Intelligence + Data: A Winning Formula
February 7, 2025Comments
For some time, automotive OEMs have doubled down on efforts to optimize vehicle design, master their production processes and, of course, worked with suppliers to do the same. But what OEMs have gotten really good at is applying sensors throughout their facilities—and in their vehicles—to gather mountains of data. They use that data in their factories to tightly monitor and optimize inventory levels, cleverly manage production scheduling, and schedule predictive maintenance based on machine monitoring. Likewise, data collected from vehicles provide insights into customer behavior, helping to guide innovation efforts.
Now we see sensors of all kinds showing up in metal forming plants, for monitoring, detecting and helping to control a host of operations—material feed, part defects, tool wear and more. Companies falling behind in these areas undoubtedly will lag their technology-savvy competitors in minimizing waste and driving advancements in productivity and quality.
Likewise, manufacturers of metal forming and fabricating equipment now can collect data from their machines in use at their customers’ facilities—just as car companies gather customer data. As such, these equipment suppliers can study real-time performance metrics, understand process-optimization efforts to fine-tune variables, and gain insights into maintenance requirements.
However, this explosion in data being collected from the shop floor brings significant and critical concerns: What data should be collected and from where, and how effectively and efficiently can data be analyzed to affect timely decision making? Simply, the task has become far too complex and time-consuming for people to perform, leading to errors and ineffective decisions.
The solution has arrived with the rapid rise in the implementation of artificial intelligence (AI). As I learned during my recent conversation with Ben Saltsman, leading the AI charge at Magna as the firm’s director of data analytics, simulation and IoT:
“The most complicated but perhaps most useful application of AI on the production floor: performance. This can be challenging,” Saltsman tells me. “Can we run the equipment faster and reduce cycle times? To answer this, we need a detailed understanding of all of the potential effects. Does part quality suffer; do we wear out the equipment more quickly or require more frequent maintenance?
“These complex relationships require a lot of data, far beyond the amount of data that people typically can calculate or solve,” Saltsman adds. “AI models most certainly represent potential tools to help facility managers make educated decisions.”
You can read all about Magna’s focus on AI in my article in this issue, beginning on page 22. And, lest you think that AI only will live amongst the big players, I’m told that undoubtedly this will be driven down the supply chain. This technology promises to bring untold improvements in efficiency, flexibility, quality and productivity—all of the attributes that your customers want to see.
