Model-based systems engineering (MBSE) is often presented as a way to improve traceability, accuracy and efficiency. But moving large volumes of existing engineering information into a model can create a significant practical challenge.
David Fields, President, co-founder and Chief Technology Officer of Enola Technologies, has seen that challenge firsthand. His company specializes in model-based systems engineering consulting, services and training, with particular expertise in CATIA Magic and No Magic tools.
For David, the value of MBSE comes from connecting engineering information and making it usable across modeling, simulation and other disciplines. But the real productivity gains appear when organizations go beyond basic modeling and take advantage of automation, openAPIs, AI and the capabilities of SysML v2.
Why connected engineering data matters
David came to MBSE after working in traditional systems engineering environments where much of the work was document-based. On some of the programs he worked on at the Navy, performing impact analysis and understanding the full scope of requirements and systems could be difficult.
MBSE provides a different approach by capturing engineering information in a model and connecting that information.
For David, the model is valuable not only because it provides a structured description of the system, but because it gives engineers a way to communicate that information to stakeholders and leadership.
The connections between model data, simulation and other engineering disciplines can also improve efficiency and accuracy.
This interconnected structure is one of the fundamental reasons David sees MBSE as more than simply a replacement for documents. The model becomes an environment in which engineering information can be related, analyzed and communicated.
The problem of bringing legacy data into the model
Once an organization decides to adopt MBSE, however, there is an immediate practical question: what happens to all the information it already has?
David identifies data ingestion as one of the major challenges facing his customers. Legacy programs can contain thousands or even tens of thousands of requirements stored in traditional requirements management systems such as DOORS.
Requirements are not the only issue. Test data and other engineering information may also need to be brought into the MBSE environment.
The challenge is therefore not simply importing a file. Organizations need to get large quantities of information into the model accurately and then make that information useful within the modeling environment.
For large programs, doing this manually can quickly become a major drain on engineering resources.
Using the CATIA Magic Open API to automate the work
This is where David sees a particular advantage in CATIA Magic and Cameo: their Open API capabilities.
He highlights both the Open API and REST API, with particular emphasis on the Java Open API, as powerful tools for automation and integration.
These APIs can be used for custom imports, integration with other engineering tools, and manipulation or refinement of information that has already been brought into the environment through DataHub or manual imports.
The significance becomes clear in one customer example.
The customer had received more than 90,000 requirements that needed to be incorporated into the model. Many of those requirements were outdated and referred to a system that no longer existed.
Manually updating that volume of information would have been a huge undertaking.
Instead, Enola Technologies helped the customer develop automation using the CATIA Magic Open API. The scripts could automatically adjust requirement text where necessary and create relationships for verification, test cases and other elements.
The result was a dramatic change in the workflow: a process that had previously taken months could be executed in minutes.
Even more importantly, the automation could be reused. When the customer received another data drop, the scripts could be run again to prepare the information for the next release.
That example illustrates how an MBSE tool’s API can become more than a development feature. It can provide a mechanism for turning repetitive systems engineering work into a repeatable process.
CATIA Magic brings modeling, simulation and integration together
David also points to the breadth of capabilities available through the CATIA Magic and No Magic product family.
From his perspective, one of its strengths is the combination of advanced simulation, API capabilities and integration with third-party platforms.
Rather than focusing on one isolated capability, David describes Cameo as providing a broad set of functions across the MBSE workflow.
Simulation is particularly important within that portfolio. Combined with the API and external integrations, it provides organizations with ways to connect modeling activities to other engineering environments.
David also notes the familiarity of these tools within the Department of Defense and aerospace communities. For Enola Technologies, that existing familiarity can make it easier to engage with customers who already understand the environment and its capabilities.
AI can take repetitive MBSE tasks further
David is already experimenting with AI in MBSE and sees significant potential in combining artificial intelligence with the structured data found in engineering models.
MBSE environments can contain structural information in formats such as XMI and XML. David believes large language models are well suited to understanding this kind of structured information.
One potential application is checking modeling standards. AI could help identify whether models conform to an organization’s expected practices.
Requirements are another area of interest. AI could assist engineers in writing better requirements and identifying areas where the system may not have been fully considered because requirements were incomplete.
For David, these capabilities could eventually become a normal part of modeling workflows.
The key benefit is not that AI can perform tasks engineers are incapable of doing. It is that AI can potentially perform repetitive activities much faster, allowing engineers to concentrate on problems that require human judgment.
Note that Dassault Systemes is rapidly expanding out of the box enterprise level AI capabilities to above mentioned and other use case.
SysML v2 makes textual modeling particularly interesting
David is especially enthusiastic about one aspect of SysML v2: its textual notation.
Coming from a computer engineering and programming background, he finds the textual representation particularly natural to work with.
But his interest goes beyond personal preference. He identifies two practical advantages.
First, textual SysML v2 can work well with AI. Structured textual representations give AI systems a form of model information they can process and generate.
Second, textual notation can make it easier to share pieces of a model. Instead of exchanging an entire model, engineers could potentially share snippets, compartments or examples more easily.
David also welcomes the move away from UML as the foundation of SysML. While he acknowledges the value of UML, he believes that moving away from that legacy creates an opportunity for a language designed more specifically around the needs of systems engineering.
Together, textual notation and a new language foundation make SysML v2 an important part of the future workflow David describes.
MBSE adoption is spreading beyond aerospace and defense
David has spent many years working with MBSE, and he sees a change in how organizations approach the technology.
Previously, consultants often had to spend significant effort explaining why MBSE was valuable. Today, he says, more organizations already recognize it as part of the future of engineering.
That does not mean implementation has become simple. Instead, the focus is increasingly shifting from convincing organizations to helping them implement MBSE effectively.
Interest is also spreading beyond traditional aerospace and defense programs.
David is seeing demand from biomedical organizations, energy companies and smaller manufacturers. In some cases, adoption is driven internally; in others, suppliers or customers are introducing MBSE and creating pressure further along the supply chain.
This suggests that MBSE is increasingly becoming relevant to organizations that may not have considered model-based engineering several years ago.
A CATIA Magic deployment needs more than a SysML course
For organizations beginning their MBSE journey, David emphasizes an issue that can easily be underestimated: training.
Selecting an MBSE tool is only one part of implementation. Organizations also need to prepare the people who will create, use and consume the models.
That includes engineers and model practitioners, but David argues that leadership, stakeholders and other disciplines also need appropriate training if they will interact with or use model information.
He compares this with CAD. Organizations generally do not expect someone to become an expert CAD user after a single week of training.
Yet MBSE programs can sometimes make the unrealistic assumption that a practitioner will become an expert after a one-week SysML course.
David argues that successful teams need a broader range of skills. Beyond the basics, engineers may need to know how to query models, run simulations and extract model information into the formats required for presentations, documents or contracts.
This means that organizations adopting CATIA Magic need to think about developing MBSE capability over time rather than treating initial training as the endpoint.
Automation, AI and expertise point toward a different MBSE workflow
David’s experience points to a shift in what MBSE productivity can mean.
The first step is creating a connected model containing engineering information. The next is making that model useful through simulation, integrations and APIs. Automation can then eliminate repetitive work, as demonstrated by the 90,000-requirement example.
AI adds another potential layer by helping engineers interact with structured model data, review requirements and modeling practices, and identify potential gaps.
SysML v2, meanwhile, introduces textual modeling that David sees as particularly compatible with both AI and software-oriented workflows.
Together, these capabilities suggest an MBSE environment in which engineers spend less time manually manipulating information and more time solving the system-level problems that require their expertise.
Conclusion
For David Fields, the future of MBSE is not simply about creating more models. It is about making those models easier to populate, connect, query and improve.
CATIA Magic‘s open APIs provide one route to automation today, while AI and SysML v2 open further possibilities for reducing repetitive work and interacting with structured engineering information.
The goal is ultimately practical: let technology handle more of the repetitive work so systems engineers can focus on the problems that still require engineering judgment.
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FAQ
- What does Enola Technologies specialize in?
Enola Technologies is a model-based systems engineering consulting, services and training company. David Fields says the company has particular expertise in CATIA Magic and No Magic tools, with many of its employees having 10 to 15 years or more of MBSE experience.
- How did CATIA Magic help with a 90,000-requirement project?
Enola Technologies used the CATIA Magic Open API to automate the processing of more than 90,000 requirements. The automation could adjust outdated requirement text and create relationships for verification and test cases. A process that previously took months could then be performed in minutes.
- What role does the CATIA Magic Open API play in MBSE?
David uses the Open API, particularly the Java Open API, for automation, custom imports, integrations and manipulation of model data. It can also be used to automate recurring processes when new data needs to be incorporated into a model.
- Why is David Fields particularly interested in SysML v2’s textual notation?
As a computer engineer with a programming background, David finds textual notation natural to work with. He also sees two major benefits: it can work effectively with AI and makes it easier to share smaller pieces of model information, such as snippets and examples.
- What should companies consider when implementing MBSE?
David recommends looking beyond the software itself. Organizations need to define what they want to accomplish and prepare different groups through appropriate training. Engineers may need skills in modeling, querying, simulation and extracting model data, while leadership and stakeholders also need to understand how they will interact with the models.
Saulius Pavalkis is a CATIA CYBER SYSTEMS Industry Process Expert Director, within the CATIA Cyber Systems EE&F AMERICA organization.