American Rheinmetall is pushing a firms-first rethink of how combat vehicles are designed, validated, and sustained; the company’s new digital-first process promises faster iteration, clearer customer oversight, and lower lifetime costs for vehicle fleets. Where traditional vehicle programs have followed linear development paths that move from design to prototype to production in discrete stages, Rheinmetall has threaded requirements, engineering, manufacture, and sustainment into a unified digital model. That single thread drives decisions from concept through depot-level maintenance, and it changes how program managers, integrators, and end users interact with a vehicle throughout its life.
Ruben Burgos, American Rheinmetall’s program director, captures the idea succinctly: the digital model “starts with the requirement, and it goes through the architecture of the vehicle, and it touches all areas, including how the vehicle is actually built.” The model is not a static drawing or a collection of documents; it is an executable representation of the vehicle and its interfaces. That representation is divided into three digital twins: the engineering twin models mobility, survivability, lethality, and subsystem interactions; the manufacturing twin translates those engineered models into producible assemblies; the sustainment twin extends the model to long-term maintenance, part replacement, and upgrade paths. Together these twins form a continuous feedback loop that narrows the gap between design intent and fielded reality.
One practical payoff of the digital thread is speed. Feedback cycles that once took months now can occur in days because customers and integrators work against the same living model. Customers can run their own assessments, test interface compatibility, and propose changes directly against the digital representation; that transparency reduces downstream surprises and shortens the path from request to fielding. Burgos described the relationship with customers as collaborative rather than adversarial; when both parties see the same model, disagreements about fit, function, or schedule become engineering problems rather than procurement disputes.
Modularity lies at the center of Rheinmetall’s approach. The company has applied a Modular Open Systems Approach framework to define how subsystems connect, how data flows across interfaces, and how physical and electrical interfaces are standardized. The platform is built with SWAP-C growth margins already accounted for; architects have reserved power headroom, compute capacity, and physical space so that new sensors, turrets, or mission modules can be integrated without a wholesale redesign. That forward planning matters in two ways: it reduces the cost and schedule impact of future upgrades, and it enables operators to adopt new capabilities on shorter timelines. Where swapping a sensor or turret previously might derail a program for months or years, Rheinmetall’s model intends to make comparable changes a matter of weeks.
The manufacturing twin translates digital engineering into shop-floor reality. Rheinmetall has taken production steps in partnership with established U.S. facilities; vehicles will be produced at Textron Systems in Slidell, Louisiana, and at Rheinmetall sites in Michigan, Ohio, and Maine. That distributed production footprint is consistent with the digital-first mandate: factory equipment, assembly jigs, test benches, and logistics flows are simulated and validated in the model before a physical line is committed. That reduces rework and helps ensure first articles conform to their digital baselines.
Interoperability with autonomy and advanced software has been designed in from the outset. The platform supports semi-autonomous operation at initial fielding and provides pathways to full autonomy as technology and doctrine evolve. True modularity, Burgos argues, requires defining not only mechanical mounts or power taps but software contracts and data schemas; the digital representation specifies those contracts so that when a new sensor or AI module arrives the integrator knows how it will behave, how it will be powered, and how it will affect vehicle thermal and power budgets.
The business case for a digital-first approach is straightforward. By making models the canonical source of truth, Rheinmetall reduces surprises in integration, lowers upgrade costs, and offers customers a predictable upgrade cadence. When a customer requests a change they do not have to rely solely on the original equipment manufacturer to rediscover how systems interface; they can inspect the model, simulate outcomes, and proceed with confidence. That transparency can also accelerate approval cycles and shorten procurement timelines.
Engineering rigor and digital traceability do not remove risk, and Rheinmetall’s method demands disciplined processes to manage those risks. A living model that flows into production and sustainment requires robust configuration control; model branches, versioning, and baselining must be tightly governed so fielded hardware and depot practices align to a single authoritative source. Cybersecurity and data integrity become program-level concerns because the digital twin and its underlying toolchains represent intellectual property and operationally relevant technical data. Factory automation that depends on model inputs must be defended against tampering, and software contracts that allow third-party modules must be enforced with authentication and validation mechanisms.
Adoption challenges are also organizational. Program offices and customers must adapt acquisition frameworks to accept model-based deliverables as equivalent to physical prototypes or paper drawings. Supply chain partners will need access to secure model subsets so they can design subcomponents to precise interfaces. That implies both technological investment and contractual clarity about model access, change management, and export controls.
American Rheinmetall’s early program milestone with a customer, where an independent review deemed the design mature and ready for manufacture, provides a practical data point. Passing that gate required traceability from top-level requirements down to the physical build; reviewers could follow the lineage of requirements into the engineering twin and then into the manufacturing twin to see how design decisions propagated into production. That maturity at review signals that the digital thread is more than marketing; it is a working practice that can satisfy rigorous program oversight.
If the method takes hold more broadly, it could reshape how modern military platforms evolve. Digital twins and MOSA together alter the economics of upgrades, permit more rapid insertion of new capabilities, and change the role of the OEM from gatekeeper to collaborator. For customers constrained by budgets and timelines, that shift matters because it aligns modernization with operational needs rather than with the cadence of traditional defense contracting.
Rheinmetall’s approach is not a silver bullet, but it reframes complexity as an engineering problem that can be managed with data, interfaces, and governance. When requirements, engineering, and sustainment coexist in a digital model, program managers gain the ability to iterate faster, to plan for growth, and to keep fleets current without repeated heavy industrial lifts. The company is betting that buyers will value that predictability and that the U.S. industrial base can scale to meet a future where upgrades are routine and rapid. If they are right, the next generation of combat vehicles will be as much defined by their data models as by their armor and guns.
