Digital Twin Adoption in Defense Programs: What Systems Engineers Need to Know

The U.S. Department of Defense has been building toward a formal digital engineering posture since the 2018 Digital Engineering Strategy. That document named five goals, the first being to formalize the development and use of models as the authoritative source of information. For systems engineers in defense acquisition, “authoritative source” is the operative phrase. It means the model isn’t a reference artifact that mirrors a document. The document, if it exists at all, mirrors the model.

In 2025 and 2026, that mandate has moved from guidance to contract requirement language on a growing number of programs. Major system development contracts — across platforms in air, sea, land, and space domains — now include digital engineering deliverables as formal CDRLs. The vehicle for delivering on those commitments, in most programs, is a digital twin.

This article covers what that actually means for systems engineers doing the work: what a digital twin demands from the underlying data model, how requirements traceability keeps a twin synchronized with what’s actually been built, and what it takes organizationally to stand up a program that can sustain it.

What “Digital Twin” Actually Means in a Defense Context

The term is used loosely enough that it’s worth establishing a working definition before analyzing anything.

In commercial manufacturing and aerospace, a digital twin typically refers to a high-fidelity simulation model of a physical asset that receives real-time or near-real-time data from the physical counterpart and can predict behavior, detect anomalies, or support maintenance decisions. GE’s turbine monitoring programs and NASA’s structural health monitoring work are canonical examples.

In defense acquisition, the definition is broader. The DoD Digital Engineering Strategy describes digital twins as representations of physical systems in digital form — but these representations span the full lifecycle, from early concept through sustainment. A defense digital twin encompasses the system’s requirements, architecture, design geometry, simulation models, test results, manufacturing data, and configuration status as a connected whole. The physical asset and its digital representation are meant to stay synchronized throughout the system’s operational life.

That scope changes what the technical infrastructure has to support. You are not building a simulation with telemetry feeds. You are building a connected model that must absorb and reflect the consequences of every engineering change decision across every domain for the life of the program.

What a Digital Twin Demands from the Underlying Data Model

The first structural demand is relational integrity across domains.

A defense digital twin connects data from systems engineering (requirements, functional architecture, physical architecture), design (CAD, electrical schematics, software architecture), analysis (structural, thermal, signal), manufacturing (BOM, process plans, tooling), test (procedures, results, discrepancy reports), and configuration management (baselines, waivers, deviations). Each of those domains has its own data schema, toolchain, and ownership structure.

The underlying data model has to express relationships across all of them without forcing all of them into a single schema. This is where graph-based models have a structural advantage over document-based approaches. In a graph model, a requirement, a structural element, a test case, and a manufacturing record can all exist as typed nodes with named, directional relationships between them. You can traverse the graph to answer questions like: “Which structural components are derived from this safety-critical requirement, and what is their verification status?” In a document model, answering that question requires a human to manually cross-reference a specification, a drawing tree, a test matrix, and a configuration baseline. At program scale, that manual work doesn’t scale — it collapses into periodic audits that are always behind.

The second demand is change propagation visibility. Every engineering change has a ripple. A requirements change affects downstream architecture allocations, which affect design parameters, which affect test criteria, which affect manufacturing processes. A digital twin must make those ripples traceable. That means the data model needs to encode not just current state but the dependency graph that reveals where a change propagates.

The third demand is configuration alignment. The digital twin must represent what was actually built, not what was designed. As-designed and as-built configurations diverge from the first deviation or waiver. A data model that can’t record and reflect those divergences — and propagate their implications — produces a twin that’s increasingly fictional over time.

Requirements Traceability as a Synchronization Mechanism

Requirements traceability in the context of a digital twin is not a compliance exercise. It is the primary mechanism by which the twin stays synchronized with both the design intent and the as-built reality.

Consider what happens without it. A requirement is levied on a subsystem. The subsystem design is developed, verified, and delivered. A configuration change is later approved — perhaps a material substitution driven by a supply chain constraint. The change is reflected in the manufacturing record. It is not reflected in the simulation model. The digital twin now represents a system that doesn’t exist. Any analysis run against it produces outputs that are valid for the original design and wrong for the actual hardware.

Traceability prevents this by making the change visible across its downstream impact. When the configuration change is logged, a correctly implemented traceability model immediately shows which requirements are affected, which test cases verified those requirements, which simulation assumptions were based on the original design parameter, and which analyses need to be rerun or re-validated. The twin stays current not because someone does a periodic audit but because the system itself signals where updates are required.

This is why the requirements management platform is not a peripheral layer in a digital twin program. It is load-bearing infrastructure. The traceability graph it maintains is the connective tissue between the twin’s logical structure and its physical manifestation.

The specific capability that separates platforms that work here from platforms that don’t is bidirectional traceability with change impact propagation. Platforms built on document paradigms — where traceability is a spreadsheet or a linkage annotation in a document — can capture point-in-time relationships but cannot dynamically propagate change signals through the dependency graph. Platforms built on connected graph models can. That architectural difference determines whether the requirements layer actively contributes to twin synchronization or passively records it after the fact.

The Organizational Challenge: Standing Up a Digital Twin Program Office

Program managers who have been through this describe the organizational challenge as more difficult than the technical one. That framing is accurate, and it’s worth unpacking why.

Ownership of the model is genuinely contested. In a traditional program, data ownership maps naturally to discipline leads: the systems engineer owns the requirements baseline, the lead design engineer owns the CAD model, the test director owns the test matrix. In a digital twin program, the model integrates all of these. Every discipline lead has a claim on the shared model, and every change that one discipline makes can affect the validity of another discipline’s data. Authority structures that worked for siloed documentation don’t work for a connected model. Programs that haven’t resolved the model stewardship question before they start building the twin spend enormous energy on governance disputes instead of engineering work.

Tool chain integration is underestimated as a program activity. Most defense programs deploy best-of-breed tools in each domain: a dedicated requirements management platform, a CAD system, a simulation environment, an ERP system for manufacturing, and a PDM or PLM system for configuration management. Making those tools exchange data in a way that maintains the graph structure of the digital twin — rather than just exporting flat files at milestones — is a program engineering activity that requires dedicated personnel, defined interfaces, and ongoing maintenance. Programs that treat integration as a one-time IT task consistently find that the integration breaks when any component tool is updated or replaced.

Change control discipline at the model level is different from change control at the document level. Traditional engineering change boards review proposed changes to documents and drawings. A digital twin program needs change control that operates at the model level — reviewing changes to nodes and relationships in the authoritative model, not just their document representations. This requires change board members who can read and interpret model artifacts, which in turn requires training and a different kind of technical preparation than most programs have invested in.

Workforce development is a long lead-time item. The systems engineers, configuration managers, and program analysts who can work fluently in a model-based digital thread are still relatively scarce in the defense industrial base. Programs that underestimate the time required to develop or recruit this workforce will stand up a digital twin infrastructure that nobody can operate effectively.

How Modern Requirements Management Platforms Support the Digital Thread

The digital thread — the connected data backbone that the digital twin depends on — runs through requirements management at its logical center. Every design decision, analysis result, and test outcome traces back to requirements. Every configuration change either satisfies, challenges, or modifies requirements. The requirements layer is where the thread’s logical structure is defined.

This is where platforms like Flow Engineering have built a material advantage for systems engineering teams on defense programs. Flow Engineering’s approach is graph-native: requirements, interfaces, functions, and design elements exist as connected nodes with typed relationships, not as rows in a document database with linkage fields. That architecture means the change impact propagation that synchronization-aware digital twin programs need is inherent to the data model, not bolted on through custom configuration.

For a program standing up a digital twin capability, the practical implication is that the requirements management tool selection is a structural decision, not just a software procurement. A platform that stores requirements as structured documents with linkage annotations will constrain the digital thread at its logical center. A platform that stores requirements as nodes in a connected graph will allow the thread to function as intended — with dynamic propagation of change signals across the full dependency structure.

Flow Engineering’s deliberate focus on hardware and systems engineering programs, rather than serving general software and enterprise use cases, also means its data model is already typed to the entities defense programs work with: system requirements, interface definitions, verification methods, allocation relationships, and configuration variants. Programs don’t have to configure a general-purpose tool into the right shape before they can start modeling.

Honest Assessment: Where the DoD Digital Twin Mandate Stands

The mandate is real. The contract language is appearing. The pressure on prime contractors and major subs to demonstrate digital engineering capability at milestones is increasing, not decreasing.

The execution gap is also real. Most programs that have a “digital twin” deliverable in their contract today have a collection of models and databases with some integration tooling and a significant amount of manual synchronization work happening beneath the surface. Very few programs have achieved the continuous, bidirectional, change-propagating digital thread that the strategy envisions.

The gap closes when programs treat the requirements management layer as a technical architecture decision rather than a tool procurement, resolve model ownership governance before they start building, and staff the integration and model stewardship function as a persistent program activity rather than a project.

Programs that do those things will build digital twins that stay accurate across the lifecycle. Programs that don’t will build expensive documentation systems that nobody trusts after the first major configuration change.

The systems engineers reading this article will know which situation they’re in. The question is whether they have enough organizational leverage to change it.