but the future backbone of predictive logistics.

• Application Layer: This layer is the interface that commanders and sustainers see. It provides dashboards and decision-support tools that transform raw data into predictive analytics and sustainment recommendations. Instead of a spreadsheet of fuel reports, a commander might see the following: “At current consumption rates, Bravo Company will need resupply in 18 hours — recommend pre-positioning fuel at X location.” This is distribution management in action, informed by predictive tools.
• Data Layer: In NGC2, this layer serves as the system’s engine room, integrating sensor and platform inputs into a unified operational picture. It ensures data are accessible across echelons, delivering real-time intelligence essential for predictive logistics. Leveraging AI and ML, it filters, validates, and prioritizes vast data streams, eliminating noise, confirming accuracy, and transforming raw inputs into structured, decision-ready insights. These refined data enable accurate forecasting, proactive maintenance, and optimized resupply, empowering commanders to act swiftly and maintain mission readiness in complex environments.
• Infrastructure Layer: This layer anchors computing, storage, and user devices at the tactical edge, enabling resilient, real-time data access in contested environments. This distributed setup empowers predictive logistics by supporting supply forecasting, equipment monitoring, and proactive maintenance while accelerating decision-making and keeping operations agile and mission ready.
• Transport Layer: This layer is the highway system. It ensures that all the data move securely and reliably, even under cyberattack or electronic warfare. For logisticians, this means confidence that predictive insights will still flow even in contested or denied, degraded, intermittent, or limited network environments.

Together, these layers transform sustainment data into decision advantage, helping commanders see further, decide faster, and act with greater precision.

Lessons Learned So Far

At Ivy Sting 1 in September 2025 at Fort Carson, Colorado, the first in a series of 4ID training exercises to incorporate NGC2, the Artillery Execution Suite (AXS) demonstrated how all four layers could be integrated into a single division-level command and control environment. By moving beyond legacy fires systems, such as the Advanced Field Artillery Data System, and replacing stove-piped networks with a unified digital backbone, the 4ID showed how data could flow seamlessly across the formation. Soldiers and leaders at every echelon of the fires process embraced AXS for its decisive advantage compared with legacy systems — from intel support and the targeting cell to the Joint Air-Ground Integration Center, the division artillery fire control element, the platoon fire direction center, and the AXS M777 section chief. It provided the ability to see and move data rapidly, ingest them into AXS, and apply them to improve effectiveness and lethality, without wasting time fighting their own systems.

The fires warfighting function was among the first to adopt these capabilities, showing how integrated data and applications accelerate targeting and decision making. The same approach is vital for predictive logistics, where NGC2 and future integration of AI/ML models rely on accurate, trusted data to forecast supply needs, anticipate equipment failures, and optimize resupply before shortfalls occur. Early experimentation shows predictive tools can shorten decision cycles and improve sustainment planning, but only when unit-level data are timely and reliable. Building confidence in AI recommendations requires training and repetition: units must learn not just to view dashboards but to interpret forecasts and turn them into actionable plans.

The success of AXS underscored that NGC2 is about giving commanders, sustainers, and warfighters time otherwise lost to fighting legacy logistics systems while also sharpening their ability to make faster, more informed decisions. These lessons will guide the next phase of experimentation.

Looking Ahead

4ID’s Ivy Sting 1 marked the first success in the Ivy Sting series, proving the value of an agile, iterative approach to building NGC2. This milestone set the foundation for 4ID’s culminating division-level training events, Ivy Mass, and ultimately Project Convergence Capstone 6, where predictive logistics will be tested at scale as a core enabler of decision dominance.

As the Army continues working with 4ID to refine NGC2, each iteration in training events incrementally adds capability, reducing the burden of legacy systems and giving commanders and sustainers time to focus on decisions rather than data wrangling. Future events, including Project Convergence Capstone 6 and beyond, are expected to incorporate AI/ML, autonomous resupply orchestration, cognitive decision support that simulates outcomes, and edge analytics capable of operating in disconnected environments. Because logistics decisions focus on readiness and resupply rather than lethal actions, logistics provides a lower-risk environment for building trust in AI-enabled decision support. Sustainment is a natural fit for the development of agentic AI, generating massive volumes of measurable data, from inventory and maintenance records to fuel use and demand forecasts that feed ML models to identify patterns and optimize processes at scale. These advances will strengthen logistics commanders’ ability to make timely, informed decisions that directly support the warfighter.

To enable this evolution, the Army must continue to invest in requirements documents that are both technically rigorous and operationally grounded. These requirements must define how emerging capabilities are designed from the outset, ensure alignment with sustainment workflows, and establish how data are aggregated, flowed, and stored from tactical platforms into the NGC2 ecosystem. Just as importantly, they must address integration with enterprise business systems such as the Integrated Personnel and Pay System–Army and the Global Combat Support System–Army. Achieving this requires two-way data flow: not only pulling readiness, personnel, and sustainment data into NGC2, but also pushing refined, decision-quality data back into those systems to maintain accuracy and synchronization across the enterprise.

By treating requirements as living artifacts that guide this integration, the Army will ensure that personnel, readiness, and operational data are unified. This approach allows NGC2 to mature into a trusted decision-support system that delivers predictive logistics at scale, giving leaders both the time and the confidence to make faster, better decisions in support of the fight.

Conclusion

The Army’s doctrine is clear: predictive logistics is essential to winning in LSCO. NGC2 provides the digital backbone to make it actionable, but it is still a prototype. For the logistics community, this means moving from reactive resupply to anticipatory sustainment is not a distant aspiration but an ongoing journey.

As the Army works toward Project Convergence Capstone 6, sustainers will play a central role in shaping how NGC2 matures. With their input and experience, and with disciplined documentation to guide integration, NGC2 can evolve into the trusted decision-support system that gives commanders confidence and ensures the warfighter is never left waiting.