On the far other end of the data set size from Fluke is Amatrium, Inc., a solutions provider that uses machine learning, a subset of AI, to help small and medium-sized manufacturers eliminate waste with its custom tools such as Amatrium Process, a quality control tool that aims for scrap reduction, and Amatrium Predict, which can foresee the properties of a metal alloy based on its component materials, saving time and expense in the development process. And Amatrium does its work with little input data.
“About 500 to the low thousands of lines of data is what we typically see,” said Andrew Halonen in technical sales and marketing for Amatrium. “Material results are so equipment-related and raw materials-related, it’s imperative that we use the customer’s data as opposed to random data from other sources. The beauty of ML is that there’s no bias. Why not leave it up to the tool to tell you where the biggest impact is? You want to drive the highest profitability. Scrap is money. If you can identify what’s driving scrap, that’s a big deal.” In its work with one global foundry, for example, Amatrium was able to drive a 10% scrap reduction.
Again, even if you haven’t begun with AI yet, you’re not too far behind. But that’s going to change fast. “In 10 years, it will be standard practice,” Halonen said. “Today, only the early adopters are taking advantage of it.”
“How do we democratize the technology?” asked Malhotra. “It provides a level playing field. A customer can start with 25 assets, the biggest pain points, and see results almost immediately.”
“I couldn’t think of a more fun time to be in this industry,” said Terwiesch. “There’s tremendous excitement around the opportunities and solutions.”