38: AI's Role in Reducing Scrap and Maximizing Manufacturing Profit

a BROADcast for Manufacturers

Read the article here: When will AI usher in a new era of manufacturing?

Lori: Not even getting into the AI component of this, but I was fascinated that this manufacturing process to create diapers. There's 40 separate loose streams to assemble per diaper.

Erin: Just that assembly line, and I'm sure there's a ton of automation within that already. And they're producing 1200 diapers a minute? In 140 different manufacturing lines globally? So when you're talking about data exactly, the amount of of data that they've captured. Exactly. And that's the other thing. They've invested in technology to be able to capture the data. That's step one, so they're not making assumptions. But the other thing that I found fascinating… As they did necessarily just take the raw data and throw it into AI, they actually created different simulated situations where there was an issue that occurred to identify it and try to be a little bit more proactive on how to minimize the downtime of their their machines.

Erin: If there's some way that we could operationalize the dissemination of learning so that the smaller folks have a chance to really exploit the learning that we're getting from AI. I think that'd be awesome.

Lori: Yeah. I mean, I agree with that a hundred percent, and that would be the end goal. To some extent the R and D comes in at the cost of the larger companies and then finds efficiencies that can carry down to the smaller companies.

The article also had a case, which was Siemens, where the opportunity to support those smaller manufacturers as Siemens is actually using the products that they're selling that has AI built within their products. I thought that was kind of cool that they were, they didn't really specify exactly what it was that they were developing or building. But it's basically learning from the production lines, and then the machines themselves will be able to modify that the way that they're producing the equipment. To minimize mistakes and maximize production. So I think that's super cool, but also kind of like scary at the same time.

Lori: Everyone's got their own playgrounds or shared tools being used in the AI space. But a lot of organizations are just creating their own AI. So it's interesting.

Erin: Yeah, it is. And I think that's one of the discussed implications of AI. Iit goes right back to the manufacturing model in the early phases of AI when it was funded as a public good. So the artificial intelligence systems were being built as research for the sake of knowledge, and that information was shared. It was public information in that era, and this is sort of unknown to us. I think largely that era has closed, and so now the market driven development is really the phase that we're in. But we closed the public good development era before, I think, we really reached for the stars with what we can do for the good of humanity and what we could do for the good of all economic drivers.

And so much more…

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