In a TechTarget article, As AI evolves, manufacturing will face an old data problem, Emerson chief technology officer, Peter Zornio, joins other executive leaders to discuss this challenge. The article’s author opens by highlighting the promise of AI in manufacturing.
AI has long been an integral technology in manufacturing operations, but technological advances are opening new possibilities for improving efficiency, safety and productivity.
Peter explained how AI is not a new development in manufacturing.
Manufacturing, particularly in process industries, has used digital systems to read and analyze sensor data and software to take corrective actions since the 1970s, Zornio said. This has continually evolved to the current machine learning and generative AI technologies that are coming on now.
“[Manufacturing was doing] AI before AI was cool,” he said. “From the mid-1980s through today, we’ve been constantly applying these numerical mathematical AI techniques to provide ever better control and optimization of what’s happening in the manufacturing process.”
Manufacturing has always had the data, much of it coming from sensors that capture manufacturing processes as they operate, but the evolution with AI is building the models and improving the analytics, Zornio said.
Even so, good data is essential for AI applications in manufacturing, he said.
“You can have a lot of raw data, but if that data is not in the right format or the right context, you can’t interrelate the data from this set of applications with that set of applications,” Zornio said. “You can’t run the optimization and build the model across those multiple data sets, especially when you’re trying to span multiple functions inside of a plant.”
There are several promising use cases for generative AI in manufacturing, he said. For example, a company can use the technology to design automation systems specifically for each of its facilities.
“GenAI can configure the system automatically by feeding the system drawings of the physical equipment and how they’re interconnected, and coming out with an initial configuration of the control system,” Zornio said. “That’s engineering work that GenAI is able to take over for us.”
Generative AI can also be used as a “super assistant” for products by ingesting all the documentation, support interactions, logbooks and application notes to create a product chatbot that has vast knowledge and can respond better when you ask how the product works, he said.
“Everybody who makes a product is looking to build like a super product assistant like that,” Zornio said.
We’ve shared many examples here on the blog of how AI has been incorporated into automation-related products to help drive improvements across the lifecycle of a manufacturer’s operation from up front design and engineering through ongoing maintenance and operations.
