Vishwanath Rao, MD, Altair India, has been associated with the organisation since October 1999. He joined the company as a sales engineer for the West India region. Rao rose through the ranks to become a Director at Altair India in January 2015. Rao has been the MD and Country Manager for Altair India and Gulf Cooperation Council countries since January 2019.
What does this Memorandum of Understanding with IIT Delhi Incubation park mean for Altair?
We have been working with various incubation centres across the country. Altair already has signed MoUs with several incubation centres in Bangalore, Hyderabad, and Chennai. We are also beginning to see many tech startups coming up in the NCR region.
It is clear that many cutting-edge technology innovations are happening at the startups. So we are not looking at revenue; we are looking at how we can contribute to the startup ecosystem by creating breakthrough innovations. And if those innovations are using our technology, it adds much value for us in showcasing this to the rest of the world.
Your focus is on automotive engineering, among other verticals. What innovation do you see in the automotive world?
The definition of a vehicle has changed dramatically from what used to be a traditional mechanical product. Most vehicles today are a combination of electronic and software devices on wheels. That's where a lot of cutting-edge innovation is going to happen.
The traditional mechanical stuff has reached a certain level of maturity. Innovation is happening in newer areas like autonomous driving, parallel verification, and connector technology. The use of data, the use of machine learning, use of AI are some aspects that are going to increase.
What are the three most critical points for auto OEMs from a technical point of view?
Light weighting is extremely important as making a vehicle lightweight without compromising performance will help achieve a higher range. If we can reduce the weight and increase the range, we automatically answer the anxiety problem.
Second in line is incorporating more and more aerodynamic elements in automotive designs. The same will make the wind resistance minimal and again help in increasing range. Look at any survey, and we can find range anxiety as the biggest hurdle in electric vehicle adoption. Light weighting and aerodynamics will also help petrol and diesel-powered vehicles achieve more mileage.
Safety should be the priority on the list. OEMs and battery manufacturers should invest more in ensuring batteries are safe in every situation, whether a short circuit, an increase in temperature or an accident. They should also focus on how to put the data they are collecting to better use and create better products.
What's the next chapter for automotive OEMs in AI, ML, and simulation?
Simulation, historically, was more of a forensic tool. It was used to figure out what caused a failure. Then it became a verification and validation tool. However, simulation has now become a design tool. OEMs are now infusing simulation to start with a mathematically correct design. It is helping OEMs reduce iterations between design and simulation.
AI and ML have wide applications. These can be applied from the concept stage to the after-sales service stage. AI and ML can help drive designs based on data and simulation.
The other use case of these three technologies coming together is digital twins. This technology can help OEMs and component makers design and manufacture better products without having to test them in the real world. For example - data collected from sensors of a car that has been on the road for five years can be used to create a digital twin of the same.
Simulations can then be run with AI and ML deployed to help create better components. These stages can help an automaker create better products.
In which stages of an automobile design can these technologies be deployed?
The simulation should be deployed in the product development phase. It has to be done before prototyping. AI and ML can be used across the entire duration of the product life cycle.
Can AI and ML help better the supply chains of the automotive industry?
Yes, absolutely! AI, ML, and even simulation can help improve supply chains. For example, we can use data to calculate how much we will be manufacturing and then the same data can help the suppliers understand what kind and quantity of raw materials and components they will need to supply.
All of this can be done in advance, say four to six months or more. So simulation data can help a supplier, component maker, or designer create better components in advance. They can then proactively reach out to OEMs about the refined quality of components.
There's a dearth of data in India. So how do you solve that problem?
A lot of the data can be created and sourced from the testing stage itself. A good number of test vehicles are running in India before the final version is launched. These test runs, from Kashmir to Kanyakumari, generate much data.
Whether high-end or mid or low-end cars, all these have transmitters. A lot of these also have mobile apps. All this can be used to collect and analyse data and create better products.
Wouldn’t be investing in AI, ML, and Simulation-based tools increase the CAPEX of the automotive industry?
I think the benefits outweigh the cost. Sensors are not very expensive these days, so it's one less worry for the industry. Then the OEMs can offer smart features to their consumers as well.
Then there is cost saving for OEMs. For example - assume the frame or chassis is 10kg, and our tools help an OEM reduce it to 8kgs; they are saving 2kg of raw material in terms of production volume and millions of vehicles. So we're talking about lakh of kilos of weight reduced.