Autonomous and electric cars were front and center at this year’s North American International Auto Show (NAIAS). But when looking under the hood at what creates the magic of these connected cars from the controls in the cars to the analytics – it’s Artificial Intelligence (AI) that is doing the heavy lifting. With a connected car running on the computing power equivalent to 20 computers, featuring 100 million lines of programming code, and processing up to 25 gigabytes of data an hour, the endless amount of data that is produced is now supporting accurate insurance premiums, improved diagnostics and smart maintenance services. The key benefactors today, however, are those in post-production.
From our vantage point, Original Equipment Manufacturers (OEMs) and auto part manufacturers are dipping their toe into the AI foray but without a coordinated, sustained effort they are yet to gain significant improvements to their operational performance.
Exciting advancements in AI for the automotive industry were on display at Automobili-D 2019
Time to put the pedal to the metal
One of the key challenges that has been holding auto manufacturers back from improving their manufacturing operations is the ability to connect the dots between the screeds of data that is generated on the operational floor, the variables that impact it along the process to the yield outcome. We’ve found that inhouse teams either shy away from projects because they don’t know where to start or those who are brave enough found that it has taken anywhere from six to 24 months to analyze the data to derive any insights. By then – the ROI on the project has greatly diminished and the results rarely implemented. What was typical of their approach was that the heavy lifting was done manually.
Artificial intelligence-powered industrial analytics can now remove these two major sticking points because it eliminates the manual processing of data so that operational teams can focus on deriving insights and making data-driven decisions that positively impact performance.
Time to put the pedal to the metal - Canvass representative, Ryan Parmentier, explains how the Canvass platform can accelerate your production
How AI is driving intelligent industrial operations in Auto
The applications of AI are endless from predictive maintenance, yield optimization, energy reduction to waste minimization – all contributing to better quality products and better margins.
Our customers are proof that applied AI is here to stay.
For one major Tier 1 automotive part supplier, the Canvass AI Platform is being used in their operations to predict part quality within their welding process. By applying Canvass AI to predict defects, the Canvass platform is not only helping to significantly reduce time to inspect, but also ensuring that fewer defective welds leave the plants and in turn dramatically reducing the repair/replacement costs incurred.
Another Canvass customer went even further and applied Canvass AI to improve both the quality of their welds as well as reduce asset downtime. By optimizing the many parameters that influence weld quality, this customer reported improvements in their operations such as:
- Increase in consistent, First Time Quality of welds
- Reduction in waste and rework required
- Reduction in asset failure
- Increase in throughput
AI is a journey, not a destination
These examples are just the tip of the iceberg when it comes to the huge potential for auto part manufacturers and OEMs to apply AI into their operations and begin the journey towards intelligent industrial operations.
It’s important to note that AI is not an endpoint, but a journey.
And like any journey, you will not see any results unless you commit to starting. If you are ready to fast track your competitive advantage and start realizing the benefits of AI in your operations, contact us today. Don’t wait, because your competitors have most likely already started.