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Manufacturing Intelligence

Exploring the Spectrum of AI Use Cases

“The initial value is going to be in productivity, such as saving time. Then there are revenue driving opportunities where AI can help us – either in products or in services – and that's where the excitement comes in.”

— Tim Speicher , Senior Manager of Advanced Analytics and AI, MSA Safety

Some of the top issues for manufacturing leaders today involve artificial intelligence (AI) in their operations and how the latest AI advancements fit into their overall digital modernization strategy. The sector has been using machine learning and other forms of AI for decades, but the increasing potency of AI is dramatically changing the ways it can be integrated into production and beyond.  

Manufacturers Alliance Foundation surveyed more than 200 companies and conducted in-depth interviews with more than 40 manufacturing leaders currently engaged in new AI deployments. We uncovered countless pilot projects and a strong interest in initiating more advanced AI applications (93% have already added new initiatives in the last 12 months, and 6% plan to launch such initiatives soon).  

The key insights include how industrial enterprises are adding new use cases to their operations, which deployments are top priority, and where companies have encountered unanticipated challenges and even unexpected successes. 

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There is widespread agreement that the latest advancements in AI deliver significant and novel value to manufacturing. Companies are gearing their AI initiatives toward strategic business gains, with 55% of those surveyed prioritizing strategy, although 24% are focused on cost or efficiencies. Indeed, many see a competitive advantage in the prospect of unleashing citizen developers who can “go figure out AI” and help overcome perhaps the biggest barrier of all – imagination.

How Do Manufacturers Prioritize AI Implementation Initiatives?

 

When Will AI Impact the Bottom Line?

Progressing from a collection of use cases to a guiding strategy is a stumbling block for many companies. 16% of manufacturers expect ROI within 12 months, but the vast majority are in it for the long haul, as 42% expect to see it within 1-2 years, and 40% in 2+ years. With cost as less of an obstacle, interviewees were more enthusiastic about the potential for topline growth and competitive advantage.

Top 6 Expectations for ROI

  1. Revenue growth
  2. Improved uptime
  3. Product quality
  4. Customer satisfaction
  5. Long-term strategic impact
  6. Performance, relative to industry peers, competitors

“We don’t have to spend hours writing the code. From a product development perspective, it’s huge. We use it every day.”

— Tom Hewer , Vice President of R&D, Heavy Duty Transportation Group, Marmon Holdings

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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