Engineering is increasingly defined by complexity. As the machinery and systems that underpin our world grow more complex, so do their logic and control systems.
Engineering teams are tasked with delivering millions of lines of high-assurance code under tighter schedules and increasing regulatory pressure, pushing traditional development processes to their limits.
These challenges are pushing more engineering teams to adopt a development process previously centred in the aerospace and automotive sectors: Model-Based Design.
Model-Based Design is a development process that uses a single, system-level digital model as the central guide for a project from start to finish.
This approach replaces traditional, sequential methods that rely on separate text documents, expensive physical prototypes and manual, error-prone coding. The model functions as an “executable specification,” allowing for continuous simulation and testing from the earliest stages of design. By enabling engineers to identify and fix flaws early – when they are cheapest to resolve – and to automatically generate software code, this method significantly reduces costs and accelerates project timelines.
Studies have shown teams can achieve up to 60% cost reductions while recapturing valuable engineering time. Crucially, Model-Based Design is not just about efficiency. It enables digital continuity by uniting mechanical, electrical, and software engineering under one environment, improving traceability, feeding operational insights back into design, and supporting broader digital transformation initiatives.
Overcoming the adoption hurdle
Despite the proven benefits, a common hurdle for teams considering a switch to Model-Based Design is the perception of the transition itself.
“A challenge that teams think they have is that they have to do it all at once, which is actually not something I would recommend,” said Ruth-Anne Marchant, Application Engineering Manager for MathWorks Australia. Many engineering managers worry about the perceived risk, time consumption, and potential skills gaps involved in overhauling established processes.
Marchant advises a more pragmatic path. “Take it piece by piece,” she said. “Pick a small part of your project. Pick a small element of Model-Based Design, start there and then grow out from there.”
In practice, this could mean running a pilot project that develops a virtual model of a physical system in parallel with existing hardware-based workflows. “They can continue going the way they’re going and then have this other pilot going on,” Marchant explains, noting that this approach helps teams validate the model while using learnings to inform and improve their traditional hardware testing processes.
De-risking innovation
This incremental, de-risking approach isn’t just for large, established companies. In fact, it’s a powerful strategy for startups, where resources are tight and the margin for error is slim. This is exemplified by Stralis, a Queensland-based startup developing emission-free, hydrogen-electric aircraft.
“Aerospace has some unique challenges, particularly around weight and power density,” said Mark Broadmeadow, propulsion lead at Stralis. To tackle this, the firm uses Model-BasedDesign with hardware-in-the-loop testing to de-risk its development.
“The value of hardware-in-the-loop is the ability to de-risk our development to show behaviours in hardware-in-the-loop testing that we wouldn’t see in pure software modeling,” Broadmeadow said. This high-fidelity simulation allows the team to predict unexpected issues and proactively implement solutions or choose different components before committing to expensive physical builds. The approach has proven essential for tackling their biggest technical hurdle: thermal management for their novel fuel cells.
“For a small company, this capability is game-changing,” said Stralis co-founder and CEO Bob Criner. “We decided to jump into this program early on because it’s a very affordable way for a startup like u s to get access to a huge suite of valuable, capable tools,” Criner said.
Tangible returns
Beyond the strategic advantages, Model-Based Design delivers practical, day-to-day benefits that resonate directly with engineers. The primary gain is time, which can be reinvested into innovation.
This is most evident in the move from manual coding to automatic code generation. Marchant recalls a conversation with an automotive customer who quantified the impact directly. By automatically generating code from a model instead of tasking a software engineer to rewrite it manually after each test, they were able to deploy it onto the hardware later the same day. This change, she said, “helps save them at least a day per design iteration.”
This ability to rapidly iterate, test, and validate is central to the ROI of Model-Mased Design. Defects found during the initial requirements and design phases are orders of magnitude less costly to fix than those found later during physical testing. By shifting defect discovery earlier, teams reduce costs and improve the quality of the final product.
Although Model-Based Design first emerged in aerospace and automotive design due to its complexity, Marchant is seeing increasing applications in a much broader swathe of industries, from energy to resources to consumer electronics.
Adopting this approach delivers a clear business ROI and represents a vital opportunity for professional development. For engineers, developing these capabilities can future-proof their careers.
