Discover What Other Manufacturers Are Doing
Manufacturers are moving quickly to add AI throughout the value stream, from supply chain management to warehousing and inventory and even beyond to aftermarket and customer services. In fact, 99% of those surveyed are working on AI projects to improve generative design, catching errors before the manufacturing process begins, and nearly all manufacturers are using AI or plan to implement programs to manage asset health and remote diagnostics. There are already hundreds of use cases within manufacturing that have moved beyond proof-of-concept and are delivering value. Now, it’s a race for successful adaptation. Discovering new and more sophisticated use cases creates an untapped opportunity for manufacturers to make some long-term gains. Here's just a few examples we discussed with manufacturers.
AI for raw material optimization is helping steel manufacturers determine how to use their own scrap in the most efficient way. “As a steel manufacturer, we always have a mix of scrap material on hand as well as the possibility of what that scrap could turn into. So, we’re using AI to help us figure out the probability of producing the product that we want out of this mix. It will help us streamline both the ordering process on the raw material end and the manufacturing process on the melt end,” Jared Noble, Director of Digital Technology for Charter Manufacturing, told us.
Southco sees potential future applications of AI during the advanced product quality planning (APQP) phase. Deep learning AI algorithms can assist with designing for quality by helping the engineer select the right materials. Shailesh Patel, Director of Quality at Southco, described the situation: “If an engineer tries to design a product in a certain material, AI tells them ‘Don’t use that material.’ This is based on past data. We have had a ton of issues with some specific problem materials, so this is very important for us.”
Manufacturers are also evolving from using AI with individual machines or lines to large-scale production planning optimization. Katrina Redmond, Executive Vice President and Chief Information Officer at Eaton, talked about the challenges and the solution. “We have 30,000+ different products. We always need to know how, when, and in what bundles they are selling. AI has given us better clarity across all of our different ERP environments to know which items we are clear to build. For example, it tells us if we have all the product available in the quantities necessary to fill order X, Y, or Z. So, AI is really helping the planners and the production floor teams figure out which products they can complete before getting to step one of the manufacturing or assembly process. Essentially, AI is telling us, ‘Don’t bother with this order yet because you’re going to be missing a piece at step four.’ That allows us to go to the next item where we have all parts and material available.”
When it comes to logistics, AI is playing new roles as well. John Deere, for example, is looking at AI solutions to become more efficient with its delivery trucks. Using generative AI for dynamic load matching may be the answer. As Wallas Wiggins, Vice President Global Supply Management and Logistics at John Deere explained, “We would like to have a truck out there to deliver and pick up multiple times before it circles back home. I see that as one of the next big fronts for us because we spend a lot of money on logistics. We also have sustainability goals. I want to make sure every time a truck rolls, it’s meaningful rolling, not empty rolling.”
Read more in the full report to see how manufacturers are embracing cutting-edge technology to increase productivity, gain forecasting and predictive capabilities, and harness data for better design.
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