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Supply Chain Use Cases for AI in Manufacturing


New Report from The Manufacturers Alliance Foundation Takes Detailed Look

June 20, 2024
SCDigest Editorial Staff

What are the current and future use cases for AI in manufacturing?

Supply Chain Digest Says...

As AI evolves from individual use cases to bundles of use cases that address sets of requirements, the power of AI will expand even more rapidly.

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An excellent new report from The Manufactrers Alliance Foundation takes a detailed look at this question, including one chart that lists 75 potential use cases, under categories such as supply chain, design, maintenance, quality and more.

Here we’ll focus on the section on supply chain applications for AI.
Intelligent supplier selection is already being used to help evaluate a supplier’s pricing as well as resiliency based on current market data, the report says.

Another application is supplier relationship management. As Paul Guerrier, Advanced Technology Center Manager at Moog, told the authors, “I can see us using generative AI tools to evaluate incoming information from vendors to check that the vendor data pack is complete, contains good performance measurements, etc.”

Tim Speicher of MSA Safety envisions AI helping procurement teams “negotiate better contracts and terms with our vendors.”
When there is a sudden global shortage of a specific material or subcomponent, AI can be used for alternate material optimization, the report notes.

For example, Katrina Redmond, Executive Vice President and Chief Information Officer at Eaton, talked about partnering with Palantir Technologies to use generative AI for finding alternatives. “All of our information from our ERPs is processed by these large language models. A part shortage or a raw material shortage may be preventing us from completing the manufacture of a product. Do we have alternatives available? By having the descriptions in the system, you can sometimes match on description. If you need an 8-foot rod but only a 10-foot rod is available, we just have to cut 2 feet off to satisfy the requirement for the order.”


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Marc Rosen, Strategic Accounts, Palantir Technologies, added that “Working with Eaton, we created this AI agent that helps cross-reference parts so that if you have a $1 million sale being blocked by $50 worth of raw materials, it helps them find what else they can use in their inventory to complete the order.”

AI for raw material optimization is helping steel manufacturers determine how to use their own scrap in the most efficient way, the report says.

“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 the authors.
Looking ahead, one manufacturer is hoping that advanced machine learning and AI will help them predict outcomes of a range of end-to-end supply chain decisions to help them land on the right choices based on the metrics they prioritize.

The report also includes the chart below, showing the adoption of AI for five key supply chain applications:

"As our interviews made clear, the race to bring more AI into manufacturing is on. It is not a question of if manufacturers will lean more heavily on AI throughout their value stream but how much and how quickly,” the report concludes, adding that “The already large number of AI use cases is expanding every day as manufacturers open the aperture on how to think about the power of AI throughout the value chain. Likewise, as AI evolves from individual use cases to bundles of use cases that address sets of requirements, the power of AI will expand even more rapidly.”

Any reaction to this report on AI in manufacturing? Let us know your thoughts at the Feedback section below.








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