The A&D Manufacturer's Competitive Edge: Digital Engineering & the Digital Thread

Regulatory compliance standards have shifted dramatically since 2024. As a result, Aerospace and Defense (A&D) manufacturing sits at a critical inflection point.

The Department of Defense (DoD) now mandates digital engineering (DE) as a primary component of agency acquisition strategies through DoDI 5000.97, a directive that fundamentally changes how government contractors approach development of complex systems.

Concurrently, market pressures continue to intensify. Your agency customers demand delivery cycles at speed, on-demand customization, enhanced sustainability practices, and mission readiness at an unwavering pace. Traditional document-centric approaches to engineering simply can't deliver.

This article will cover the benefits of DE and those of its inseparable companion, the Digital Thread. We'll show you how they interconnect to drive real innovation, strengthen regulatory compliance, and position your firm for competitive advantage in modern manufacturing.

Table of Contents

• What Is Digital Engineering?
• The Business Case for Digital Engineering
• Take A&D Engineering Beyond Documents
• How Digital Engineering and the Digital Thread Work Together
• How the Digital Thread Connects the Product Lifecycle
• KPIs That Matter in Measuring Digital Engineering Success
• Digital Engineering Use Cases Across the Manufacturing Lifecycle
• Build Your Digital Engineering Implementation Roadmap
• Address Digital Engineering Implementation Challenges
• Advanced Digital Engineering Capabilities on the Near-Term Horizon

Key Takeaways

Digital engineering replaces document-driven workflows with connected digital models that improve speed, quality, and compliance across the manufacturing lifecycle.

Model-based systems engineering, simulation, and the digital thread work together to reduce risk early and prevent costly rework later.

A connected digital thread delivers end-to-end traceability, which supports audit readiness and change management as part of daily operations.

Digital engineering is not a one-time initiative. It is an operating model that strengthens competitiveness as regulatory and customer demands continue to rise.

What is Digital Engineering?

Digital engineering is a method of designing, building, and managing products using shared digital models and data, rather than relying on disconnected documents. It connects people, processes, and systems across the entire manufacturing lifecycle, enabling teams to make faster decisions, reduce risk, and adapt to change more easily.

The Business Case for Digital Engineering

Digital engineering isn't just a technical upgrade; it's a business advantage. By replacing fragmented, document-heavy workflows with connected digital models, A&D manufacturers gain real-time visibility into cost, schedule, quality, and risk. That transparency helps leaders make better decisions earlier, when changes are less expensive, and programs are more likely to stay on track.

The payoff is measurable. Digital engineering shortens development cycles, improves first-time quality, and reduces rework and compliance risk. It enables faster response to changing requirements, supports audit-ready traceability, and strengthens collaboration across engineering, manufacturing, supply chain, and program management.

In an environment defined by tighter margins, stricter oversight, and faster acquisition timelines, digital engineering enables manufacturers to shift from reactive execution to proactive control, transforming compliance, speed, and innovation into a sustainable competitive edge.

Take A&D Engineering Beyond Documents

At its core, digital engineering is a fundamental shift in how A&D manufacturers create, share, and act on technical information across design and production. Instead of locking system requirements, design specifications, and performance data inside static documents, DE consolidates them into connected digital models that serve as a single, authoritative source of truth throughout manufacturing. The impact is immediate and material.

Model-Based Systems Engineering

DE comprises several interlocking components that work in concert. Model-Based Systems Engineering (MBSE) is the methodological foundation. This formalized approach uses digital models to define, design, and analyze complex systems throughout the manufacturing lifecycle. Rather than documenting system architecture in prose, MBSE captures it in structured data models that maintain explicit relationships between every component, requirement, and design decision. By making those relationships visible and traceable from the start, MBSE enables earlier, evidence-based decisions.

Simulation and Digital Twins

Simulation and digital twins further extend this capability. Simulation enables your engineering teams to predict how design changes will impact your system before you ever build a physical prototype. Digital twins create virtual representations of your physical products and processes, enabling continuous monitoring and predictive analytics throughout the product lifecycle. This cuts down physical prototyping costs, reduces program risk, and validates performance earlier.

Product Lifecycle Management

Additionally, Product Lifecycle Management (PLM) integration ensures that these digital models remain central to every decision from initial product concept through retirement. By eliminating version conflicts and disconnected workflows, you enable faster builds and audit-ready traceability across the lifecycle.

The benefits compound across your entire operation. Development timelines compress because teams no longer waste effort reconciling conflicting information or hunting through archives for a correct document version. Manufacturing costs decline as you reduce rework and scrap driven by misunderstandings. Quality improves dramatically because MBSE's inherent verification capabilities catch design flaws early when they're least expensive to fix.

How Digital Engineering and the Digital Thread Work Together

Digital engineering defines how work gets done using connected models and data, while the digital thread ensures that information flows continuously across every stage of the product lifecycle. DE creates the authoritative digital models; the digital thread links these models to downstream processes, such as manufacturing, quality, supply chain, sustainment, and compliance. Together, they turn isolated engineering activities into a closed-loop system where decisions made in design carry forward and insights from production and the field flow back. This creates full lifecycle visibility, faster execution, and traceable, audit-ready outcomes.

A Digital Thread Connects Product Lifecycle Stages

While digital engineering enables the methodology and tools behind this new manufacturing approach, the digital thread provides the connective tissue that ties it all together. Think of the digital thread as an invisible line running through every stage of a product's lifecycle: design, manufacturing, field operation, sustainment, and eventual retirement.

How the Digital Thread Differs from a Digital Twin

How does the digital thread fundamentally differ from a digital twin? Although the two concepts are often used interchangeably in modern discussions, a digital twin represents your product or process at a specific moment in time. The digital thread, by contrast, represents the continuous journey a product takes through its lifecycle. It's the narrative written in data, connecting design intent to manufacturing execution, field performance, and compliance documentation.

Lifecycle Connectivity & Traceability

Lifecycle connectivity defines the essence of a thread. Let's examine a complex aerospace system as it transitions from design to manufacturing. Every change made during detailed design is automatically reflected in manufacturing work instructions. When the shop floor encounters an issue requiring design modification, that change ripples backward to update design documentation and forward to update supplier specifications. When the product enters service, field performance data feeds back into the sustainment organization, creating a closed loop that informs future design iterations.

This connectivity is essential for traceability. You don't need to create records to prepare for an audit; they're automatically created as part of normal operations.

The Digital Engineering and Digital Thread Ecosystem

DE and the digital thread don't exist in isolation. They form an integrated ecosystem of tools, standards, and practices. This ecosystem typically includes specialized software for different functions: PLM systems managing design data, CAD tools enabling detailed design work, Manufacturing Execution Systems (MES) controlling shop-floor operations, and Enterprise Resource Planning (ERP) systems, which orchestrate financial and operational data.

The magic happens when these systems talk to each other. When your PLM system feeds design data directly into your MES, your shop floor always operates from the latest specifications. When your MES captures production data that flows directly into your ERP, your financial team has accurate actuals in real time rather than estimates. When your quality management system (QMS), connects to your PLM, every inspection finding contributes to engineering decisions rather than languishing in a separate database. 

Standards are the foundation for this integration. International standards, such as ISO 10303, enable the structured exchange of data among different tools, and DoD standards ensure that classified and controlled information maintains appropriate security while remaining accessible to authorized teams.

Emerging technologies amplify the ecosystem's capabilities. Artificial intelligence (AI) and machine learning (ML) enable predictive quality management—identifying patterns in inspection data that suggest an impending process drift before defects actually appear. Internet of Things (IoT) devices on the manufacturing floor signal real-time visibility into equipment use and conditions. Finally, cloud computing enables secure collaboration across multiple facilities while maintaining the infrastructure resilience that defense work requires. 

KPIs That Matter in Measuring DE Success

Abstract benefits like "improved collaboration" matter less than concrete improvements that appear in your financial results, such as shortened time-to-value in your customer's supply chain or a boost in your competitive standing, such as reaching the top rung of an agency's preferred vendor roster.

Compliance Readiness

This should be your leading indicator. Track the percentage of your compliance documentation that's automatically generated from digital models versus manually assembled from various "hard" sources. Track the time required to prepare for audits. Track the number of audit findings. As DE implementation matures, you should see dramatic reductions in documentation effort. Too, audit findings should concentrate on implementation details rather than flagging missing information or inconsistent records.

Cycle Time Reduction

Cycle time reduction is the DE benefit that's most visible. How do you measure it? Track design phase duration, first article inspection cycle time, and manufacturing lead time for new products, as well as time efficiencies from customer specification to first delivery. DE typically compresses each of these significantly. When you eliminate document synchronization delays and rework driven by specification misunderstandings, cycle time improvements naturally follow. Faster delivery enables you to pursue more aggressive bid strategies and win contracts where competitors might be resting on their laurels and incorrectly assuming awards will be theirs.

Cost Savings

Economic efficiencies can accumulate across multiple areas: You'll automatically lower scrap and iteration costs as quality improves. Inspection labor can be reduced as a byproduct of automation and digital work instructions. Warranty and field service costs will usually be lower as sustainment becomes predictive rather than reactive. Reduced proposal preparation time can result from reusing model components and implementing quality improvements of similar magnitude.

DE Use Cases Across the Manufacturing Lifecycle

DE shows up differently across various phases of your product lifecycle, each trumpeting distinct tactical approaches and benefits.

Design Phase

During the design phase, MBSE fundamentally changes how your engineering teams approach complexity. Instead of beginning with CAD models, teams start with formal specifications captured in modeling languages. These specifications drive simulation and analysis activities that would previously have required multiple prototype iterations. Your teams can explore design alternatives, validate performance requirements, and stress-test designs against edge cases, all in the virtual realm, before committing to expensive physical prototypes. For complex defense systems in which the cost of design iteration can stretch into millions of dollars, this capability alone justifies DE investment.

Manufacturing Phase

As your product moves into manufacturing, the digital thread triggers what we might call "digital-first" operations. Your shop floor teams access electronic work instructions linked directly to design specifications, complete with embedded images, embedded quality checkpoints, and automatic change notifications that appear when engineering makes modifications. First Article Inspection (the process of verifying that your first production unit meets all requirements) becomes much more manageable when all relevant documentation, specifications, and design data are kept synchronized in a connected system. Rather than manually assembling inspection documentation from various sources, your quality team references a single integrated record that captures design intent, manufacturing process specifications, and inspection criteria all in one place.

Supply Chain Phase

Throughout your supply chain, the digital thread creates unprecedented visibility and risk mitigation. You know which suppliers are manufacturing which components, exactly when those components will arrive, whether they've passed all required inspections, and what their complete genealogy includes. When a component issue emerges, you can immediately identify all affected units in production, in inventory, and potentially in the field. Visibility at this level empowers you to take corrective action with surgical precision. Say goodbye to expensive blanket recalls and widespread rework.

Sustainment Phase

During sustainment and field operations, digital twins collected over years of operational data facilitate predictive maintenance. No need to follow manufacturer recommendations for maintenance intervals; you maintain equipment based on actual condition data. Identify which components exhibit stress signatures that predict imminent failure. Schedule maintenance during planned downtime and avoid unexpected failures during critical missions. For defense applications where mission interruption can have serious consequences, this capability translates directly into operational superiority.

Build Your DE Implementation Roadmap

It's not an overnight transformation. Nor is it a simple point solution. Implementing DE to benefit your contracting firm requires a phased, deliberate approach. Build capabilities progressively. Manage cultural change concurrently. Address system integration challenges as they happen.

1. Start with an honest assessment of your gaps. In terms of digital maturity, where does your organization stand in relation to the DoDI 5000.97 requirements? Which regulatory mandates are you struggling to meet? Which operational pain points consume disproportionate resources? Which teams show the greatest resistance to change? Candid answers to these questions can determine your roadmap priorities. If you have significant investments in legacy systems, you may prioritize data integration and migration strategies. If you have newer infrastructure, you might focus on implementing MBSE practices and model-centric workflows. 
2. Construct the secure digital ecosystem that DE requires. Given the classified information typical in A&D work, it's crucial to establish an infrastructure that maintains data security while enabling seamless access for authorized teams across multiple facilities. Cloud platforms designed specifically for government contractors enable this capability, combining government-grade security with the flexibility modern collaboration requires.
3. Progressively integrate MBSE and the digital thread across your programs. Start with a pilot program where your engineering and manufacturing teams commit to working model-centric rather than document-centric. Because these systems are now built to run intuitively, learning occurs rapidly. And this early program becomes your proof point for enterprise rollout. As confidence builds, expand MBSE adoption across additional programs and eventually make model-centric practices your standard operating procedure rather than the exception. 
4. Pay attention to workforce transformation. Your teams need training in MBSE principles and practices. Your leadership needs to understand how model-centric decision-making changes traditional workflows. Your quality professionals need to grasp how digital traceability enables compliance verification. Your IT organization needs expertise in the specialized infrastructure required to secure DE ecosystems. Investing in training pays dividends throughout implementation and beyond; organizations with confident teams implement DE far more successfully than those that treat training as just a box to check. 

Address DE Implementation Challenges Head-On

Any kind of digital transformation invariably encounters friction. Acknowledging the real challenges you'll face with DE and planning responses in advance significantly improves your likelihood of success.

The most visible challenges?

Legacy Systems and Data Silos

Your firm likely runs multiple systems acquired independently over the years. Some examples: MRP systems, quality databases, accounting systems, labor tracking, and compliance documentation. Systems like these were initially designed to optimize their narrow domain rather than to share data with the broader organization. When you impose a requirement for seamless integration, you immediately encounter friction. Phased migration strategies prove essential, but you don't rip and replace all legacy systems simultaneously. Instead, you establish integration points progressively, starting with the areas that offer the most significant benefit and building from there. Modern APIs and middleware allow you to connect disparate systems without rebuilding everything from scratch.

Cultural Resistance

People typically prove more challenging than technical obstacles. Your experienced engineers learned to practice systems engineering a certain way. Your manufacturing leaders built their expertise around processes developed over decades. Your quality professionals know how to work within familiar frameworks. Asking them to fundamentally change their approach triggers natural resistance. Robust change management addresses this directly. But how do you implement? Your most respected technical leaders need to become visible advocates for new approaches. Celebrate early successes prominently. Provide support rather than simply dictate that things must change.

Cybersecurity and Intellectual Property Protection

These can't be afterthoughts. In some cases, your digital models represent your most valuable proprietary knowledge. Your DE ecosystem must meet DoD standards for handling classified information, while your commercial platforms have to protect trade secrets. This dual requirement sometimes feels contradictory- maximum security and maximum confidentiality can seem mutually exclusive. However, well-designed systems achieve both through appropriate segmentation, role-based access control, and sophisticated auditing that proves who accessed what information when.

The Manufacturer’s Quick-Start Plan

• Start with one pilot program.
• Modernize data flow before tools.
• Build security and compliance in from day one.

Common Misconceptions About DE

• DE is not a software purchase.
• DE is not just for primes.
• DE doesn’t require replacing every legacy system.

Advanced DE Capabilities on the Near-Future Horizon

There's no going back. AI-driven solutions will increasingly automate routine decisions in A&D product design and engineering. Instead of manually creating dozens of design alternatives and analyzing each, AI systems can generate thousands of optimized variants that meet specified requirements, and cull down to the most qualified. Engineers will focus on evaluating these machine-generated options and only make strategic decisions that require human judgment.

Similarly, autonomous manufacturing and adaptive supply chains will leverage real-time digital data to make production decisions with minimal human intervention. When demand fluctuates, your supply chain will automatically adjust component orders and supplier capacity allocation. If production processes experience drift, MES systems will automatically adjust parameters to compensate. This level of responsiveness demonstrates a new guard of agility in responding to changing customer requirements and market conditions.

Finally, sustainability and circular lifecycle management will become central to defense procurement. Digital twins will enable you to track material flows through multiple product lifecycles, identifying opportunities for remanufacturing and recycling. Lifecycle assessment data will guide design decisions toward sustainable alternatives. Embedding sustainability into DE practices now positions you for new regulatory requirements that are virtually certain to arrive within the next decade.