EDITOR’S NOTE: This is the first of Automotive News Canada’s two-part look into artificial intelligence in the Canadian auto industry.
Scanning an employee badge at a Martinrea International Inc. plant is no longer reserved for the front gates.
Today, operators swipe into assembly equipment with their keycards, logging details about how well they have been trained, their experience on the machines and what output level they can achieve.
Sifting through the evolving stream of data using artificial intelligence (AI) helps match the right operator to the right machine, said Ganesh Iyer, chief technology officer at the Toronto-based supplier.
The process is one in a growing arsenal of AI tools aimed at improving speed and precision at automakers and parts suppliers. Adopters are likely to boost productivity, while manufacturers not making use of AI are likely to fall further behind.
Many Canadian plants are already using AI to comb through large portions of their data, said Brendan Sweeney, managing director of the Trillium Network for Advanced Manufacturing, based at Western University in London, Ont. Others are in the early stages of employing the new set of tools.
“If they’ve done it properly, if they’ve done it thoughtfully, they’re probably realizing benefits and ROI (return on investment) that those who didn’t do it are not realizing.”
SENSING A BETTER WAY
Iyer would not specify the precise productivity gains AI has delivered at Martinrea but said the set of technologies helps minimize downtime while making staff more efficient. The parts supplier uses sensors and algorithms to track staff capabilities, customize equipment maintenance schedules and flag errors on customer orders, among other applications.
Eventually, Iyer aims to connect every piece of equipment at the parts supplier’s dozens of plants to its growing AI network.
“If I was king for a day, that would be my goal, and that is what I’m working toward.”
The opportunities extend well beyond the shop floor. For automakers and parts suppliers already on the cutting edge, operations at the periphery of the core business often represent the biggest opportunity, Sweeney said.
“You’d go into some plants — maybe ... Toyota and Honda — it would be hard for them to get any more productive,” he said.
In these cases, turning AI loose on fine-tuning supply chains can result in big gains.
Polly Mitchell-Guthrie, vice-president of industry outreach and thought leadership at Ottawa-based Kinaxis Inc., said her company’s software uses AI to predict lead times for parts and carry out what is known as demand sensing.
For automakers such as Ford and Nissan, demand sensing relies on AI to look through “signals” that provide more insight than just sales history. Metrics include customer sentiment, how long vehicles spend on the lot, sales price versus the manufacturer’s suggested retail price (MSRP) and social media chatter.
“We take all of those data points, we incorporate them using artificial intelligence into a forecast, and you get a much more accurate view of what true demand actually is and a more accurate forecast,” Mitchell-Guthrie said.
With a clearer picture of demand, carmakers can hand their suppliers refined marching orders and respond faster when problems inevitably arise.
With the microchip shortage and the COVID-19 pandemic roiling supply chains, Mitchell-Guthrie said, Kinaxis has seen demand tick up from automakers and suppliers working to take more control of their material inputs.
If he can demonstrate a new AI tool offers clearly defined benefits or a definitive solution to a problem, Iyer said he has never run into cost or resource issues as a barrier to implementing the new ideas at Martinrea.
But that doesn’t mean AI systems come cheap. At many Canadian facilities, and those with tight margins or at inopportune points in the investment cycle, expense is a barrier, Sweeney said.
While he couldn’t provide cost estimates, Sweeny said “smaller companies seem to be pretty hesitant to invest the money.”