Leveraging AI, Digital Twins, and Advanced Analytics to deliver mission-critical solutions for Department of Defense operations.

Excerpt

​Risks have increased in recent decades, with new threats emerging from non-state actors, shifting alliances, and rapid technological globalization. ​As a result, the defense industrial base faces mounting pressure to deliver new and evolving military capabilities faster, less expensively, and with greater reliability.

​AI-integrated digital twin technology is emerging as the key enabler for achieving these demands. Once the domain of science fiction, digital twins now drive the modernization of military manufacturing. They are part of a tangible outgrowth of the push to adopt digital engineering, which utilizes software and hardware to design, test, and sustain complex systems and processes, transitioning them from concept to production while enabling new and diverse types of collaborations.

​The same military-grade reliability safeguards will still exist. Products and processes will still have to prove themselves as fail-safe and mission-critical. Testing and validation will be as challenging as ever. ​Digital twins will simply accelerate the process with the power of microprocessors, advanced analyses, and large-language-model artificial intelligence (AI).

​The question for the defense industry is not, “What are digital twins?” but rather, “How do we implement them effectively?”

​This guide provides the answer. Sumaria Systems has developed a roadmap for implementation, one that balances technology, people, and long-term process changes—and is not a one-time showcase.

​Follow these clearly defined strategies to effectively integrate AI-enabled digital twins into your manufacturing and sustainment processes.

Shift to a Digital Engineering Mindset

The critical first step is cultivating a digital-first mindset, enabling design, testing, and iteration to occur in virtual environments well before raw materials are committed to physical manufacturing.

Even in today’s high-tech world, that’s not as easy as it sounds, as the US Army is learning. Defense News reports that the XM30 Mechanized Infantry Combat Vehicle system, considered a digital engineering “pathfinder” program, struggled to find its footing. The Government Accountability Office determined that “it took longer to release the request for proposals due to a lack of experience with digital engineering while directing contractors to use specific software design approaches.” Yet the Army remains undaunted, vowing to use the program to “illustrate DE’s potential contributions, highlight existing policies and processes that may hinder a program’s ability to implement DE and identify how to advance DE adoption in various contexts.”

To that end, the Department of War has leveraged the pathfinder concept, selecting a series of programs with characteristics that make them good candidates for digital engineering.

In addition to the XM30, other pathfinder programs include the Army’s Integrated Fires Mission Command, the Joint Targeting Integrated Command and Coordination Suite, the M113 armored personnel carrier, and a program office for various combat helicopter platforms.

The addition of the M113 was notable, observers say, since it’s an older program. However, that was a strategic choice, informed by a broader understanding of system architectures. Jennifer Swanson, the Department of War’s deputy assistant secretary for data, engineering, and software acquisition, said during a press event on digital engineering, “There is a lot of reuse of parts between the M113 and our newer vehicles.”

A vital aspect of developing a digital engineering mindset is understanding the critical importance of interoperability. Building a digital twin requires integrating various software components and making them accessible to all partners within a project. That software must be able to share data across platforms that all contractors, subcontractors, and program officials can access, as the Army’s chief scientist told National Defense Magazine.

The Army has helped foster a digital engineering mindset by remaining open to lessons from the private industry. For instance, “We sent Army employees to companies like Ford and Siemens and others … and those employees got to see firsthand how the commercial industries are doing digital engineering,” said David Gorsich, the chief scientist at the Army’s Ground Vehicle Systems Center.

Partners of the Department of War are signaling publicly that they are all in on the digital shift. Pratt & Whitney, part of RTX, is a prime contractor on the Army’s Next-Generation Active Propulsion program. The company passed the digital design review step in its development in February. “The digital tools and processes that our NGAP team has demonstrated, and will continue to advance, will be at the foundation of our technology maturation for our future next-gen solutions," Jill Albertelli, president of Pratt & Whitney's Military Engines business, said in a press release.

Adopting a digital engineering mindset sets the foundation for change in defense manufacturing; it gets teams thinking in terms of models, data, and integration, rather than documents and silos. But to turn digital engineering from a design philosophy into an operational advantage, the models will need to be equipped with intelligence, so they can learn, adapt, and predict.

That’s where AI-driven digital twins come in.

Put AI at the Center of Your Digital Twin Strategy

Building a digital-first mindset in defense manufacturing starts with the foundation. The next step is realizing the power of virtual modeling through smart, not static, digital twins. That means embracing the power of AI to deliver the real-time insights that you need to deliver breakthrough results in readiness and efficiency.

AI elevates efforts to simulate processes and performance to another realm by making predictions—rather than educated guesses—about the best decisions to achieve success. Example questions include:

• Which materials perform best at extreme temperatures?
• How long can vehicles run without requiring a routine maintenance checkup?
• When might logistics routes break down under combat stress?

The value of AI-informed digital twins is just beginning to emerge. To lead, you must center AI at the heart of your digital engineering strategy. It enables engineers to anticipate failures, optimize processes, and run rapid simulations in real time.

AI also turns digital twins into decision-support powerhouses. The scenarios are now coming into sharp focus. For example, when sensor data indicates that components are degrading, algorithms can forecast how long they can remain in service and suggest the best possible strategy, e.g., have them come in for repairs, adjust their usage, or shut them down immediately. In a supply chain, AI-enabled twins can suggest alternate routings or highlight bottlenecks before they cause mission delays.

The Air Force’s Rapid Sustainment Office has already taken this from concept to scale. Its Predictive Analytics and Decision Assistant (PANDA) is now the official system of record for condition-based maintenance across aircraft fleets. The Air Force and C3.ai co-developed the platform. It “ingests data from sources including aircraft sensors to prevent equipment failure by alerting service teams of necessary repairs, preventing unplanned downtime,” the company said in a press release.

PANDA’s alerts and recommendations are expected to number in the tens of thousands annually, saving money across multiple platforms. The system quickly expanded to implementation across more than sixteen aircraft platforms across the nine major Air Force commands, generating over 30,000 predictive maintenance recommendations and alerts for imminent component failure.

The lesson is clear: Integrate AI early in your planning, not as an afterthought.

Building living twins requires more than models; it demands investment in sensor networks, data governance, and data science talent. The more sensors you connect, the better the data and the predictions. Starting early enables you to tune for precision, making investments that make sense for your business.

The sooner that framework is in place, the faster twins can evolve into assets that improve reliability and strengthen readiness.

Apply Digital Twins Across Defense Manufacturing

With the foundation in place, focus on leveraging digital twins throughout your production chain. Digital twins offer transformative opportunities throughout the design, production, and sustainment of complex systems.

​The impact goes way beyond the floor. AI-informed digital twins have applications in defense manufacturing that cover the entire lifecycle of a project, from concept and R&D to full-rate production and customer delivery.

​With AI as the enabler, digital twins are more than design tools. They represent a continuous feedback system that can even impact how your military clients plan and execute their missions.

​Streamline Design and Open New Possibilities

Digital twins stimulate creative thinking from the earliest stages of design. They enable engineers to review and study numerous potential variants in a short period, as their creation is entirely virtual. Consequently, the costs and risks of abandoning options quickly become significantly lower than in a legacy physical environment.

​The technology also enables developers to assess different environments and stresses long before considering materials and assembly, so development cycles are compressed.

​When production begins, digital twins can provide a different set of considerations for manufacturing engineers. AI-enabled twins can be deployed with machine-vision capabilities to monitor throughput, detecting deviations from expected build specifications and tolerances to flag potential failures before reaching customers.

​Information from digital twins can be valuable for developing production schedules by forecasting output and reducing waste, scrap, and rework.

​Know the Applications Beyond Production

The digital twin approach pays dividends even after products teach customers. Manufacturers can deploy AI and predictive analytics to streamline supply chain planning by advising when parts and materials are expected to arrive. It feeds valuable data into the military command process by highlighting potential constraints.

Warfighters can also take advantage of twins in developing sustainment and concepts of operations. Digital twins provide information to monitor asset health, predict failures, and identify optimal maintenance times. The benefit is minimizing unplanned downtime. Commanders can also perform “what-if” simulations to determine how to respond when a part fails or which supply chain path is most resilient. Then, they can close the loop.

Digital twins provide feedback data that can enhance system performance throughout the entire lifecycle. For instance, a team can check which productions hit the mark and which adjustments improved performance and outcomes.

With data, so much is possible, as defense contractor Lockheed Martin, a pioneer in digital twins for industry, attests: “When using the full digital thread and data available, it means that the integration and application of digital twin technology has the potential to have a material impact on cost and schedule—and provide data-driven insights directly to the customer. By opening visibility to a full data ecosystem, we are driving access to information in an open, standard-driven environment for cross-service and cross-platform collaboration. But first, the industry needs to have a shared consensus on how that connection works.”

Connect the Ecosystem Through Integration

Digital twins create new avenues for collaboration. Their real promise isn’t simply operational, such as faster testing or less-expensive production; it’s that they enable contractors to collaborate in ways that were not possible before.

In the physical world, stakeholders rarely see the same data. However, digital twins provide a common operating picture for prime contractors, subcontractors, equipment depots, suppliers, and the military itself.

Sharing data instantly makes it strategic because shared insights lead to synchronized action. By integrating efforts, stakeholders can more easily come together on data formats, shared protocols, and update cycles. In building a shared dashboard, data accelerates decision-making and surfaces problems early, such as supply bottlenecks and production delays. It enables contractors to work on issues in tandem and sharpens strategic decisions.

As defense supply chains move into more fragile environments, connected twins can provide new levels of visibility and scenario modeling to the entire developer ecosystem.

Of course, in a competitive market, sharing data may be easier said than done. Contractors may feel that they are giving up proprietary advantages and face challenges from internal security and compliance rules. This can slow progress across the supply chain, as Accenture notes in a recent study: “Making the most of digital twins involves the entire organization in a deliberate and continuous strategy that aims to set a new performance frontier—Total Enterprise Reinvention. Our research indicates that the defense community recognizes the applications of digital twins. With the right strategies and resourcing, we believe they can make significant strides.”

According to a CapGemini survey, companies can see those strides taking many forms, including “technological advancement (78%), cost savings (71%), reducing time to market (70%), increasing sales (63%), and providing an advanced training environment for employees (68%).”

The lesson is to go into the world of digital twins with an implementation/integration plan. Contractors should determine how they will share and control access to data. You should define common interoperability standards, including metrics and shared definitions, to ensure seamless data exchange and interoperability.

Prepare the Workforce

Even the best technology will stall without the people and processes to sustain it. The next step is ensuring that your workforce is equipped—and confident—to operate in a digital-first environment.

​Leaders should emphasize building awareness and focus on defusing the fear of change. The potential for sudden alterations in workflows and the introduction of new metrics can seem unsettling to even the most confident employees.

​The Chief Digital and Artificial Intelligence Office at the Department of War has introduced initiatives for developing the necessary skill sets in this new era. One is “Digital On Demand,” a portal offering education in data, digital engineering, and AI. The initiative has three priorities: upskilling, strategic recruiting, and identifying and expanding access to talent.

​Engineers, data analysts, manufacturing staff, and maintenance crews all need training relevant to digital engineering, AI literacy, data interpretation, and twin-based decision-making tools. “Identifying opportunities and expanding access for our workforce to develop and thrive is key for optimal manning of advanced systems and tools for critical missions,” the CDAO states in its mission description.

For example, leadership and program management need to understand what digital twins can and can’t tell them, to avoid over-promising beneficial results. One way to achieve this is through small-scale pilots that feature predictive maintenance programs or supply chain risk modeling. These can showcase value and help the workforce address process issues without the pressure of full-rate production.

As employees become more fluent with these new tools, their confidence grows through practice and experimentation. Immersive training—from simulations to digital twin “sandboxes”—helps engineers, analysts, and leaders become comfortable with testing ideas in a virtual space before applying them in the field.

Sustaining that readiness requires more than one-off training. It calls for continuous learning, where teams share lessons from pilots, update models as they evolve, and recognize that digital twins and digital engineering take time and patience to master.

The outcome isn’t just technical competence; it’s an adaptable workforce that sees digital transformation as an opportunity, not upheaval.

Defense manufacturers are moving cautiously and can learn from the Department of War. In fact, continuous and sustainable learning is a crucial aspect of the Department’s strategy for adapting its own workforce. For instance, the Defense Acquisition University requires acquisition professionals to complete eighty hours of continuous learning every two years. This includes formal training, along with self-study, teaching, and personal experiences. The Department believes that supervisors should share responsibility for “ensuring that individuals are provided duty time for planned CL activities while within organizational workload and funding constraints.”

Once your teams are trained and engaged, the next challenge is maintaining momentum through a structured approach. That’s where governance and process come in. Oversight is crucial for keeping digital twin programs aligned with mission goals and ensuring that data remains reliable, secure, and useful throughout its lifecycle.

Chart a Scalable Roadmap for Digital Manufacturing

Get started with digital twins by building on what you already do well, but ensure that those efforts are guided by strong governance. Clear oversight defines who owns what: Data governance, model fidelity, cybersecurity, and continuous improvement all need champions. Establishing these roles early will prevent confusion and speed adoption as projects scale.

Governance also extends to acquisition and contracting. Models must include requirements for data access, updates, and integration, while suppliers should be vetted for their ability to support digital twin-related deliverables. Including these expectations early helps build continuity in the process.

Once oversight is in place, start by gauging digital maturity across engineering, data, and other areas. Without this baseline, leaders risk setting goals that outpace their people and processes, ultimately leading to frustration and inefficiency.

For now, it’s enough to focus on small wins. Find a project that’s manageable, affordable, and visible within the organization. The idea is to build a proof point that the workforce can get excited about, such as supply chain risk modeling for a critical part. Keep stakes low, though; treat it as an experiment, and focus on quickly getting feedback on what worked and what didn’t.

Once validated, apply the approach more broadly across similar platforms, multiple depots, or supply chain tiers. For example, Tyndall Air Force Base’s “Installation of the Future” initiative used a digital twin of the base infrastructure. Lessons learned from that pilot informed how the Air Force approached other base redesigns.

Embedding digital twins into acquisition strategies and budgeting cycles ensures that they have a future beyond the new and the novel and that you can compete for longer-term resources.

The bottom line is that success comes from phased, measurable progress—not by trying to do everything at once.

Team With Sumaria Systems on Digital Twins

Digital twins and AI are not future promises. They are currently in production across aircraft maintenance, shipboard monitoring, base infrastructure, and supply chain planning.

Implementation is a leadership choice. Start with a clear vision, prove value through pilots, scale deliberately, and build governance that lasts.

The payoff is faster modernization, stronger resilience, and decisive strategic advantage. With extensive experience in IT, networking, and digital infrastructures, Sumaria Systems is ready to serve as your partner in bringing digital twins and AI to the manufacturing process.

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.