Data is the key to manufacturing success in an EV Future
In their August 21 issue, Automotive News Canada ran an Expert Spotlight interview with Acerta CEO, Greta Cutulenco. Greta shared her insights on the role of data in manufacturing electric vehicles. Here are some highlights from that interview.
1. The automotive industry, and the automobile itself, is becoming more and more about data. Does the role of data in EV manufacturing differ than in non-EV manufacturing?
Data is important whether you’re building parts for internal combustion vehicles, off-highway vehicles, or electric vehicles. With EV part production specifically, there is a need to use manufacturing data to accelerate speed to market. Data can help EV component manufacturers reduce test times, innovate faster during new product introduction and ramp phases, understand how new part designs will impact production, and introduce new products to market as quickly as possible.
A high level of precision is required when building electric vehicles. For example, noise and weight reduction are both big problems for EV manufacturing which means that manufacturers need new ways to innovate during production. More data will help them deal with problems quicker, iterate on designs, launch new designs, and drive productivity. Manufacturers have had a hundred years to get to the point of stable, predictable production of combustion vehicle parts. If we want to be at full EV production in, let’s say ten years, we need faster innovation which means manufacturers need data and they need AI.
2. Data in automotive can be used for both predictive maintenance and predictive quality but they are quite different. What are the benefits of predictive quality vs predictive maintenance?
More people are familiar with predictive maintenance because it has been popular in manufacturing for some time. Predictive maintenance comes into play when manufacturers monitor activity on their equipment and use the data to predict when machines will wear down or fail so they can perform maintenance ahead of time. Focusing on machine uptime and overall equipment efficiency is important but it doesn’t help manufacturers with the core lifeblood of their business – the parts they produce. There is tremendous cost to poor quality, from waste of materials and labour to brand damage and customer churn. Investment into predictive quality can have a significant impact on the bottom line of any plant, more so than an investment in predictive maintenance alone.
Through advanced analytics made possible with machine learning and artificial intelligence, predictive quality enables us to identify anomalies in manufacturing – in real time so we can help a manufacturer understand when defects will likely occur or determine what is most likely contributing to a quality issue. We help them identify the root causes of defects and address potential quality issues proactively, not after a problem has already occurred or when a faulty part shows up at an end-of-line test station. By focusing on predictive quality, the engineering team can make adjustments and implement solutions before potential problems ever impact production.
3. Why is predictive quality extra important with the manufacturing of EVs?
Again, in today’s race to EV adoption, speed to market is everything – so if an EV part producer can optimize production and ensure high quality and first time through rates without rework, it puts them in a better position to beat their competition.
Because manufacturers are investing in new EV lines and plants, now is the perfect opportunity to introduce advanced digital technology. It’s much easier to do things right from the start, rather than trying to revamp lines later. For example, choosing an MES and equipment that makes it easy to share and export data will mean a manufacturer can gain value from predictive quality much faster which will enable speed, productivity, and the ability to ramp up and scale production.
4. In the highly competitive world of automotive, the manufacturing process is closely guarded. In broad terms, are you able to share an example of Acerta’s AI platform predicting or detecting a malfunction, and the end result?
We can’t share specific details about most of our work with OEMs like Nissan and Tier 1s like Dana, BorgWarner, and Linamar, but you can view our case studies to learn how we’ve optimized production in different automotive manufacturing plants that were facing different quality issues.
I can tell you a bit about our latest innovation which is in alternative fuels. We’re working with Ballard Power Systems out West on a project that’s supported by NGen AI for Manufacturing funding. Our new technology will help Ballard shorten their factory acceptance testing times and identify the sources of test failures in their fuel cell stack assemblies.
This will be a multi-year project and we’ve already completed the initial proof of concept with great results: tests that previously took 2.5 hours were running in under 30 minutes. Ballard is excited to move forward because our new approach will not only reduce their factory acceptance test times by up to 80% like we saw in the POC, but it will also enable them to deliver industry-leading quality to their customers at much higher speed.
5. Many technology companies from other disciplines are trying to cross into the EV space. Acerta has been solely focused on the auto sector – how does that differentiate you against newcomers who are trying to get into EV?
EV manufacturing is an attractive market sector for technology companies that customarily focus on other verticals, especially so for companies typically serving electronics manufacturers since the number of electronics required in an electric vehicle is growing significantly. However, we have a couple of big advantages over them.
Advantage number one: we have a proven track record of successful and scalable implementations within the automotive manufacturing environment, and our deep experience with the unique needs and realities of these facilities positions us well for a transition into EV component manufacturing – far more so than newcomers who are trying to enter the EV market from other industries outside of automotive. We’ve built a solid foundation upon which we can dive into fuel cell, battery, and electronics production for EVs.
Advantage number two: our focus on predictive quality is unique in how we operationalize the use of machine learning and AI in high-volume production, and it is scalable globally. This is key in the automotive industry and will play a huge part in the growing EV transition. It’s also something that other newcomers will struggle with.
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