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The State of AI in Manufacturing

By August 09, 2021

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During this webinar, industry experts discussed the state of AI in manufacturing. We’ve included a short transcription of the webinar, beginning at 7:46 of the webinar.

Maria Araujo, Johnson & Johnson - What opportunities does AI provide when it comes to manufacturing? and with examples

Carolina Pinart, Nestle - So number one is machinery, maintenance, and quality. So for example, predictive maintenance, or anomaly detection, like detecting if our frozen pizza has three pepperoni slices versus four, that kind of thing. So that's by far the use case that's kind of mostly in production and beyond experiments. We're also leveraging, for example, digital twins and AI to, you know, that there is sometimes unplanned stoppage or there are some bottlenecks and to help us with that, and that was really useful during covid times when we had to make tensions and changes. And, and again, in R&D we're starting to leverage and, and experiment with AI to gain insights on, you know, customer transfer example, or accelerate testing, or help us with sensory when we innovate and renovate our products. So, we're starting to see a lot of AI helping us get faster to market with products that people will like more. 

Maria Araujo, Johnson & Johnson - Various areas for us to study how AI is being leveraged, you know, things like an inspection. So whether it's an inspection of products or manufacturing a variety of products. So mixing many times computer vision with machine learning to automate those inspection points. Look for defects sometimes defects that are very difficult to find whether it's in consumer products like tablets or even, you know, pharmaceuticals. So that's a huge area that we're already working in and expanding. I think there's a lot of opportunities in that space though; the defect inspection or product inspection, if you will, through the manufacturing right. Quality control, which again did the inspection part is tightly tied there with quality control, is another area where again, a lot of times a combination of computer vision and AI is a key enabler there and an area was heavily working on as well. The asset management aspect that you mentioned Carol, another great area predictive monitoring of assets, be the building assets or manufacturing assets and it's probably one of the first ones people go to. 

Sanjay Kumar, Siemens - My co-panelists have covered this fairly adequately. I guess, what the audience, what I'll add is I mean think about what AI is and if we come from the world of statistics and statistical process control and the application. So essentially everything you've shared and we've heard so far from the panelists is true. Examples around quarter quality, manufacturing operations, quality inspection. It's I think the one example that I probably could share is there's the element of what's called predictive quality. For example, you probably know Siemens is a large company. But we manufacture automation products. So what we've done in our manufacturing facilities, virtually all of them, we eventually take them through that journey for industry 4.0 for which a core part is analytics and AI. So primarily what we are seeing is while we understand the foundational elements of predictive maintenance there is also the element of predictor quality which says, I understand the manufacturing process and the dependencies, I understand the parameters in manufacturing. Where can I reduce the time?

Alana Cento, Sinequa - I'll just add to that. I think, you know, an area like, you know, APC or advanced processes control, that's looked at very closely there. In other words, there is an inspection at the end of the line, if you will. And at that point, if you catch it, and you need to catch it, but then, it definitely could be scrapped, or reworked. In either case, It's costing more money than it should as opposed to catching something in process like that. And right now in the process, not outside, and actually act and at that point where you can actually still fix the problem, if you will, without any substantial or much lower cost or no cost in some cases.

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