As modern systems – from autonomous vehicles to industrial automation and machinery – grow more complex, the software that powers them must evolve just as quickly.
Engineering teams are now asked to deliver millions of lines of high-assurance code under tighter schedules and increasing regulatory pressure. Traditional development processes – linear, tool-fragmented, and reliant on manual coding – are hitting their limits.
Jason Ghidella, Principal Product Manager at MathWorks, explores how model-based design is helping engineering teams tackle rising system complexity, improve collaboration, and accelerate delivery.
In what ways does model-based design enable tighter integration between mechanical, electrical, and software disciplines compared to traditional development approaches?
In traditional document-based workflows, teams often work in silos – mechanical, electrical, and software engineers each do their bit, then hand things off. Integration happens late in the game, once components are already designed and built. And that’s precisely when errors tend to surface. At that stage, fixing them isn’t just frustrating – it’s expensive and time-consuming, often requiring significant rework and delays to the overall timeline.
Model-based design enables all engineering domains to work from a shared executable specification – starting right at the system engineering stage. This means requirements can be validated and refined early, and different domain teams can reference those models for their detailed designs.
For control and software teams, these models aren’t just references – they’re the foundation for detailed design and implementation. Because they’re executable, they allow for virtual testing of software before physical prototypes are even built. This helps teams catch issues early, reduce ambiguity, and ensure alignment across disciplines.
Bosch eBike Systems is a great example. Their teams used model-based design to co-develop mechanical and software components for their electric bike controller, improving traceability and reducing integration risk across domains. With Model-Based Design, you get digital continuity from concept to deployment, which is a huge win for collaboration and quality.
What are the biggest organisational or cultural barriers to adopting model-based design, and how have successful teams overcome them?
One of the biggest barriers is the shift from document-based workflows to working with executable models. It’s a change in mindset, and understandably, some teams are hesitant – especially in industries where safety and certification are paramount.
Culturally, there can be resistance to new tools and methods. But successful teams often start with pilot projects, demonstrate early wins, and build confidence through simulation and virtualisation. When engineers see how model-based design helps catch errors earlier and speed up development, adoption tends to follow.
Leadership also plays a key role. When engineering leaders tie model-based design to business outcomes, like reduced costs or faster delivery, it becomes easier to bring the organisation along.
How are engineering teams quantifying the returns produced by model-based design? What are they measuring ROI against? (Is this just the cost of the software initially or the cost of the entire project?)
It’s a fair question – and one we hear often. The short answer is: No, it’s not just about the cost of the software. Engineering teams are measuring ROI against the entire system development lifecycle – from requirements and design all the way through to testing and deployment.
Now, you might think that’s too broad to quantify meaningfully, given all the variables involved. But that’s where structured ROI frameworks come in. These frameworks help teams track specific metrics like reduced development time, fewer integration issues, and lower testing costs. And because model-based design enables early simulation and automatic code generation, many of those variables become more predictable.
Take Dyson, for example. They used system-level simulation and rapid prototyping to identify and resolve issues early, which helped reduce rework and accelerate their development. That translated into faster design iterations and a more robust product with measurable savings in time and cost. The scope is broad, but the benefits are measurable. It’s not just a software upgrade but is a smarter way to engineer.
What metrics or benchmarks have proven most effective in demonstrating value to stakeholders? Why?
The most compelling metrics are the ones that speak to both engineering and business outcomes. We’re talking about reductions in development time, fewer design errors, faster software releases, and improved product quality.
Danfoss, for example, applied model-based design across its energy systems and saw significant gains in quality and deployment speed. Ather Energy used model-based design to develop electric scooters and charging stations, accelerating their control system development and reducing verification time.
Stakeholders love these kinds of metrics because they’re easy to tie back to business goals – faster time to market, lower costs, and better-performing products.
How does model-based design support CI/CD practices in complex, safety-critical systems where validation and certification requirements are stringent?
model-based design is fully compatible with CI/CD workflows. Traditionally, model-based design has helped teams improve product development on the desktop – through simulation, automated testing, code generation, and report generation. But all that functionality can also run in CI/CD pipelines.
You’re working at a higher level of abstraction with models, but those build and test steps work very well in automated workflows. This means teams can continuously validate system behaviour, run regression tests, and generate production code – all within a CI/CD environment.
This is especially valuable with safety-critical systems in industries like automotive and aerospace, where compliance with standards such as ISO 26262 or DO-178C is essential. With model-based design, you can automate documentation, reuse test cases, and maintain alignment with certification requirements.
How do you see model-based design evolving over the next five years as digital transformation and AI continue to reshape the engineering landscape?
We’re seeing a clear shift toward software-defined systems where software isn’t just an add-on, but the core of the product’s functionality. As this trend accelerates, document-based approaches will continue to be replaced by model-based design.
With this explosion of complexity, engineering teams will need to deal with systems at a higher level of abstraction from the very start, getting a good understanding of requirements and removing ambiguity early. It’s also likely we’ll see digital twins used across every stage, from conceptual design through to operation and optimisation, helping teams understand the effects of design decisions and ensure safe, robust systems before ever building physical prototypes.
Over the next five years, model-based design will play an even greater role in helping teams manage complexity. By integrating real-time operational data, simulation, and AI-driven modelling, digital twins will support predictive maintenance, smarter control strategies, and faster adaptation to changing conditions. This will allow teams to explore more scenarios, validate performance earlier, and build confidence in their designs – all within a virtual environment.
Author biography:
Jason Ghidella is Principal Product Manager at MathWorks. Prior to this role, Jason held positions as a Technical Support Manager at International Technologies and Mango DSP, where responsibilities included providing application engineering and technical support for MathWorks 3rd party products and software services related to DSP hardware systems, respectively. Earlier career roles included Application Engineer positions at Scientific Software Benelux and ceanet, focused on MathWorks products, as well as a Research Scientist role at DSTO. Jason holds a Ph.D. in Aeronautical Engineering from the University of Sydney and a Bachelor of Engineering in Mechanical from James Cook University.
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