Industrial AI in Automotive

AI in Auto: How Manufacturers can Improve their Operations

Published on:
May 20, 2019

While the spotlight on AI in auto shines brightly on the rise of autonomous cars, for manufacturers in the industry, the opportunities for AI to transform their operations can be equally exciting.  

AI puts operational improvements in the fast lane

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 driving intelligent industrial operations in auto today.  

For example, one major Tier 1 automotive part supplier is using the Canvass AI platform 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.   

"Our customers are proof that applied AI is driving intelligent industrial operations in auto today."

Another Canvass Analytics 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  

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.   

But data overload is putting the brakes on innovation

One of the key challenges 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 in-house 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.