Unlocking Innovation Through Digital Engineering

The Challenge

Most of us learned to approach engineering design, development, test, and evaluation (DDT&E) of complex systems with the classic systems engineering “V” cycle that framed requirements, design, test, verification, and deployment in a serial, gated way. These processes were very effective for long acquisition and systems development programs when the strategic, fiscal, and market environment was more stable. Today, sectors like space, energy, and climate face urgent societal needs, evolving public-private sector roles, and increased competition. Responding to these imperatives means working with a portfolio of smaller technology demos, rapid prototyping, and achieving operational capabilities more quickly. Sticking to the legacy of serial, document‑centric workflows results in decision latency, pushing costly fixes deep into development cycles, and increasing overall risk to successful outcomes.

Moving technologies to market faster does not require us to sacrifice technical rigor. It calls for an update of systems engineering practices to modernize engineering DDT&E. Digital engineering, anchored by the Digital Thread and integration of emerging advanced tools, offers that pathway.

From MBSE to the Digital Thread

Model Based Systems Engineering (MBSE) began the shift away from document-based approaches toward model‑centric development, improving requirements traceability through design, test, and verification. The Digital Thread (DT) extends that idea: it forms a continuous, data‑driven backbone that links requirements, design, analysis, test, and verification over the full lifecycle.

At the core of the DT is the Authoritative Source of Truth (ASOT), a living technical baseline that captures the current state and history of the system, including physics‑based models that describe design behavior in operational environments. When teams work from a shared ASOT, they reduce uncertainty, accelerate decisions, and resolve technical issues quickly.

Modernizing Engineering DDT&E

With the Digital Thread as the backbone, we can incorporate more advanced tools around the ASOT as they become mature:

• Generative artificial intelligence (Gen‑AI) for rapid requirements drafting, design trade studies, and exploring technical solutions.

• Augmented reality (AR) for interface definitions, test procedures, integration workflows, and quality assurance.

• Advanced manufacturing (AM) for rapid prototyping and earlier physical validation.

• Digital twin simulations to evaluate behavior across development and verification phases.

• Agile development processes for software, design-analysis (DAC) cycles, and iterative system refinement.

When these tools integrate through consistent data models, teams can explore larger trade spaces, iterate quickly, and manage risk more effectively.

Operational Benefits

Adopting a DT‑based approach with advanced toolsets is already producing measurable operational advantages and these benefits will accelerate with increased adoption:

• Expanding the trade space and finding higher‑value solutions faster, potentially optimizing for mass, volume, power, and other technical performance metrics.

• Accelerating cycle times by digitizing workflows and streamlining reviews; reducing the time spent on slide decks, document preparation, and extensive review cycles.

• Reducing errors and non‑conformances through consistent, transparent data and a single ASOT as well as investigating solutions more quickly to technical anomalies found during integration and test.

• Streamlining physical testing from component to subsystem to system level and enabling more efficient hardware‑in‑the‑loop (HWIL) and software‑in‑the‑loop (SIL) testing for software.

• Tracking risks and mitigations comprehensively, turning lessons learned into reusable institutional knowledge.

  
  

When I was leading engineering at a NASA research center, we were faced with a challenge to pivot towards more focused technology demonstrations, commercial partnerships, and hosted payloads. Early demonstrations of requirements tailoring, agile practices, and digital models showed how both time and cost could be reduced compared to legacy DDT&E practices.

Going Further: The Potential of AI

The acceleration of AI tools will surely deepen the DT’s value even further:

• Gen‑AI can speed concept development and trade‑space exploration, enabling teams to surface promising options in hours instead of weeks.

• Automated code generation and simulation can reduce software integration time and speed iterations in SIL and HWIL environments.

• Domain‑specific models and Retrieval‑augmented generation (RAG) can protect proprietary knowledge while enabling targeted, high‑confidence responses.

• Edge AI and domain‑aware models can help operationalize digitized baselines in constrained or secure environments.

• Advanced techniques such as deep reinforcement learning (DRL) offer new ways to develop autonomous control systems through simulated adaptation and learning.

Addressing Real Barriers

Adopting DT and digital engineering at scale raises four persistent challenges.

1. Tool‑stack fragmentation

   • Problem: Multiple excellent tools exist for things like requirements management, structural design, environments analysis, and test procedures, but integration across heterogeneous data streams is still evolving.

   • Mitigation: Prioritize open data standards, define canonical data models for the ASOT, and pilot integration layers that translate between tools before committing to an enterprise‑wide system.

2. Process and governance change

   • Problem: Organizations still default to milestone gates and long review cycles that are hard to reconcile with iterative models.

   • Mitigation: Redesign milestones around executable demonstrations and measurable outcomes; require artifact traceability into the ASOT and shift reviews to shorter, model‑based check points.

3. IT infrastructure and security

   • Problem: Secure, manageable IT environments for sensitive models and IP remain a barrier.

   • Mitigation: Invest in segregated secure enclaves and adopt RAG-style controls that separate indexing from model inference; treat DT infrastructure as a strategic asset and fund it accordingly.

4. Cultural resistance and change management

   • Problem: Many DE (digital engineering) efforts falter from non‑technical causes: resistance to change, lack of leadership buy‑in, or insufficient incentives for adoption.

   • Mitigation: Identify early adopters, fund visible pilot projects that demonstrate rapid value, and align incentives—technical and managerial—to reward working in the DT and contributing to the ASOT.

Practical Steps

1. Use focused pilots that target a measurable outcome (development time, life cycle cost, or performance improvements) using a fully digital approach for quick wins.

2. Define the minimal canonical set of data models and interfaces before integrating multiple tools.

3. Pair technical pilots with a change‑management plan: leadership sponsorship, training, and a communication plan that highlights wins.

4. Treat security and IP protection as design constraints—explore the use RAG and domain‑specific model controls when necessary.

5. Capture lessons learned as structured artifacts in the ASOT so knowledge compounds over time.

   
   

Pilots and early demonstrations across government and industry already exist; the challenge is moving from isolated wins to institutional adoption. That requires disciplined governance, a clear statement of expected value, and active leadership to translate pilots into scaled capability.

Conclusion

Digital engineering, centered on a robust Digital Thread and an Authoritative Source of Truth, should be regarded as a strategic enabler for delivering optimized and potentially faster solutions to urgent public‑good challenges in space, energy, and climate. The technology stack, AI tools, and market drivers are converging to make broad adoption practical. The harder problems are process, security, and culture—but each is solvable with focused pilots, executive sponsorship, and disciplined data governance.

For organizations committed to impact and speed, the choice is clear: evolve engineering practice now to stay relevant and deliver more value, faster.