Altair seeks to crunch OEM production cycles from 36 to 24 months with its latest software.
28 Jul 2024 | 4287 Views | By Amit Vijay M, Ketan Thakkar
Over the past two decades, Vishwanath Rao, Managing Director of Altair India, has been representing the global Nasdaq-listed entity's operations in South Asia and GCC countries, nurturing a strong leadership team that is driving a USD 5-6 billion global market cap on annualised sales of USD 600 million.
Altair ...
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Over the past two decades, Vishwanath Rao, Managing Director of Altair India, has been representing the global Nasdaq-listed entity's operations in South Asia and GCC countries, nurturing a strong leadership team that is driving a USD 5-6 billion global market cap on annualised sales of USD 600 million.
Altair India, which currently contributes USD 60–90 million of the global sales, is seeing sharp growth, as legacy OEMs and electric mobility firms look at an increasing need for faster adoption of technologies like AI and machine learning. Auto OEMs are looking to crunch go-to-market product cycles from 36 to 24 months, opening up opportunities for players like Altair to successfully use the latest technologies like Digital Twin to reduce their cycle time.
As an example, at Mahindra’s Research Valley (MRV) in Chennai, which handles extended product development for M&M, approximately 300 R&D engineers use Altair’s simulation tools for crash, durability, NVH, and other tests.
The simulation company counts all major two-wheeler makers in India, whether Bajaj, TVS, Royal Enfield, or the new age EV OEMs such as Ather, River, Matter Motor, or Tork Motors, among its customers. Such strong growth has seen Altair's employee strength cross over 1,000 employees, of which 750 work out of Altair's Bengaluru office and the remaining 250 are spread across Pune, Chennai, and Aurangabad. Rao, who holds a Bachelor's degree in mechanical engineering from Bangalore University and an MBA in sales and marketing from Symbiosis, Pune, recently spoke to Autocar Professional on his firm’s plans going forward. Edited excerpts:
Can you give our readers a slice of Altair's history, and how it has gained industry acceptance since it started in 1985?
Altair was born out of engineers from Ford and GM, who bought over a company called PVS Works, which made software for the US Space agency NASA. The PVS
Works' acquisition was the first step to introducing high-performance computing software (HPC) for commercial enterprise — computing workload management software for firms to manage large computing clusters. While simulation and computing have been the mainstay over the last five years, the firm has been bringing Artificial Intelligence and Machine Learning to our core simulation business. The idea is to use data to build machine learning models early in your product development process to get a lot of insights into OEM product performance and what kind of product optimisation the firm can undertake.
So can you help us understand how machine learning solves other business problems like forecasting, inventory management, and supply chain management?
Once you have the basic computing software that can decipher the complex codes written for various algorithms, you can use the same tools to start looking at not just engineering problems but also other business problems.
Another solution that the industry is quickly migrating to, is warranty analytics. What's your take on that and the other tools you offer?
Warranty analytics as a solution is nothing but how you ensure that you minimise your warranty claims for a product. The software helps OEMs understand where there are warranty issues and what kind of corrections you need to make for your product to minimise warranty claims.
Also, with electromagnetics now playing a key role, how competent are you to provide EMI and EMC tests?
Electromagnetic emissions from electronic devices can affect their functioning and that of other devices. Manufacturers must prove compliance with Electromagnetic Interference (EMI) and Electromagnetic Compatibility (EMC) testing before bringing products to market.
These tests ensure new products can function as intended alongside current devices and systems. EMI, EMC, electromagnetic interference, and communication testing are very critical, as there are so many devices communicating with each other. There should not be interference; otherwise, the effectiveness of your communication is lost.
How do you see the adoption of AI and ML in today's industry? Where do you see the improvement areas emerging?
As AI and machine learning are newer technologies, the level of adoption is also much lower, and the maturity is much less. As there is a lot more scope within AI and ML, we have created a customer innovation technology centre that uses cloud computing and data science to create digital twins that use augmented reality and virtual reality. So, when data goes from the physical world to the virtual world, the virtual model has to update itself to stay exactly in sync with the physical world. That's a digital twin.
We are trying to bring in an immersive experience where a person is actually able to go inside a car after there has been a crash and see which panel is deformed to what extent, where the failures happen, which welds have failed, and which rivets are breaking. Such insights help customers achieve a 30-40% improvement in time to market.
So your India technology centre is your platform for your customers to come and train on your latest technologies?
Every second or third day we have about 15-20 people who come and undergo training in our software. So, we plan to take all of these engineers to the Technology Innovation Centre to have them experience all of these technologies will be in play in the future, enabling firms to use a minimum amount of material to create the best possible design.
But this space too is getting crowded, as we are seeing legacy players and new-age start-ups also competing for the same client work. How are you able to differentiate?
We are competing with hundreds of players in machine learning and data science models, from Google to Amazon to Microsoft to a small startup in Bengaluru, with three or four people.
So, our differentiation comes in the areas in which we operate, which are engineering and product development, where there is hardly anyone who comes close to what we can do.
Moving forward, what will the critical pillars of growth for the future be? What is the kind of growth you anticipate, given the fact that you started something like this?
The last few years have been phenomenal, as we grew upwards of almost 25% year-on-year for the last three years in the India business to be specific, and we anticipate similar growth going forward. With this kind of growth, we are looking to double our India volume in the next three to four years, and that's the target that we are gunning for.
Machine learning, data science, and digital twin is the second area where we see a tremendous amount of opportunity for us. Our other businesses like structural simulation, will continue to grow at a good pace.
What are your other priorities?
Expansion into multiple areas is the number one priority. The second priority would be that we will be scaling up more in terms of adding more manpower to our AI-ML portfolio from 40 engineers in India. We want to double that team in 2024.
This feature was first published in Autocar Professional's July 15, 2024 issue.