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Why Industrial AI Still Struggles in Supply Chains · Speaking of Supply Chain

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  Speaker 1: You're listening to Speaking of Supply Chain, a meeboch podcast.
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  Speaker 1: This is a show for logistics professionals looking to learn
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  Speaker 1: more about the latest innovations in supply chain. Each episode
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  Speaker 1: will feature a conversation on topics such as mitigating supply
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  Speaker 1: chain disruption and reducing risk, current automation trends, sustainability initiatives,
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  Speaker 1: and more. Let's dive right in.
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  Speaker 2: Hello and welcome to Speaking of Supply Chain, where we
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  Speaker 2: explore trends, current events, and innovations impacting the logistics and
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  Speaker 2: supply chain industries. I'm your host, Ellen Wood. AI is
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  Speaker 2: rapidly transforming manufacturing by enhancing efficiency, reducing costs, and addressing
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  Speaker 2: labor shortages. Recent stats indicate over eighty percent of businesses
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  Speaker 2: have adopted AI to some extent, with thirty five percent
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  Speaker 2: implementing it across multiple departments. But one area that's still
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  Speaker 2: working to catch up is in dust operations. From predictive
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  Speaker 2: maintenance to automating repetitive tasks, the industrial manufacturing sector is
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  Speaker 2: rapidly finding ways to incorporate these advanced capabilities. Joining me
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  Speaker 2: today to dig into some of the details is Brian boy,
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  Speaker 2: director of Industrial AI at Rovisus, welcome Brian.
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  Speaker 3: Hi, thanks for having me.
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  Speaker 2: Great to have you. So before we get into our
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  Speaker 2: discussion today about AI, let's take a moment just to
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  Speaker 2: get to know each other a little bit better. This
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  Speaker 2: is a feature that a lot of our listeners are
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  Speaker 2: probably not used to. It used to only be available
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  Speaker 2: in our video content, but now that video is available
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  Speaker 2: on Spotify, we're putting it into the full program. So
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  Speaker 2: our icebreaker question for today, I've got one for you,
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  Speaker 2: and then you've got a different one for me. So
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  Speaker 2: mine for you. What's a weird fact about you?
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  Speaker 4: So I would say the I live a pretty normal
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  Speaker 4: suburban life. But I would say the one weird fact
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  Speaker 4: is that my wife and I are kind of roller
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  Speaker 4: coaster freaks, and so we both have each ridden I
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  Speaker 4: would say at least over one hundred rides on roller coasters,
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  Speaker 4: probably one hundred and fifty at this.
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  Speaker 3: Point when we met.
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  Speaker 4: We met at eighteen in college and both kind of
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  Speaker 4: bonded over roller coasters. We live in Ohio and so
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  Speaker 4: we've got Cedar Point is up the street from us,
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  Speaker 4: so that's you know, that's kind of why. And then
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  Speaker 4: when we raised our kids, we kind of raised them
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  Speaker 4: to be roller coaster freaks, so they ride with us now,
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  Speaker 4: so all five of us jump on these coasters.
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  Speaker 3: But yeah, we love them. We love them big and
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  Speaker 3: fast and scary.
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  Speaker 2: So my kids also love roller coasters, and my husband
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  Speaker 2: is a roller coaster freak. We've been to Cedar Point
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  Speaker 2: multiple times. There's one roller coaster there. I forget which
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  Speaker 2: one it is. It's the ginormous one that's so tall
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  Speaker 2: that you can see Canada on a clear day.
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  Speaker 3: Yeah, that's money and force.
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  Speaker 2: The millennium force. So I remember, I love riding that
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  Speaker 2: roller coaster. But I read something recently about it that
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  Speaker 2: was really interesting, and that is that it is one
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  Speaker 2: of the few roller coasters in the world that can
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  Speaker 2: help break up a person's kidney stones. Totally random. If
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  Speaker 2: you have kidney stones, you can ride that roller coaster
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  Speaker 2: and it will help break them up so that you
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  Speaker 2: can pass them.
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  Speaker 3: I don't know.
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  Speaker 4: I don't know how accurate that is, but uh, I
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  Speaker 4: guess as I age, that's good to know.
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  Speaker 3: If it's true.
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  Speaker 2: That speaks to the force of that roller coaster.
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  Speaker 4: It is the first hill. It's wild, it's wild.
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  Speaker 2: Yeah, yeah, yeah. I was talking about roller coasters just
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  Speaker 2: this past weekend with my son. We were driving through
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  Speaker 2: Louisville and looking over and seeing when Kentucky Kingdom was
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  Speaker 2: going to open, because it's just off I sixty five
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  Speaker 2: in Louisville. You can see it right by the fairgrounds.
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  Speaker 2: And he's he's very excited. He's like, when is it
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  Speaker 2: going to open? Can we go find roller coasters?
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  Speaker 3: Yep?
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  Speaker 4: Yeah, I can't wait. We got our season passes. I
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  Speaker 4: can't wait for see your point open.
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  Speaker 3: We'll be there.
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  Speaker 2: So all right, that sounds like a lot of fun
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  Speaker 2: for the summer. So you had a different question for
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  Speaker 2: me to get to know me a little bit better
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  Speaker 2: for our listeners.
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  Speaker 3: And what was that I did? So, Ellen, I like
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  Speaker 3: to cook a lot.
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  Speaker 4: I do all the cooking in our house, so I'm
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  Speaker 4: always looking for new ideas, new inspiration.
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  Speaker 3: What's your favorite dish to cook?
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  Speaker 2: Oh? My favorite or the one I cook the most often,
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  Speaker 2: because that's a different thing all together.
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  Speaker 3: I would say, what's your Yeah, what's your favorite?
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  Speaker 2: My favorite? So I love making soup, and yeah, I
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  Speaker 2: make soup all the time. And I've I've worked this
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  Speaker 2: past winter and fall at perfecting my bone broth. Okay,
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  Speaker 2: and I've been making multiple batches of it and I
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  Speaker 2: make bone broth at least once a week, and I
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  Speaker 2: freeze it. I give it away to friends. But I
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  Speaker 2: just I've wanted to find like the best bone broth,
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  Speaker 2: and I like beef bone broth, and I have I
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  Speaker 2: have a grocery store near us. It's an international grocery
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  Speaker 2: and it has a lot of different ethnic that most
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  Speaker 2: grocery stores do not have. And this store has all
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  Speaker 2: the parts that you need for making good broth that
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  Speaker 2: most grocery stores aren't going to have prepackaged nicely for
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  Speaker 2: someone to go consume. And one of the biggest additions
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  Speaker 2: to my chicken broth is the feet, oh chicken.
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  Speaker 3: Okay, okay.
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  Speaker 2: You get so much more collagen out of chicken feet,
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  Speaker 2: and it's not something you will ever find at a
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  Speaker 2: regular grocery store. But if you can get your hands
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  Speaker 2: on them from a butcher and add them into your
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  Speaker 2: your bones and your your vegetables and your herbals that
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  Speaker 2: you put in there, that the chicken feet make all
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  Speaker 2: the difference in a very very collagen rich broth, which
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  Speaker 2: is what you want.
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  Speaker 3: Yeah, well, that's a good tip. I like that.
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  Speaker 2: It makes my kids laugh every time I make it
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  Speaker 2: because there's these little chicken feet stickn open.
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  Speaker 3: Well, you know, as long as it tastes good in the.
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  Speaker 2: End, exactly, it hasn't been them from eating it. So
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  Speaker 2: all right, Well, let's get on to our topic today,
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  Speaker 2: which is not chicken feed or roller coasters. It is
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  Speaker 2: AI in industrial manufacturing. And this is something that you
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  Speaker 2: have a passion for, So tell us a little bit
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  Speaker 2: about it.
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  Speaker 4: Yeah, So, well, I mean to get there, I kind
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  Speaker 4: of have to start back at my journey here, my
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  Speaker 4: career at Robusis. So I work for a company. So
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  Speaker 4: we're a system integrator and we target manufacturing and industrial customers.
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  Speaker 4: So I started here actually right out of college. I
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  Speaker 4: co opt here and then I started full time. So
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  Speaker 4: I've actually spent twenty five years here at Robusis has
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  Speaker 4: been a health professional career, which you don't see as
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  Speaker 4: often any more, folks spending kind of their whole career
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  Speaker 4: at a single company. And I came in with a
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  Speaker 4: software background, and so at the beginning of my career,
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  Speaker 4: we were writing a lot of custom software and things
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  Speaker 4: like that for customers. Now, things that progress, there's a
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  Speaker 4: lot of commercial off the shelf software that you can buy,
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  Speaker 4: so so in some ways we've kind of advanced and
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  Speaker 4: now so we're focused on AI for those same.
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  Speaker 3: Industrial and manufacturing customers.
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  Speaker 4: I'm very passionate about it because I you know, I've
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  Speaker 4: spent I really came into this industry knowing nothing about manufacturing.
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  Speaker 3: When I started twenty five years ago, I.
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  Speaker 4: Knew I liked the show How It's Made, so that
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  Speaker 4: was kind of cool, like to seeing how stuff was made.
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  Speaker 4: And so now I've spent the last two and a
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  Speaker 4: half decades working in this industry and really have fallen
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  Speaker 4: in love with manufacturing. I do feel like manufacturing is
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  Speaker 4: the biggest lever that we can pull as a civilization
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  Speaker 4: to create the most prosperity for the most people. I
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  Speaker 4: think it's that important. I think it is the wealth
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  Speaker 4: generation mechanism for for civilization, for societies, certainly for economies,
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  Speaker 4: and so I'm.
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  Speaker 3: Very passionate about it. I'm passionate about what we do.
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  Speaker 4: And manufacturers in general are not known for being real
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  Speaker 4: cutting edge right there for a lot of good reasons.
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  Speaker 4: They're very risk averse, and so they're typically five to
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  Speaker 4: seven years behind more of the IT world and in
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  Speaker 4: the rest of the world other industries, and so it's
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  Speaker 4: been you know, a longer process to get them to
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  Speaker 4: adopt AI. But all of I mean almost all of
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  Speaker 4: my customers now are taking some kind of steps towards that.
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  Speaker 4: Even if they haven't gone full born AI yet, they've
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  Speaker 4: got a data science team where they're at least starting
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  Speaker 4: to build out some of the infrastructure, do some of
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  Speaker 4: the infrastructure upgrades that are required to get to an
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  Speaker 4: AI future?
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  Speaker 2: All right, So once that initial AI project gets off
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  Speaker 2: the ground, how can organizations effectively scale that up and
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  Speaker 2: make it into those bigger projects that don't necessarily overwhelm
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  Speaker 2: the team As you said, they're they're risk averse. They're not.
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  Speaker 2: This isn't their wheelhouse, this isn't where they look for
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  Speaker 2: the next thing, and this is this is very new.
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  Speaker 2: So how do we do it without overwhelming those teams
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  Speaker 2: or risking the quality of that implementation and try to
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  Speaker 2: do something We certainly don't want to do something and
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  Speaker 2: fail because then you know, it's it's going to make
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  Speaker 2: the team risk averse to trying something new again.
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  Speaker 3: Right, Yeah for sure?
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  Speaker 4: And I know it sounds self serving, but the answer
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  Speaker 4: really is to hire a system integrator. So that's that's
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  Speaker 4: really our role is to in cases where the customer
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  Speaker 4: doesn't have any internal capability like that, any internal team.
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  Speaker 4: We're there to bring in the experts and to be
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  Speaker 4: the horsepower behind those types of projects. But when we
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  Speaker 4: also have plenty of customers that have existing you know,
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  Speaker 4: data science or AI teams and then we come in
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  Speaker 4: to scale their capabilities out. So you know, it's like, look,
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  Speaker 4: we want to you know, we've heard this where we've
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  Speaker 4: got some models built and we think we were onto
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  Speaker 4: something here, but we want to roll this out to
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  Speaker 4: five sites. So we've got a three year plan to
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  Speaker 4: roll us out to five sites. I'm like, wait, I
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  Speaker 4: can scale horizontally, I can bring in bodies and we
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  Speaker 4: can do we can roll it out to five sites
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  Speaker 4: in parallel. And so that suddenly is is an eye
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  Speaker 4: opener there when they realize we.
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  Speaker 3: Could shrink this schedule a heck.
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  Speaker 4: Of a lot more So, like I said, I know
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  Speaker 4: it's sound self serving, but that really is the role
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  Speaker 4: that system integrators play in these types of things is
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  Speaker 4: to augment that capability.
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  Speaker 3: I will say.
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  Speaker 4: Longer term though, one of our key tenants at robsis
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  Speaker 4: is that we believe that customers should own the solution.
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  Speaker 3: When we're done. So we spend a lot of time
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  Speaker 3: on training.
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  Speaker 4: You know, we tell customers you're allowed to look over
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  Speaker 4: our shoulder the entire project if you'd like. We want
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  Speaker 4: to make sure that you feel comfortable owning this system
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  Speaker 4: when when we're done, because I mean, it is your
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  Speaker 4: system ultimately, and that's that you know. So we you know,
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  Speaker 4: we know that they're going to call us when they
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  Speaker 4: have the next big project or whatever. But we don't
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  Speaker 4: want to lock them in into a situation where, you know,
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  Speaker 4: every time they have to retrain the model or add
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  Speaker 4: a little you know, add some tags to it or something,
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  Speaker 4: we don't have to be able to force to call
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  Speaker 4: us for that kind of thing.
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  Speaker 2: Sure, so as it becomes more embedded in these workforces,
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  Speaker 2: as you're handing off the capabilities and the tools to
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  Speaker 2: do it, what new skills should companies be proactively you know,
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  Speaker 2: teaching their employees and getting people to a comfort level
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  Speaker 2: with this type of technology, and how can they best
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  Speaker 2: approach the training for those things? Because I know we've
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  Speaker 2: had a number of trainings in our organization on AI.
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  Speaker 2: We had a workshop just yesterday trying to you know,
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  Speaker 2: workshop and get some new ideas, some fresh ideas that
• 11:30
  Speaker 2: aren't just what's your chat GPT prompt? I mean, that's
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  Speaker 2: that's the one I use the most. But how do
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  Speaker 2: we train this next generation of workers to function with
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  Speaker 2: the AI and not fear it.
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  Speaker 4: Yeah, so there's a couple of different answers there, because
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  Speaker 4: it really does depend on what your role is in
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  Speaker 4: the organization. So if you're somebody who actually feasibly you're
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  Speaker 4: an analyst or an engineer, and you feasibly would be
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  Speaker 4: maybe interfacing to these models or maybe even building these models,
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  Speaker 4: you're going to need some kind of data science background. Now,
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  Speaker 4: the good news is that there are free resources. There
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  Speaker 4: are in resources available, so there's lots of ways to
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  Speaker 4: get some of that data science background. I will say though,
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  Speaker 4: that that doesn't seem to be as much of a
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  Speaker 4: problem for our customers where they're seeing the bigger problem
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  Speaker 4: is in the loss of expertise. So we are seeing
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  Speaker 4: some people are calling it a silver tsunami, but we
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  Speaker 4: are seeing a loss of primarily baby boomers post COVID
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  Speaker 4: who are leaving the manufacturing workforce, and what we are
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  Speaker 4: not seeing is a corresponding increase in younger folks entering
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  Speaker 4: the manufacturing workforce. So just to give you some SPAT stats.
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  Speaker 4: This came from LNS Research in twenty nineteen. The average
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  Speaker 4: tenure in the this is for US. The average tenure
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  Speaker 4: for the manufacturing workforce average years at a company is
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  Speaker 4: twenty years. The average tenure, So that was twenty nineteen
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  Speaker 4: and twenty twenty three, that average tenure fell to three years. Yeah,
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  Speaker 4: the average time and position fell from seven years to
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  Speaker 4: nine months in twenty twenty three, in just four years.
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  Speaker 4: It's a significant issue that we're seeing. It is the
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  Speaker 4: number one issue that customers bring up when we sit
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  Speaker 4: down with them nowadays. It's a continuing issue. And so
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  Speaker 4: there's two aspects of that then that we typically talk about.
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  Speaker 4: One of those things is how can we build AI
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  Speaker 4: systems that can augment the capabilities of a novice person
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  Speaker 4: someone who hasn't been at the place very long. They
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  Speaker 4: also have very high turnover in these positions, so oftentimes
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  Speaker 4: you're talking about operators on the line. You know, you
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  Speaker 4: had a ten to fifteen year person and the person
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  Speaker 4: replacing them maybe it has six months to a year
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  Speaker 4: in that position. Because the high turnover, they're constantly starting
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  Speaker 4: over again. So how can we build AI systems that'll
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  Speaker 4: supplement the capabilities of these no office operators so that
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  Speaker 4: we're not just running these lines suboptimally forever. And the
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  Speaker 4: other aspect of it is capturing that expertise from their
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  Speaker 4: subject matter experts before they leave. So I was just
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  Speaker 4: at a conference. I was speaking at a conference this
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  Speaker 4: week and that was one of the key things they said.
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  Speaker 4: Somebody raised their hands said, you know, we've got We've
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  Speaker 4: got some of those people that you're talking about that
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  Speaker 4: have been here for fifteen twenty years, but they are
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  Speaker 4: on their way out. I mean within the next couple
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  Speaker 4: of years they're playing and retiring. What can we do
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  Speaker 4: to capture that expertise in AI? And the good news
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  Speaker 4: is is that there are mechanisms to mechanisms to do that,
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  Speaker 4: but the time to do that is now, before you
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  Speaker 4: lose that expertise out the door.
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  Speaker 2: Yeah, that really has to be a proactive program to
• 14:45
  Speaker 2: get that information while there's still the opportunity to do so.
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  Speaker 2: And it's great that they're AI tools to do that
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  Speaker 2: as well. But one of the things you were talking
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  Speaker 2: about earlier was, you know, driving the efficiency and the
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  Speaker 2: productivity within that manufacturing facility. How can they also leverage
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  Speaker 2: that AI to advance sustainability goals or something like energy
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  Speaker 2: management or waste reduction. You know, there are a lot
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  Speaker 2: of other things besides, just like you said, the manufacturing
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  Speaker 2: process itself not that excited about changing the way they
• 15:28
  Speaker 2: do it. They're five to seven years behind. So there
• 15:31
  Speaker 2: are other ways that they can use this technology to
• 15:34
  Speaker 2: improve operations. That isn't just the manufacturing process, but more
• 15:38
  Speaker 2: about the facility itself and the processes of or I
• 15:43
  Speaker 2: guess the byproducts of the processes. There's so much involved
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  Speaker 2: in manufacturing and AI is going to touch all of it.
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  Speaker 4: Yeah, yeah, And so it's actually it's so interesting to
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  Speaker 4: kind of generationally to see because especially when you talk
• 15:58
  Speaker 4: to millennials, when you talk to gen z's sustainabilities top
• 16:02
  Speaker 4: of mind for them, and many of them are now
• 16:03
  Speaker 4: in leadership positions at these companies, and so that's good.
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  Speaker 4: Like so, so we want to be cognizantve sustainability. There's
• 16:11
  Speaker 4: kind of two aspects there. One is that we need
• 16:14
  Speaker 4: to start to define AI projects as sustainability projects, because
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  Speaker 4: you mentioned a whole bunch of things, and every single
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  Speaker 4: one of those things. If I'm increasing the efficiency of
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  Speaker 4: the line, I am implicitly I am reducing energy. I
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  Speaker 4: am I am increasing throughput, which means that I'm reducing
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  Speaker 4: waste all of those things. I mean, the amount of
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  Speaker 4: waste in manufacturing is mind bibling when you see how
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  Speaker 4: much gets thrown out, how much draft gets thrown out, uh,
• 16:43
  Speaker 4: and then the amount of energy that's used to run
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  Speaker 4: these facilities. So if I'm doing an AI project and
• 16:50
  Speaker 4: I'm squeezing you know, singlar double digit percentage improvements out
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  Speaker 4: of those lines, I am inevitably reducing the amount of energy.
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  Speaker 3: You know.
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  Speaker 4: We did an AI project for a glass manufacturer, and
• 17:02
  Speaker 4: it's so funny too, because depending on who you talk
• 17:05
  Speaker 4: to at the company, you tend to get a different answer.
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  Speaker 4: So we did a glass project and we were building
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  Speaker 4: an AI optimizer to more efficiently cut glass. Think like
• 17:14
  Speaker 4: window panes out of a big sheet of glass. Obviously,
• 17:17
  Speaker 4: everything else that's not window is scrap. It gets broken
• 17:20
  Speaker 4: down and it gets remelted. Now, we were talking to
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  Speaker 4: a couple of the operators and they go, well, we
• 17:24
  Speaker 4: don't have scrap because all of that gets that gets remelted, right, So,
• 17:29
  Speaker 4: so effectively our scrap level is zero. Then later on
• 17:33
  Speaker 4: in the day, you have a meeting with one of
• 17:34
  Speaker 4: the finance people and they're like, no, all of that
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  Speaker 4: is scrap. We track all of that, even if they
• 17:39
  Speaker 4: don't know, we track all of that because all of that,
• 17:42
  Speaker 4: even the melting down. You think about all of the
• 17:44
  Speaker 4: energy that went into producing that pane of glass the
• 17:47
  Speaker 4: first time, the first time. Now you're gonna break it down,
• 17:49
  Speaker 4: you're gonna smash it, and you're gonna remelt it and
• 17:52
  Speaker 4: pour all of that energy and raw material back into it,
• 17:54
  Speaker 4: all the labor that it took. Absolutely so all of
• 17:57
  Speaker 4: that is wasted energy. So we were made able to
• 18:00
  Speaker 4: make massive Their optimization engine that they were using to
• 18:04
  Speaker 4: lay out these glass panes was very suboptimal. We were
• 18:07
  Speaker 4: able to squeeze so many more panes of glass out
• 18:09
  Speaker 4: of out of these sheets of glass and reduce their
• 18:11
  Speaker 4: scrap by double digit percentage. That's a huge impact on
• 18:15
  Speaker 4: energy reduction. Was that classified as a sustainability project by
• 18:19
  Speaker 4: corporate It was not.
• 18:20
  Speaker 3: It should have been.
• 18:21
  Speaker 4: So that's what I'm saying, we need to start defining
• 18:23
  Speaker 4: these AI projects as sustainability projects.
• 18:26
  Speaker 3: But the other aspect of it is.
• 18:28
  Speaker 4: That I think is in some ways even more important
• 18:30
  Speaker 4: when we look at sustainability goals, is many of these
• 18:33
  Speaker 4: companies have very aggressive sustainability goals. Great, we want to
• 18:37
  Speaker 4: be carbon neutral bi X here. Fine, here's the problem
• 18:40
  Speaker 4: with that is there is no other technology other than
• 18:44
  Speaker 4: AI that is like in the wings waiting to reduce.
• 18:48
  Speaker 3: Their greenhouse impact or their carbon like.
• 18:51
  Speaker 4: There isn't any other technologies other than AI. So if
• 18:54
  Speaker 4: you want to get hit these aggressive state sustainability goals,
• 18:58
  Speaker 4: which again I encourag that's great, then you're going to
• 19:02
  Speaker 4: need to start to look seriously at adopting AI and
• 19:05
  Speaker 4: adopting AI as aggressively as you're trying to hit these
• 19:08
  Speaker 4: goals because there isn't any other technology, like we would
• 19:10
  Speaker 4: have already known about it, it would be implemented already.
• 19:13
  Speaker 4: If there was some technology, it was going to give
• 19:15
  Speaker 4: you double digit percentage decreases in natural gas utilization or
• 19:20
  Speaker 4: in you know, coal utilization, like, only AI can.
• 19:23
  Speaker 3: Get you there.
• 19:23
  Speaker 2: So unless you're horribly inefficient to begin with, but let's
• 19:28
  Speaker 2: hope that everyone is starting from at least a place
• 19:31
  Speaker 2: of we're trying our best.
• 19:33
  Speaker 4: Well and so you know, what's really interesting you say
• 19:35
  Speaker 4: that because one of the questions I get is is, well,
• 19:37
  Speaker 4: so you know Rovis's service is fourteen different industries, so
• 19:42
  Speaker 4: everything from oil and gas to life science, to consumer
• 19:45
  Speaker 4: package goods, food and beverage, you name it. One of
• 19:48
  Speaker 4: the questions I get is is what industries have adopted
• 19:51
  Speaker 4: AI first, And it's not what you would think.
• 19:54
  Speaker 3: The industries that are.
• 19:55
  Speaker 4: Adopting AI first in a big way. Turns out there
• 19:58
  Speaker 4: the commodity industries. Glass, I've got more steel projects go
• 20:02
  Speaker 4: AI projects going in steel than you would imagine. And
• 20:05
  Speaker 4: to your point, those are processes that have been around
• 20:08
  Speaker 4: for like one hundred years and haven't changed that much. Right,
• 20:11
  Speaker 4: These are well understood, well optimized processes and they are
• 20:14
  Speaker 4: the one. But it's also very tight margins. It's commodity pricing.
• 20:18
  Speaker 4: So they are the ones that know if they can
• 20:20
  Speaker 4: squeeze a couple more percentages out of their existing equipment,
• 20:23
  Speaker 4: that that can be the edge over their competitors. The
• 20:26
  Speaker 4: industries that have not adopted it widely is life science,
• 20:31
  Speaker 4: I think because of the risk aversion, plus they always
• 20:34
  Speaker 4: have to worry about FDA validation and things like that,
• 20:37
  Speaker 4: so there's a there's a there's a lot more risk there,
• 20:40
  Speaker 4: and so they have not adopted it as as much
• 20:42
  Speaker 4: as you would think.
• 20:43
  Speaker 3: And as quickly as you would think.
• 20:45
  Speaker 2: Yeah, that's interesting. So when we're talking about some of
• 20:49
  Speaker 2: these adoptions and readiness to adopt, you mentioned that, you know,
• 20:52
  Speaker 2: these commodities companies, these these raw materials companies, they're they're ready,
• 20:56
  Speaker 2: they're chomping at the bit, They're they're interested. One of
• 21:02
  Speaker 2: I mean that's a critical component is being ready. You're
• 21:04
  Speaker 2: not going to be able to make a change if
• 21:05
  Speaker 2: your if your team, if your organization isn't ready for it.
• 21:09
  Speaker 2: What are some of the common pitfalls that manufacturers encounter
• 21:12
  Speaker 2: regarding like data quality. I know that's a huge one
• 21:16
  Speaker 2: for us when we're working with our clients, is the
• 21:19
  Speaker 2: quality of the data that they're able to provide. I'm
• 21:21
  Speaker 2: sure as an integrator you also face that where they
• 21:25
  Speaker 2: think they have good data and then they hand it
• 21:27
  Speaker 2: to you and you're like, way, not, it's it's okay.
• 21:32
  Speaker 2: So the data quality, things like their their infrastructure, like
• 21:36
  Speaker 2: what do they have to be able to work with
• 21:38
  Speaker 2: once they've they've got some information? And what kind of
• 21:43
  Speaker 2: practical steps could companies who are interested start making in
• 21:47
  Speaker 2: order to increase their maturity and be ready for those
• 21:50
  Speaker 2: next AI projects.
• 21:51
  Speaker 4: Yeah exactly, And so I mean there's a lot of
• 21:53
  Speaker 4: pitfall pitfalls and tips and tricks and things like that
• 21:57
  Speaker 4: that we could talk about. But let me let me
• 21:58
  Speaker 4: boil down, because you already mentioned a couple let me
• 22:00
  Speaker 4: add to that list. So I would say the number
• 22:05
  Speaker 4: one thing is data infrastructure at the plant floor level.
• 22:09
  Speaker 4: So this is capturing the data as it happens in
• 22:13
  Speaker 4: real time. For those of you who may not be
• 22:15
  Speaker 4: as familiar your listeners with manufacturing, we use these what
• 22:20
  Speaker 4: are called time series databases that we call them historians,
• 22:23
  Speaker 4: and we capture that process data, the very fast moving data.
• 22:27
  Speaker 4: We captured it in real time and we store it
• 22:30
  Speaker 4: in these optimized databases that are made to store this
• 22:32
  Speaker 4: type of data. We want to get more historians in.
• 22:35
  Speaker 4: We want to get more data capture as close to
• 22:39
  Speaker 4: the sources of data as possible, So that's step one.
• 22:42
  Speaker 4: Step two then is that we want to consolidate that
• 22:45
  Speaker 4: data into the cloud. So we want to marry that data,
• 22:50
  Speaker 4: both the process data, but then we want to marry
• 22:52
  Speaker 4: it to the transactional and relational data that's both on
• 22:55
  Speaker 4: the plant floor but also might exist in IT systems
• 22:58
  Speaker 4: like EUERP systems.
• 22:59
  Speaker 3: We need to.
• 22:59
  Speaker 4: Marry a really important factor that a lot of people
• 23:02
  Speaker 4: underestimate is mirroring up that quality data, right, So it's
• 23:05
  Speaker 4: not enough to know how the thing was made if
• 23:07
  Speaker 4: you were making junk the whole time, So you need
• 23:09
  Speaker 4: to know what was the final quality of that batch.
• 23:12
  Speaker 4: There's some tricks that we can do to marry that
• 23:14
  Speaker 4: data together so that you can get a single data set.
• 23:17
  Speaker 4: But ultimately, and even the data quality things you were
• 23:20
  Speaker 4: talking about, those are important, but a lot of times
• 23:22
  Speaker 4: we've got we do real data science too, so the
• 23:25
  Speaker 4: data science algorithms a lot of times can tell you
• 23:27
  Speaker 4: when you're having data quality issues, so you can go
• 23:29
  Speaker 4: back and kind of fix those after the fact. But ultimately,
• 23:33
  Speaker 4: we're trying to build consolidated, clean, concise, consolidated data sets
• 23:39
  Speaker 4: that we can then leverage to build mL models. But
• 23:42
  Speaker 4: the final step or the final tip here I would
• 23:45
  Speaker 4: say around readiness is to start with use cases and
• 23:49
  Speaker 4: work backwards to the technology and the data that's required.
• 23:52
  Speaker 4: Most customers get that backwards, and I understand because you've
• 23:56
  Speaker 4: got a lot of vendors in the space that are
• 23:58
  Speaker 4: hammering on them technology technology, so they get technology in
• 24:01
  Speaker 4: their head and then they try to work backwards to
• 24:03
  Speaker 4: the use case. You really want to start with use cases,
• 24:06
  Speaker 4: figure out where the value is, and then work backwards
• 24:09
  Speaker 4: to what's the data I'm going to need to make
• 24:11
  Speaker 4: that a reality, and what is the technology. There's no
• 24:14
  Speaker 4: limit to the amount of money in time you could
• 24:16
  Speaker 4: spend on data and technology. The limit is what value
• 24:20
  Speaker 4: you can get out of it. And that's what the
• 24:22
  Speaker 4: use cases are going.
• 24:22
  Speaker 3: To tell you. All right.
• 24:24
  Speaker 2: So speaking of value and return on investment, that's critical
• 24:28
  Speaker 2: obviously all the time. But what other measures of success
• 24:31
  Speaker 2: are there with AI? And you know, is there something
• 24:35
  Speaker 2: else they should be looking at to measure success of
• 24:38
  Speaker 2: a project.
• 24:39
  Speaker 4: One hundred percent? And I'm so glad you asked that
• 24:41
  Speaker 4: because I get into these meetings where customers get so
• 24:46
  Speaker 4: laser focused on the dollar ROI and it's like, yes, okay,
• 24:49
  Speaker 4: I understand that, and we can get there with AI projects.
• 24:52
  Speaker 4: We can pick the right projects that have the dollar
• 24:54
  Speaker 4: dollar ROI. But there are so many other things. The
• 24:57
  Speaker 4: first one I would say is quality. I'd say three.
• 25:00
  Speaker 4: The first one is quality of life for those folks
• 25:03
  Speaker 4: who actually work in the plant. So if we can
• 25:06
  Speaker 4: make their life better, safer, if we can put them
• 25:09
  Speaker 4: in positions where they're adding more value to the organization
• 25:13
  Speaker 4: if we can just make their life less repetitive and
• 25:16
  Speaker 4: their job, you know, and make them excited to come
• 25:18
  Speaker 4: to work.
• 25:19
  Speaker 2: Like.
• 25:19
  Speaker 4: Those are all things. I mean, you're not those aren't
• 25:21
  Speaker 4: going to be captured in dollars, but those are the
• 25:23
  Speaker 4: quality of life things that AI can bring. I would
• 25:25
  Speaker 4: say the second one is agility to face shifting regulations,
• 25:31
  Speaker 4: Agility to handle disruptions and supply chain, agility to respond
• 25:36
  Speaker 4: to customer demands. I've got a lot of customers that say,
• 25:39
  Speaker 4: you know, five ten years ago, we were the company
• 25:41
  Speaker 4: that like eighty percent of our output was one product,
• 25:45
  Speaker 4: one skew. We just we just kind of burn and
• 25:48
  Speaker 4: churn on this one thing. And now suddenly we've got
• 25:50
  Speaker 4: this huge mix of products. We've got a service because
• 25:53
  Speaker 4: that's where the market has gone. You know, it's more
• 25:55
  Speaker 4: custom it's more focused. So that ability to to to
• 26:00
  Speaker 4: handle those things and face those types of changes more
• 26:03
  Speaker 4: more quickly. And the final one, and the one that
• 26:05
  Speaker 4: I don't think manufacturers think enough about, is the ability
• 26:10
  Speaker 4: to attract a workforce who has grown up with technology.
• 26:13
  Speaker 4: So these these younger generation, they my kids were born
• 26:17
  Speaker 4: with an iPad in their hand, good, bad, or indifferent,
• 26:20
  Speaker 4: like we just were Like they've been using an iPad
• 26:23
  Speaker 4: since they could, you know, crawl and whatever, so so
• 26:26
  Speaker 4: they are used to that and that's what they're looking
• 26:29
  Speaker 4: for in their job that they're going to spend eight
• 26:31
  Speaker 4: plus hours a day at so so manufacturers need to
• 26:35
  Speaker 4: spend more time thinking about that ability to attract a workforce.
• 26:39
  Speaker 4: You know, it's one of those things that Silicon Valley startups,
• 26:41
  Speaker 4: those tech companies, they think a lot about how do
• 26:44
  Speaker 4: I attract and keep a good workforce and manufacturers. You know,
• 26:49
  Speaker 4: so an AI project even you know, maybe the dollar
• 26:52
  Speaker 4: amount ROI you know, is closed, but maybe not as compelling.
• 26:56
  Speaker 4: But boy, if I'm a company that looks like I
• 26:59
  Speaker 4: invest in AI, and I can use that as a
• 27:01
  Speaker 4: talking point, and I can get out to career fairs
• 27:03
  Speaker 4: and say, hey, we've got five AI projects going right now,
• 27:06
  Speaker 4: that's a company that's going to attract those workers again
• 27:09
  Speaker 4: that are in high demand because they don't have enough
• 27:11
  Speaker 4: of these people.
• 27:12
  Speaker 2: So absolutely, because it goes right back to your first
• 27:15
  Speaker 2: point that the you know, we don't have the longevity
• 27:17
  Speaker 2: in these positions anymore, and it good better otherwise we
• 27:22
  Speaker 2: have to assume that that's because of some sort of churn.
• 27:26
  Speaker 2: And if that's the case, then you don't only need
• 27:30
  Speaker 2: to attract the right people, you need to keep the
• 27:32
  Speaker 2: right people. And so if that's going to elevate their
• 27:38
  Speaker 2: status in the eyes of today's workforce, that's huge. And
• 27:44
  Speaker 2: that's not necessarily something you can measure with the number.
• 27:46
  Speaker 2: I'm sure there's a number somewhere, a metric, a KPI
• 27:48
  Speaker 2: that they're going to come up with. They're going to
• 27:50
  Speaker 2: say that this is our you know, net quality score
• 27:52
  Speaker 2: or whatever for recruiting or for you know, being a
• 27:55
  Speaker 2: techie company. There's lots of awards, there's lots of distinctions
• 27:58
  Speaker 2: that they can utilize to show that, but it's it's
• 28:01
  Speaker 2: not a dollar amount that's going to go on on
• 28:04
  Speaker 2: their annual report. So, looking towards that next decade, the
• 28:08
  Speaker 2: next five to ten years, how do you see it
• 28:11
  Speaker 2: evolving in manufacturing? Is there a trend or a particular
• 28:16
  Speaker 2: technology within the AI scope that that manufacturers should be
• 28:21
  Speaker 2: watching out for to stay ahead one hundred percent?
• 28:23
  Speaker 3: So obviously generative AI.
• 28:27
  Speaker 4: So for those for your listeners, genera of AI is
• 28:29
  Speaker 4: an umbrella term that includes CHET, GPT, all the large
• 28:33
  Speaker 4: language models or llms as well as the.
• 28:35
  Speaker 3: Image and video generation sure generative AI.
• 28:39
  Speaker 4: We understand the impact it's having, and it's it's new
• 28:42
  Speaker 4: and it's exciting. And again, when most people say AI,
• 28:45
  Speaker 4: they think that's what they think.
• 28:46
  Speaker 3: Of generative AI.
• 28:48
  Speaker 4: Right, there are other types of AI, and one in
• 28:50
  Speaker 4: particular that I'd like to highlight briefly is what's called
• 28:54
  Speaker 4: autonomous AI. And autonomous AI is AI that's built on now,
• 29:00
  Speaker 4: it's almost a decade old a mL algorithm called deep
• 29:03
  Speaker 4: reinforcement learning. And what makes autonomous AI unique is its
• 29:07
  Speaker 4: ability to it's able to build long term strategy, so
• 29:12
  Speaker 4: it's able to make human like decisions. If they started
• 29:15
  Speaker 4: out by using it to play games, it beat all
• 29:17
  Speaker 4: of our best grand masters at the game of go,
• 29:20
  Speaker 4: at the game of chess, StarCraft. But then you know,
• 29:24
  Speaker 4: it's matured a lot since then, and so we're using
• 29:26
  Speaker 4: and I've got customers, real customers that are running on
• 29:29
  Speaker 4: the plant floor right now that it's they're using an
• 29:31
  Speaker 4: in production. And again, it's the perfect technology to because
• 29:34
  Speaker 4: it can learn like a human can. It's the perfect
• 29:37
  Speaker 4: technology to augment the capability of younger employees, newer employees
• 29:43
  Speaker 4: and through a process that we use called machine teaching.
• 29:46
  Speaker 4: We're actually able to capture that expertise from those subject
• 29:50
  Speaker 4: matter experts and build it into the autonomous AI.
• 29:53
  Speaker 3: So it uses that as.
• 29:54
  Speaker 4: Its basis, and then when we train it, it learns
• 29:57
  Speaker 4: and gets to an expert level at whatever part of
• 29:59
  Speaker 4: the problem us we're pointing it out. So I think
• 30:02
  Speaker 4: a lot of manufacturers when I talk to them, they
• 30:05
  Speaker 4: tend to call me in because they want to talk AI,
• 30:07
  Speaker 4: and they think we're going to talk about generative AI,
• 30:09
  Speaker 4: and immediately I pivot and I talk about autonomous AI
• 30:13
  Speaker 4: because the reality of it is is that the types
• 30:14
  Speaker 4: of problems that we're solving on the plant floor tend
• 30:17
  Speaker 4: to be more operational problems, and that's what autonomous AI
• 30:20
  Speaker 4: is good at addressing. JENITTI of AI is great at
• 30:23
  Speaker 4: knowledge type problems, but those are kind of in a
• 30:25
  Speaker 4: different part of the manufacturing facility. We need to solve
• 30:28
  Speaker 4: operational problems. So I would encourage for those manufacturers who
• 30:32
  Speaker 4: maybe haven't looked into autonomous AI, please do look into that.
• 30:35
  Speaker 4: It is we feel like an inflection point. We do
• 30:39
  Speaker 4: feel like autonomous AI and the adoption of it is
• 30:42
  Speaker 4: going to be one of those things that's going to
• 30:43
  Speaker 4: make a c change in you know, a big step
• 30:47
  Speaker 4: change in how our manufacturers work. So we're very bullish
• 30:51
  Speaker 4: on it. And then of course generative AI. I mean,
• 30:53
  Speaker 4: there's no question that it's going to have a huge impact,
• 30:56
  Speaker 4: and we should continue, my customers should continue to look
• 30:59
  Speaker 4: into generative and keep it on the radar. But there's
• 31:02
  Speaker 4: some very big limitations.
• 31:03
  Speaker 3: With generative AI. It cannot reason.
• 31:06
  Speaker 4: In fact, there was a study that came out from
• 31:07
  Speaker 4: Apple late last year that finally proved once and for
• 31:10
  Speaker 4: all that jenerat of AI is not capable of reasoning.
• 31:13
  Speaker 3: It will never be a tot.
• 31:14
  Speaker 4: Them say, I can reason, it can understand causal connections,
• 31:16
  Speaker 4: but generative I won't be able to. And the other
• 31:19
  Speaker 4: thing is hallucinations, which they're they're working to try to minimize.
• 31:23
  Speaker 4: But we just saw on Saturday, the Chicago Sun Times
• 31:27
  Speaker 4: ran an article, I don't know if you saw it
• 31:29
  Speaker 4: with the summer reading list for twenty twenty five, and
• 31:31
  Speaker 4: over half of the books on that list were don't exist.
• 31:35
  Speaker 4: Don't exist because the author had used AI to generate
• 31:38
  Speaker 4: the thing, and no one from editors on now and
• 31:40
  Speaker 4: no one had bothered a double check that these were
• 31:42
  Speaker 4: actual books, so hallucinations, I mean, and that's you know,
• 31:46
  Speaker 4: it's funny when it does those types of things in
• 31:48
  Speaker 4: those kinds of context or generates crazy marketing lurbs. But
• 31:52
  Speaker 4: on the plant floor, I.
• 31:53
  Speaker 3: Mean, you could kill somebody.
• 31:54
  Speaker 4: So it's just not appropriate to use generative AI yet
• 31:58
  Speaker 4: on the plant floor. And you know so, but keep
• 32:01
  Speaker 4: an eye on I mean, it's obviously it's going to
• 32:03
  Speaker 4: get there, but it's still a relatively new technology, all right.
• 32:06
  Speaker 2: So it's clear that this conversation is just beginning, and
• 32:11
  Speaker 2: we look forward to seeing how these strategies continue to
• 32:14
  Speaker 2: evolve in the manufacturing space. Thanks so much, Brian for
• 32:18
  Speaker 2: joining us and sharing your insight today.
• 32:21
  Speaker 3: Yeah, thank you Ellen.
• 32:22
  Speaker 2: To our listeners, if you found today's episode valuable, be
• 32:25
  Speaker 2: sure to follow Speaking of Supply Chain wherever you get
• 32:27
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• 32:30
  Speaker 2: future of operations. Until next time, keep innovating and keep automating.
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