Across four vehicle development programmes, engines built by Jaguar Land Rover, Isuzu, Mahindra, and Sharda Motor Industries, engineers using the same simulation software cut development time by an average of 58% and reduced costs by 43%. Those are not projections. They are outcomes—documented across production programmes, on hardware that is on the road today.
The numbers deserve a moment because they reframe a question the automotive industry usually treats as an engineering one. It is a question of whether a manufacturer can afford not to, particularly in India, where development timelines are compressing and powertrain complexity is multiplying.
When Mahindra launched the BE 6 and XEV 9e last year, the vehicles had already survived a Rajasthan summer, climbed the Sahyadris, and cruised the Mumbai–Pune Expressway hundreds of times. Long before the first physical prototype was signed off, the company's engineering teams had run each of those scenarios virtually — testing how Indian heat, Indian gradients, and Indian traffic would affect range, battery performance, and thermal behaviour.
They then used those findings to shape the design itself. By the time a mule hit the tarmac, it was not discovering problems. It was confirming that a design already stress-tested in ways no proving ground could replicate — at speed or at scale — had held up. An earlier Mahindra programme developing a BS6 engine through the same platform had come in at 50% of its original development timeline and 40% below projected cost.
This is system simulation in practice.
What is simulation, and how is it different from ordinary CAD?
The automotive industry is familiar with CAD — Computer-Aided Design — which produces the geometry of a part or a vehicle. Simulation is a different discipline. Computer-Aided Engineering (CAE) tools model not what something looks like, but how it behaves under thermal stress, under electrical load, under the particular punishment of Indian roads, and across thousands of operating conditions simultaneously.
In a vehicle context, this means building mathematical models of individual systems, the battery pack, the thermal circuit, the engine, the HVAC, and integrating them so the software can solve their interactions together. What happens to cabin temperature when the battery is working hard on a 42-degree afternoon? How does that change the range calculation? How should the cooling circuit respond, and what does that response cost in energy?
These are not questions a CAD file can answer. A simulation model can — in minutes, not months, and without consuming a single physical part.
As Matthew Warner, Vice President at Gamma Technologies, put it at his company's annual technical conference in Pune this February: “The idea is to reuse that value you have in your CAE model to answer questions in other parts of the development organisation — controls, test, requirements engineering. One model that delivers consistent results regardless of the application.”
One source of truth, used everywhere. The 58% time saving, in large part, lives in that sentence.
Why the Urgency?
Three forces have converged to make simulation less of an engineering preference and more of a commercial necessity.
The first is powertrain complexity. The clean narrative of the green mobility transition has given way to something messier. OEMs are simultaneously developing battery EVs, hybrids, and updated internal combustion vehicles — not sequentially but in parallel across engineering teams. Each architecture demands its own simulation environment and its own validation loop. The workload has multiplied without a proportional expansion in headcount or timeline.
The second is the shrinking product cycle. “The cycle of product development has reduced significantly, from 3 to 5 years to now nearly 2 years,” said Dr N.H. Walke, Senior Director at ARAI, at the SIAT 2026 conclave in Pune, which drew a record 2,000-plus abstract submissions this year, with testing as its dominant theme. “Now it is concurrent engineering. Simulation, component development, and proving — all these tests have to happen simultaneously.”
A programme that could once afford to discover problems in sequence must now surface and resolve them in parallel. Isuzu Technical's commercial diesel programme, using the same simulation platform, cut dynamometer testing hours by 60% and costs by 45%, time that would otherwise have sat in physical test queues. Jaguar Land Rover's Ingenium 2.0L diesel programme achieved a 50% reduction in development cycle time and 35-40% cost savings.
The pattern is consistent enough across programmes to be structural rather than exceptional.
The third force is specifically Indian: the historical cost of proving vehicles abroad. For validation infrastructure that barely existed domestically, Indian OEMs travelled to European facilities — absorbing the time, the cost, and the strategic inconvenience of developing products calibrated to foreign conditions.
ARAI is closing the gap with domestic investment in crash labs, advanced battery test facilities, and ADAS proving grounds. Simulation compounds the value of that infrastructure by reducing the number of physical runs a validated design actually needs — which, in practical terms, also reduces how many flights an Indian engineering team needs to book to Stuttgart or Gaydon.
The market has priced the trajectory accordingly. Automotive simulation software was valued at $7.06 billion globally in 2025 and is projected to reach $24.35 billion by 2034, a CAGR of 14.75%, according to Precedence Research.
A Company that Saw this Coming in 1994
Gamma Technologies was founded in Illinois thirty-one years ago on a premise that was, at the time, contrarian. Most CAE vendors were building tools for single-physics, single-component analysis. GT's founders believed the more valuable problem was the system as a whole.
“Products do not exist in single physics domains — they exist as multi-physics systems,” says Dimple Shah, who has led the company as CEO since 2020 and has worked in CAE since 1991. “The underlying thesis was that with time, the complexity of systems would increase, and as complexity increases, it becomes important to understand the interdependencies of subsystems with each other.”
Thirty years later, that thesis is mainstream. GT-SUITE, Gamma's flagship platform, covers engine performance, battery electrochemistry, electric powertrain, thermal management, exhaust aftertreatment, fuel cells, and HVAC within a single integrated environment. Its user base spans every major global OEM; in India, it includes Tata Motors and Mahindra.
GTTC 2026 was the occasion to launch GT Intelligence Studio — an AI-native addition to the platform that layers generative AI and machine-learning meta-models onto the existing physics engine. The announcement raises an obvious question from engineers working in safety-critical systems: how do you trust an AI model when the output affects brake calibration or a battery management decision?
Shah's answer is grounded rather than promotional. “The quality of meta-models depends on the quality of the datasets they are trained on. Our meta-models are trained on physics data, which can also be augmented by data from external sources. Because a large part comes from physics itself, the trust in the model is high. One should not deploy meta-models blindly; just like any simulation model, you need confidence that it is applicable in the range you intend to deploy it.”
Warner adds context that matters: the machine learning component is not a new development. “We have had it in our software for almost 20 years. The usage of ML meta-models is often not deployed to an actual vehicle in operation. They are utilised within the development process of the vehicle, so in that use case they are not safety mission-critical.”
What is genuinely new is the generative AI layer and, more practically, the cloud platform GT-PLAY that places validated simulation models in the hands of engineers across an enterprise who are not simulation specialists, but control teams, test planners, and product managers.
India at par, with One Honest Caveat
The Mahindra presentation at GTTC 2026 showed what that looks like in practice, from an Indian engineering team operating at the front of the field.
On the BE 6 and XEV 9e programmes, the company's Vehicle Performance Simulation group ran integrated thermal modelling that quantified the range impact of India's climate extremes. Aerodynamic trade-off analysis mapped the relationship between drag coefficient, vehicle weight, and range across different feature configurations before a physical prototype carried any of them.
A pan-India virtual drive exercise mapped highway, ghat, and city profiles across the country's geographic zones, accounting for gradient, AC load, and regional speed distributions. The same team framed simulation as virtual calibration at the front end, battery health monitoring and predictive maintenance post-launch, and digital twins that give engineers objective data against which to audit subjective customer feedback from the field.
Shah's assessment of where Indian OEMs stand is precise: “Indian companies are truly aspirational. They are becoming very strong contenders on a global scale. At this conference, when I compare the quality of papers being presented by our community to our European, American, and Japanese conferences, I see no difference. The user community is at par.”
The caveat he offers is the right one to leave with. “Many global OEM companies look at technologies five to ten years out, building core expertise in-house. Some companies in India could do more of that. One area where India could do more is fundamental research. Today, they do it, but largely through partners and collaborators rather than in-house. That is going to be one of the key trends in the coming years.”
There is a clear distinction between using simulation tools at a world-class level and generating the foundational knowledge that shapes what those tools do next. The 58% and 43% figures were earned by engineering teams who knew how to run the software. The companies that will define the next version of that software are the ones building that knowledge in-house, ahead of the programmes that will need it.