Why is Digital Engineering a Game-Changer for Complex Systems?
Systems integrate mechanical, software, and electrical subsystems at an unprecedented scale. Aerospace and Defense (A&D) industries face this reality on a daily basis. However, managing this complexity requires more than traditional engineering; it requires an extensive and well-thought-out digital approach. Digital engineering offers these industries the tools and methodologies to develop, verify, and operate complex systems efficiently and reliably. In fact, the premise of digital engineering leads the Department of Defense (DoD) to officially position its efforts to accelerate the implementation of a comprehensive digital engineering strategy across its systems and programs.
In this article, we will dive into Digital Engineering, why it matters, what it means, and why it is transforming systems development in the A&D sector, and how SodiusWillert supports the implementation of Digital Engineering for systems development.
TABLE OF CONTENTS
What is Digital Engineering?
Why Digital Engineering?
The Key Components of Digital Engineering
The Benefits of Digital Engineering
Typical Challenges Faced When Adopting Digital Engineering
How SodiusWillert Contributes to the Implementation of Digital Engineering
FAQ about Digital Engineering
What is Digital Engineering?
Digital engineering is a comprehensive approach that leverages digital models, simulations, data, and processes to develop, validate, and sustain complex systems into a streamlined, agile process. Instead of relying solely on physical prototypes and document-based workflows, digital engineering integrates all lifecycle phases through interconnected digital representations and artifacts.
Unlike traditional systems engineering frameworks, which follow quite rigid document-driven processes, digital engineering emphasizes real-time collaboration, rapid prototyping, and continuous integration across disciplines. But to get there, digital engineering involves a significant shift in mindset.
➡️ Learn more about SodiusWillert Solutions for Digital Engineering
Building a model-centric, collaborative engineering culture
Teams need to consider the digital model as an Authoritative Source of Truth (ASOT), the only environment where all stakeholders (engineering teams, partners, and decision-makers) access and contribute to data.
For example, a defense contractor, a government acquisition team, and a software supplier can all work from shared digital models, ensuring alignment and reducing communication errors. Everyone uses shared data formats and models, enabling faster decisions and better results throughout the product lifecycle. The various digital models are interconnected using digital threads, ensuring information flow, traceability, and consistency across the models.
From digital document to digital engineering
The field has grown from simple digitization (converting material documents) to more complex workflows. Over time, it evolved to embrace model-driven design, enabling engineers to represent complex systems through dynamic, reusable models rather than static documents.
This shift laid the groundwork for advanced practices such as digital twins and the integration of diverse engineering data via connected platforms. Today, digital engineering supports continuous collaboration, traceability, and lifecycle management across disciplines and tools.
Advances in computing power, data analytics, connectivity, and AI techniques have made digital engineering more practical and effective than ever.
|
🔎 Digital Engineering vs. Engineering Digitization While related, these terms differ. Sometimes, they are even wrongly referred to as implying the same idea, so knowing the distinction is important.
Digital engineering harnesses digitized data within a structured framework, enabling decision-making, collaboration, and continuous updates. |
Fig.1 - U.S. Department of Defense digital engineering framework
Why Digital Engineering?
A&D industries are under an ongoing complexity challenge, to say the least. The systems they develop must not only perform flawlessly under demanding conditions but also adhere to rigorous safety and regulatory standards. Increasingly, these systems are part of broader, interconnected ecosystems that span multiple disciplines. This evolving landscape requires engineering teams to navigate a web of technical, operational, and compliance-related challenges with precision and foresight.
1) Accelerating delivery through early verification and optimization
By leveraging Model-Based Engineering (MBE), teams can simulate, verify, and optimize systems early in the lifecycle, before physical prototypes even exist. Upstream validation accelerates development timelines, reduces costly late-stage changes, and significantly improves design quality. MBE enables faster iterations, deeper insights, and higher confidence in system readiness from the very start.
2) Enhancing cross-disciplinary coordination
Aircraft, spacecraft, and defense systems combine the integration of advanced materials, sophisticated electronics, and complex software – all developed by separate teams using different tools and processes. Digital engineering provides a shared, interoperable foundation to synchronize work across disciplines and reduce errors due to misaligned data or delayed updates.
3) Managing data at scale and with integrity
Across the lifecycle, engineering generates massive, interconnected data: from requirements and design to validation and operations. Digital engineering enables structured data management practices that support real-time decision-making while also protecting information from cyber threats through better governance and secured exchanges.
4) Integrating tools, processes, and people in systems engineering
A key objective of Digital Engineering in A&D industries is to align mission planning with system design. Rather than developing systems in isolation, digital engineering enables mission objectives to directly shape technical requirements and architecture. Without this alignment, systems risk being over-engineered, misaligned with operational needs, or requiring costly redesigns late in the lifecycle. Digital Engineering helps avoid these pitfalls by providing a connected environment where strategic intent and technical execution evolve together, as well as tools, processes, and people.
5) Automating and documenting regulatory compliance
Safety and regulatory compliance are non-negotiable in A&D as they drive system safety, reliability, and cybersecurity. Digital engineering allows traceable, auditable documentation of design decisions, requirement links, and verification evidence, making it easier to demonstrate conformity with standards throughout the lifecycle.
6) The pressing need for constant agility
In the A&D sectors, change is the only constant. Whether driven by geopolitical realities, acceleration of technical advancements, or evolving mission requirements, engineering teams must be able to adapt fast, in all circumstances, and sometimes even in real time. Traditional development models, based on strict, sequential workflows, simply can't keep up with this pace. Therefore, Digital Engineering embraces agile practices. It enables rapid, well-informed decision-making and continuous access to reliable, constantly updated digital models.
The Key Components of Digital Engineering
Digital Engineering comprises several interconnected tools and practices. Here is a brief overview of its core components, highlighting their roles in supporting complex systems development.
1) Digital Thread
More than just a buzzword, the digital thread creates a continuous, traceable, and reliable flow of data across the lifecycle phases (concept, design, manufacturing, operation, and retirement). It ensures that data flows seamlessly across the product lifecycle stages, enabling comprehensive traceability, impact analysis, and decision-making. By connecting siloed teams through a shared digital backbone, the digital thread fosters real-time collaboration and informed decision-making. It breaks communication barriers, allowing stakeholders to act on the same up-to-date data, regardless of their function or location.
The digital thread fosters more efficient engineering processes, which ultimately results in shorter development times and time-to-market.
📰 Read more about Digital Thread
2) Digital Models
At the heart of Digital Engineering lies Model-Based Systems Engineering (MBSE) and many other models such as mission models, disciplinary models (electrical/software, etc.), safety models, and more.
But let’s focus on Model-Based Systems Engineering (MBSE), which puts system models at the center of system design and development. Instead of relying on documents scattered here and there, MBSE relies on visual, structured models to define how a system works: its functions, structure, and behavior
MBSE supports every phase of the lifecycle, from requirements and design to testing and validation. It improves clarity, reduces errors, and helps teams make more informed decisions by providing a shared, consistent view of the system.
3) Digital Twins
Often confused with digital thread, a digital twin is a virtual replica of a physical asset or a system.
More specifically, an engineering twin is developed during the design and development phase. It is built from models (system architecture, behavior, and performance) and simulations that represent how a product should behave. It’s used to validate design decisions, simulate different scenarios, predict performance, and align multidisciplinary teams on a shared system of understanding.
It remains static or version-controlled until the product enters operation. For example, in the aerospace industry, an engineering twin of a satellite may include thermal and orbital dynamics models to verify performance under different mission profiles before launch.
4) Digital Artifacts
Digital artifacts encompass all the structured digital outputs and MBSE models generated throughout the engineering lifecycle, including models, simulations, test results, requirements, and even 3D print files. These artifacts are not isolated. They are interlinked and version-controlled, forming the building blocks of a traceable and reusable engineering knowledge base, essential for implementing a digital engineering approach.
Here are a few examples:
- Modeling tools like IBM Rhapsody or Cameo Systems Modeler contain system models that serve as core digital artifacts for architecture, behavior, and interface definitions.
- Technologies based on Linked Data enable semantic linking of artifacts across tools, connecting requirements, models, test cases, change requests, and more. These are just a few examples among many.
And we could go on and on with this list of tools and technologies. The point is that together, these technologies help create a connected, interoperable ecosystem where digital artifacts are not just outputs but active, evolving assets that drive collaboration, traceability, and informed decision-making.
5) Digital viewpoints
Digital viewpoints help stakeholders access, extract, and visualize the specific data they need from the broader digital thread. Rather than navigating entire models or systems, each stakeholder (engineer, manager, tester, and even customer) can interact with curated views tailored to their needs. This enhances clarity, reduces information overload, and supports better decisions at every lifecycle stage.
Tools like SodiusWillert SECollab enable digital viewpoints by aggregating data across sources while maintaining traceability. As systems grow in complexity, these filtered perspectives become essential for collaboration and alignment across multidisciplinary teams.
The Benefits of Digital Engineering
Digital engineering enhances efficiency, reduces risks, and fosters innovation. Its numerous benefits include:
1) Early Design Validation and Risk Mitigation
Models and simulations clarify requirements by enabling early validation. Requirements are formally represented within digital models, making them explicit, measurable, and traceable. This reduces ambiguities and enhances compliance with standards and regulations. Digital engineering enables early validation of requirements, interfaces, and dependencies through model-based analysis.
Engineers can identify inconsistencies, gaps, or conflicts in system definitions before physical development begins. Integrated change tracking and impact analysis tools help assess the consequences of design modifications, allowing teams to make informed decisions and avoid costly errors.
2) Accelerated innovation
Digital environments facilitate fast conceptual exploration. Engineers can test even the most unconventional ideas virtually because it reduces the time and cost of experimentation. This approach fosters the development of cutting-edge systems and new solutions that might be unachievable with traditional methods.
3) Effective system lifecycle management
Digital engineering enables continuous updates and upgrades by maintaining a consistent, model-based thread of system data across the lifecycle. For example, configuration management tools integrated into digital workflows help track design changes, ensure version control, and support impact analysis. This reduces the risk of inconsistencies, improves maintainability, and ensures systems evolve in alignment with operational needs, ultimately lowering lifecycle costs and increasing system reliability.
4) Enhanced collaboration and communication
A centralized digital repository ensures that all stakeholders access the most current information. This improves communication clarity and consistency. Teams across disciplines and organizations can collaborate more effectively, sharing models, reviews, test results, and reports in real time. Such transparency reduces misunderstandings and accelerates decision-making cycles.
Speaking about faster decision cycles...
5) Data-driven decision-making across the system lifecycle
Digital engineering enables a unified, model-based view of system architecture, requirements, and constraints. Engineers can explore design alternatives with better clarity, supported by traceable data and structured analysis. This visibility helps identify trade-offs early, align technical choices with project objectives, and reduce uncertainty. It leads to more informed and confident decisions throughout the development lifecycle.
6) Cost Reduction
And we couldn't tackle Digital Engineering benefits without speaking about cost. The end-to-end visibility initiated by Digital Engineering allows teams to detect inconsistencies early, minimizing rework and late-stage changes. Automated traceability also streamlines compliance and impact analysis, reducing manual effort. By reusing validated models and maintaining a clear audit trail, organizations accelerate development, avoid duplication, and cut down on unnecessary testing, ultimately lowering both time and financial investment.
Typical Challenges Faced When Adopting Digital Engineering
While the potential of digital engineering is undeniable, its adoption even in large, well-established structures faces persistent technical, organizational, and cultural obstacles.
1) Cultural resistance
Transitioning from manual and document-based processes to digital workflows is not just about adopting new tools; it’s a deep cultural change in mindset. We ask teams to instantaneously accept new tools, methodologies, and model-driven practices after years, if not decades, of manual and paper-based habits. Resistance to change, lack of training, and cultural inertia make this transition difficult, requiring strong leadership, clear vision, and sustained support to succeed.
2) Choosing a proper digital platform
One of the most common obstacles to the adoption of digital engineering is choosing a platform that truly matches the company's tools, processes, and priorities. While some companies try to extend their existing PLM systems to meet the needs of digital engineering, others look for in-house solutions or opt for more open and flexible platforms, designed with integration and traceability in mind. There is no one-size-fits-all solution: what works for a component supplier may be less suitable for a systems integrator. The key is to align the platform with engineering workflows and data structures, not the other way around.
3) Integrating existing tools and digital platforms
Many organizations already rely on a fragmented set of specialized tools: modeling environments, requirements solutions, simulation platforms, test management tools, etc. Digital engineering does not intend to replace them all; it connects them.
Ensuring seamless data exchange, consistent identifiers, and traceability across tools is complex but essential. Poor integration leads to silos, duplicated effort, and inconsistent data, undermining the core promise of digital continuity.
4) Cost of the Digital Platform and Modeling/Simulation Tools
The financial cost of implementing a digital engineering approach goes beyond licensing fees. It includes integration efforts, customization, training, ongoing support, and potential disruptions during the transition. While some platforms promise "all-in-one" capabilities, they may introduce limitations or vendor lock-in. Alternatively, combining multiple specialized tools to cover different needs can increase complexity and demand significant investment in integration and long-term maintenance. A clear cost-benefit analysis should account for long-term ROI, not just short-term savings.
5) How to Perform a Low-Risk Adoption of New Tools Across the Enterprise
A successful digital engineering transformation rarely comes from a “big bang deployment”, as with any new approach, in fact. We've already discussed this phenomenon in a previous article on the question of implementing MBSE.
Low-risk adoption typically involves piloting tools in targeted programs or teams, learning from their feedback, and scaling incrementally. Key success factors include strong executive sponsorship, cross-functional collaboration, and well-defined metrics for success. It’s also critical to provide training and support to ensure adoption is not just technical but also practical and user-centered.
How SodiusWillert Contributes to the Implementation of Digital Engineering
SodiusWillert offers solutions that support the shift to Digital Engineering. Our focus is on enabling defense and aerospace organizations to meet their data strategy goals based on visibility, accessibility, interconnectivity, security, and reliability.
➡️ Find out more about SodiusWillert solutions for digital engineering
“Digital Engineering requires the ability to connect all related assets. That’s where Sodius Technology helps. We can link together repositories across disciplines, across that digital thread to enable digital engineering. Digital Engineering also requires maintaining a consistent configuration across repositories—so we can clearly track which data belongs together and which versions align with each other.”
Engineering data interoperability
We provide solutions that facilitate the exchange of engineering data across various engineering lifecycle tools, breaking down silos between software tools, repositories, domains, lifecycle stages, and much more. Focusing on systems and software development tools, our solutions support engineering teams who need to:
- Move system model designs between different modeling tools (from IBM Rhapsody, SPARX Enterprise Architect, UNICOM System Architect, or IBM Rational Software to Cameo Systems Modeler)
- Connect specific repositories (Cameo Teamwork Cloud to Confluence, IBM Rhapsody to Jira, and more)
- Connect their lifecycle development tools (ALM, PLM, CM, MBSE, and much more).
Building a Digital Thread
SECollab supports the creation and management of a digital thread on a single web-based platform. Import engineering data and documents from different tools (Excel, PDF, Cameo, IBM DOORS, etc.), link configured data, visualize digital viewpoints, and create automated traceability reports to demonstrate regulatory compliance.
Linked Data
At SodiusWillert, we believe that Linked Data is the future of engineering data management. Our expertise directly supports the vision for digital engineering by enabling interoperable, trustworthy, and accessible data across complex systems. Our solutions eliminate data silos, provide secure and authenticated access, as well as ensure data is linked and semantically aligned across domains and suppliers. This empowers our customers to maintain a real-time, unified digital thread. We help their teams stay agile, compliant, and mission-ready.
Frequently Asked Questions (FAQ) about Digital Engineering
1. How does digital engineering relate to digital transformation?
Digital engineering is a digital transformation of the product engineering process.
2. How does digital engineering improve compliance?
Digital Engineering’s processes and tools significantly enhance compliance by enabling real-time traceability between requirements, models, and tests, supporting, for instance, standards like DO-178 with verifiable evidence.
3. What tools support digital engineering?
We can distinguish two categories: backbone tools and modeling tools. Modeling tools like IBM Rhapsody and Cameo Systems Modeler support MBSE and system design. Backbone tools enable traceability, data exchange, digital views, configuration, and lifecycle analytics, ranging from lightweight solutions like OSLC Connect to full platforms like SodiusWillert SECollab. Some organizations attempt to achieve this using PLM systems.
4. Can digital engineering reduce project costs?
Yes. Early validation and automation reduce rework, testing, and late-stage changes.
5. Is digital engineering only relevant to large A&D projects?
No. Any engineering team developing complex, regulated systems benefits from model-based, traceable workflows.
➡️ Explore all our solutions for implementing the Digital Engineering
➡️ Contact us if you have any questions!
CONCLUSION
There's no longer any doubt about it. Digital Engineering is now an integral part of modern aerospace and defense systems development. This rapid adoption by these sectors is hardly surprising, as it provides a real and complete answer to the growing complexity of systems, the growth of data, and the need for seamless collaboration between disciplines. Through model-based approaches, the culture of data continuity, integrated platforms, and digital twins, organizations can improve decision-making, reduce costs, and enhance system reliability. These capabilities also foster innovation and ensure compliance with stringent industry standards.
SodiusWillert empowers this transformation by delivering solutions that ensure interoperability, secure data exchange, and continuous connectivity. Our deep expertise enables organizations to manage complexity, maintain regulatory compliance, and respond swiftly to evolving demands.
So, the future of systems engineering lies in embracing digital engineering. Organizations that adopt these practices will be better positioned to deliver innovative, dependable, and cost-efficient solutions in an increasingly dynamic environment.
Leave us your comment