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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.

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Speaker 1: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 2: Tracy, I think it's because of all of the illegal

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Speaker 2: cannabis stores in New York City.

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Speaker 1: But this could go anywhere. I know, all right, go on.

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Speaker 2: But the thing about them, and I guess it's kind

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Speaker 2: of funny, and I guess it kind of makes sense,

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Speaker 2: and maybe it's also kind of cool, is that The

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Speaker 2: other thing is like, so they sell various weed products,

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Speaker 2: but they also seem to be the favored vendors of

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Speaker 2: all kinds of exotic snack foods in New York City.

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Speaker 1: Right, So if you go into a corner store in

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Speaker 1: the window, yeah, yeah, exactly, you don't have to go in,

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Speaker 1: you can just look. You will see weed probably a

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Speaker 1: lot of these stores are still unlicensed. You will see

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Speaker 1: energy drinks, you will see maybe some alf bars and

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Speaker 1: things like that. Yeah, and then you will also see

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Speaker 1: lots and lots of snacks. It's like the ultimate vice shop, right,

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Speaker 1: like you can satisfy, you can satisfy all your cravings

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Speaker 1: or almost all of them in one shop.

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Speaker 2: Yes, absolutely, not only do. They have a lot of

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Speaker 2: chips and snacks. They have the type of snacks that

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Speaker 2: used to be you would get only when one of

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Speaker 2: your colleagues like went abroad, right, So you'd have a

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Speaker 2: colleague that like took a trip to the Philippines or

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Speaker 2: took a trip to Hong Kong or something, and then

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Speaker 2: they would come back and they're like, oh, look I

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Speaker 2: brought like, you know, Macha flavored kid kats or mapo

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Speaker 2: grilled tofu flavored potato chips, or diry and flavored potato chips.

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Speaker 2: And then it never was there so cool. But now

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Speaker 2: they're everywhere in the stores here.

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Speaker 1: They definitely are more available than they used to be.

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Speaker 1: My experience with this personally is probably a little different

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Speaker 1: to a lot of other people, as someone who you know,

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Speaker 1: spent a lot of you that in Japan. Yeah, I

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Speaker 1: mean I have I have vivid memories of going into

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Speaker 1: like a don Keyixote, which is this big kind of

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Speaker 1: department store, specifically to buy flavored kit cats. They were

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Speaker 1: always in the basement and there were like dozens of flavors.

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Speaker 1: Even in this would have been in like two thousand

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Speaker 1: and one, two thousand and two, and I feel like

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Speaker 1: flavored Kitkats are to me probably what shrimp is to

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Speaker 1: that character from Forrest Gum. Like I could sit here

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Speaker 1: for the next twenty minutes and just like rattle off

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Speaker 1: all the flavors that I remember. I love Kitkats.

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Speaker 2: So in doing research for this episode, I came across

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Speaker 2: this great website, takitos dot net, which basically refused every

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Speaker 2: single snack and so right now they have links like

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Speaker 2: and I don't know if all the links work, because

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Speaker 2: I get the impression that some of these flavors are

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Speaker 2: cycled in and out, but there are links and reviews

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Speaker 2: of one hundred ninety seven different flavors of Dorito's that

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Speaker 2: theoretically currently or at some point we're sold on Amazon,

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Speaker 2: Dorito's blue Grilled Steak, Dorito's Golden Cheese Flavor, Dorito's late

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Speaker 2: Night Tacos at Midnight, Dorito's three D crunched chili cheese Nacho,

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Speaker 2: and then I mean it just the list just goes

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Speaker 2: on and on. It is nothing like when I was

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Speaker 2: a kid in the US, whereas like there was like

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Speaker 2: Dorito's and cool Ranch Dorito's. Yeahs it, We're living in

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Speaker 2: a golden age.

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Speaker 1: So I disagree a little bit on this point. So, yes,

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Speaker 1: there are lots more flavors that exist, but as you

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Speaker 1: just pointed out, like they are not always available. And Okay,

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Speaker 1: in New York, you know, you can go into a

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Speaker 1: weed shop and there's a novelty value to buying like

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Speaker 1: some exotic flavored potato chips alongside you know whatever else.

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Speaker 1: But I feel like what this really kind of comes

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Speaker 1: down to is this really really intelligent marketing strategy where

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Speaker 1: you create all these new types of flavors and you

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Speaker 1: get people talking about them, and maybe you don't produce

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Speaker 1: them in huge quantities, but people like to talk about them.

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Speaker 1: People like to you know, take pictures where they're eating

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Speaker 1: Matcha flavored or like Soccer of flavored kit cats or whatever.

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Speaker 1: So like for the companies, it feels like this is

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Speaker 1: kind of a win win.

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Speaker 2: Well I agree with that, and you do see this,

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Speaker 2: like on Twitter, a lot of it's like, oh, Dorito's

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Speaker 2: just teamed up with you know, Jack in the Box,

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Speaker 2: or Dorito's just teamed up with so and so, and

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Speaker 2: then there's like a short run thing. So I do

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Speaker 2: get the impression some of them are by the way,

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Speaker 2: I just can't he Dorito's gold picking Duck flavored. Oh

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Speaker 2: that sounds so good, so good. A right, We're gonna

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Speaker 2: have to try to anyway. But it does reason the

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Speaker 2: question like how did this happen? I mean, it's great,

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Speaker 2: I love it. I mean I don't need that many chip,

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Speaker 2: but in theory I love it. How did we enter

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Speaker 2: this world where we just have so much variety? And

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Speaker 2: although a lot of them maybe are short run, like

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Speaker 2: a lot of them do exist, you have to work

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Speaker 2: a little bit. But while we're waiting, I did order

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Speaker 2: like a twelve pack Dorito's pack and a sixteen pack

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Speaker 2: different flavored kid Cat So they are gettable, you know,

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Speaker 2: at various times.

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Speaker 1: They are gettable. I still have questions about the overall

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Speaker 1: distribution strategy here, but it does seem like there is

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Speaker 1: this trend towards a much wider and more exotic variety

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Speaker 1: of flavors, and so I'm curious how that came to be,

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Speaker 1: Like what is the corporate strategy that's actually driving it?

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Speaker 1: And also the technology? Right, so how can it be

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Speaker 1: that a company like well, Kitkat's not a company. In fact,

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Speaker 1: I think Kitkats are made by both Hershey's and Nestley,

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Speaker 1: depending on what part of the world you're in. But

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Speaker 1: how is it that they're able to do you know,

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Speaker 1: dozens and dozens of different chocolate flavors and variations of.

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Speaker 2: Oh my god, I check out this flavor Dorito's Roulette.

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Speaker 2: Have you heard of this?

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Speaker 1: This episode is just going to be us like looking

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Speaker 1: at this is great.

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Speaker 2: This is such a good idea. Dorito's Roulette originally came

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Speaker 2: in a Nacher Cheese version, offering randomly placed hot chips

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Speaker 2: among regular Nacher cheese works. That's such a good idea,

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Speaker 2: like some excitement anyway. To me, this is a golden age,

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Speaker 2: So I think we need to understand and the sort

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Speaker 2: of infrastructure and science and technology that has created this

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Speaker 2: era of chip abundant And I'm really chip abundance memorous

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Speaker 2: snack food of us.

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Speaker 1: This could be a different type of Loots episode.

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Speaker 2: Put go on, Yeah, there's no shortage here. It's the

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Speaker 2: lack of shortage that we're blown away by. We're going

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Speaker 2: to be speaking with Ryan Harlan. He is the director

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Speaker 2: of business Development at Etech Control Systems Integrator, and he

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Speaker 2: works with the various food and snack companies that are

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Speaker 2: rolling these out across the assembly lines. And he also

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Speaker 2: works on distribution. So, Ryan, thank you so much for

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Speaker 2: coming on the podcast.

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Speaker 3: Thank you for having me.

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Speaker 2: So I think we're a golden age. Tracy is a

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Speaker 2: little skeptical. Where do you come down on this question.

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Speaker 4: I think we're almost in a golden age. I think

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Speaker 4: we're getting there. I think within a lot of these

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Speaker 4: snack food companies. Doesn't quite think we're to the maximum capacity, right, We're.

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Speaker 2: Still haven't reached out.

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Speaker 1: The best is yet to come?

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Speaker 2: Is yet to come?

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Speaker 1: That sounds good. So maybe before we begin, can you

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Speaker 1: explain what it is that you do in the snaw

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Speaker 1: back food industry so to speak.

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Speaker 3: Sure.

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Speaker 4: So we work on the industrial automation side. So we're

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Speaker 4: an engineering services firm and panel shop. So we build

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Speaker 4: control panels that contain the programmable logic computers the PLCs

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Speaker 4: that have the programming that tie all of the various

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Speaker 4: machine centers within a line that create the snacks. So

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Speaker 4: you'd have your buyers, dehydrators, fillers, cappers, baggers, palatizers, whatever

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Speaker 4: you would have on the line for whatever type of

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Speaker 4: snack you're producing. The control system has the program tells

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Speaker 4: what to start, when, what to do which process, and

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Speaker 4: then eventually at the end you get your product and

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Speaker 4: then send it off to the warehouse where a different

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Speaker 4: system picks it up.

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Speaker 2: So I want to talk about the technology and how

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Speaker 2: we got to this era, but first I want to

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Speaker 2: actually establish the premise of the conversation. Is it true, though,

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Speaker 2: regardless of whether we've reached the true Golden age or not,

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Speaker 2: that we just have tremendous more variety of snacks than say,

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Speaker 2: when I was a kid, and I think there were

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Speaker 2: like two flavors of Dorito's.

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Speaker 4: Yeah, no, that's absolutely true. And a lot of the

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Speaker 4: snack production is local. So if you're buying Freedola chips,

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Speaker 4: you're probably getting them from a factory within a couple

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Speaker 4: hours from you for the most part. If you're in

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Speaker 4: a large population center, and these factories have been around

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Speaker 4: for fifty plus years in a lot of cases, and

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Speaker 4: it's a local supply chain. So the change from two

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Speaker 4: flavors to multiple flavors with a similar population typically in

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Speaker 4: an area, is due to enhance production and throughput.

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Speaker 1: On that note, when did this become a thing or

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Speaker 1: start to become a thing and what was the proximate

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Speaker 1: like trigger or development that kicked off. I mean, I

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Speaker 1: imagine there was a company that was probably at the

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Speaker 1: forefront of this, and maybe it was the maker of

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Speaker 1: kit kats, or maybe it was a potato chip company

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Speaker 1: or something like that, But what was it that kind

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Speaker 1: of set this development in motion?

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Speaker 4: I actually think these soda producers were on the front

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Speaker 4: lines of this for doing single runs, so you could

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Speaker 4: dozens of different mountain dew flavors going back twenty years right,

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Speaker 4: and limited time offerings. And soda is really easy to

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Speaker 4: do this with because you have your recipe, you clean

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Speaker 4: out your pipe, everything is the same. It's a really

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Speaker 4: simple industrial process and it's a lot more difficult when

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Speaker 4: you're dealing with snack food comparatively.

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Speaker 2: Before we even start talking about variety though, how is

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Speaker 2: it chip made? Let's just go think about like a

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Speaker 2: single bag of Fritos, I like Frito's, you know, just

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Speaker 2: like when we think conceptually, like what is the process

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Speaker 2: that existed probably forty years ago, twenty years ago, ten

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Speaker 2: years ago in which we got free doos? What happens

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Speaker 2: to make a frido?

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Speaker 4: So if you look at the ingredients for a normal freedo,

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Speaker 4: I think it's corn, corn oil, and salt. If you

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Speaker 4: don't have any any other seasoning on it. So you're

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Speaker 4: going to have your ground corn product, you're going to

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Speaker 4: fry it. You could make a frito at home, right,

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Speaker 4: you could deep fry some corn flour and oidy together

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Speaker 4: into different shapes.

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Speaker 3: I've may squeeze it into shapes and fry it.

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Speaker 1: I have not made homemade fri doos, but maybe sorry.

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Speaker 4: I don't think they would turn out quite the same.

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Speaker 4: But it's very simple, right, So you're frying the corn

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Speaker 4: and sending it through and that's really it. So that

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Speaker 4: hasn't changed for the actual production of a free doo.

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Speaker 4: You've had enhancements on the consistency of the shapes, the

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Speaker 4: volume you can do it once, the timings for various fries,

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Speaker 4: and how you might move it through. But the freedom

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Speaker 4: of fifty years ago is looks a lot like how

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Speaker 4: it's made today.

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Speaker 1: So you're talking about the actual manufacturing process. But I

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Speaker 1: mean that is like in some respects, that's kind of

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Speaker 1: a later step in these snack food product cycles. And

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Speaker 1: the very beginning is you know, I imagine there's a

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Speaker 1: flavor lab somewhere at a large snack food company and

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Speaker 1: you have all these people who are brainstorming ideas and

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Speaker 1: then maybe there's research and development that goes into thinking

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Speaker 1: about a specific one, there's consumer testing, product testing, seeing

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Speaker 1: how people respond to it. It feels to me like

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Speaker 1: maybe one one of the big changes that has happened

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Speaker 1: is that people have like also figured out that, well,

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Speaker 1: you can cut down a little bit on this huge

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Speaker 1: R and D process just by producing a bunch of

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Speaker 1: different flavors. So it's not like it's not incredibly important

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Speaker 1: to your company if you unveil this new flavor and

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Speaker 1: it falls flat, so to speak, if you're unveiling a

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Speaker 1: new flavor every other.

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Speaker 4: Month, right, And I think what they've gotten very good

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Speaker 4: at is so they would have small scale versions of

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Speaker 4: their factory or digital versions even now, where you would

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Speaker 4: produce something called the digital twin of your factory line

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Speaker 4: and you can simulate the R and D process through

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Speaker 4: there for the actual production and then push everything through

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Speaker 4: digitally to the factory so you can say, we know

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Speaker 4: it's going to take this long to cook, we want

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Speaker 4: to add this much flavor, we want to add this

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Speaker 4: much salt, garlic powder, chili powder, whatever you're going to

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Speaker 4: add to it, sugar, and we can test all that digitally.

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Speaker 4: We know how it's going to flow through the line

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Speaker 4: and then send that recipe directly to the production factory

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Speaker 4: to send it through a test run there. And doing

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Speaker 4: that process I think has sped that up a lot.

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Speaker 4: And they're using ingredients they already have, right, it's just

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Speaker 4: a different ratio. They're not affecting their own supply chain

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Speaker 4: as much as they're affecting their potential total throughput when

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Speaker 4: they make these runs. Because you do have change over time,

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Speaker 4: you do have recipe management, traceability concerns that you have

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Speaker 4: to do for various potential recalls and things like that.

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Speaker 4: So there's an opportunity cost, but it's pretty limited if

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Speaker 4: you feel confident you can get this run in Oh.

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Speaker 1: That's really interesting. So you can come up with a flavor,

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Speaker 1: and then using digital technology, you can basically simulate what

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Speaker 1: the production would look like using your existing system or

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Speaker 1: maybe figuring out if you need to put anything new

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Speaker 1: in place, although it sounds like for the most part

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Speaker 1: they don't.

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Speaker 4: Yeah, I think the real benefit for being able to

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Speaker 4: put something new in place is say you wanted to

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Speaker 4: change the bag sizes and you wanted to look at

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Speaker 4: maybe we have a line that goes through normally, but

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Speaker 4: now it feeds into a one ounce, two and a

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Speaker 4: half ounce, and six ounce bag potentially with quicker change

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Speaker 4: over time, so that you can have more variety running

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Speaker 4: through a line. You could basically drag and drop the

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Speaker 4: designs from an OEM into your line and it runs

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Speaker 4: sort of. I don't know if you guys are familiar

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Speaker 4: with the Factorio game. It's a big game for engineers.

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Speaker 1: No, that's so amazing.

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Speaker 3: It's really good.

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Speaker 4: It's a it's a computer game that lets you build

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Speaker 4: a simulated factory with physics and everything. Oh wow, and

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Speaker 4: eventually your factory covers the whole planet. And it's still

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Speaker 4: a video game, but it's it's really it's really interesting

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Speaker 4: that way. And we're doing that in real life now,

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Speaker 4: where we can drag and drop different elements into the

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Speaker 4: factory and potentially move pieces from other production lines to

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Speaker 4: two different lines.

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Speaker 3: For what we're trying to do. That really speeds up

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Speaker 3: the R and D process.

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Speaker 2: So let's talk about the increased throughput or the speed

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Speaker 2: with which changeovers can happen, et cetera. So let's imagine

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Speaker 2: it's two thousand and four and someone at Dorito says,

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Speaker 2: you know what, I think that the consumer would like

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Speaker 2: a shrimp flavored dorito, and maybe they've done some testing

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Speaker 2: and some surveys and something like there seems to be

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Speaker 2: some appetite for a shrimp flavored dorito, et cetera. How

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Speaker 2: difficult is that changeover in two thousand and four to

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Speaker 2: get the line to pause and go from regular doritos

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Speaker 2: to shrimp flavored doritos versus the difficult and ease of

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Speaker 2: that technology or that changeover in twenty twenty four.

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Speaker 4: In two thousand and four, you're dealing with paper in

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Speaker 4: a lot of that part of the factory. So you're getting,

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Speaker 4: if you're an operator, you're getting a recipe on paper

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Speaker 4: to go grab a twenty pound bag of salt, Go

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Speaker 4: grab a forty pound bag of this, Go go do this.

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Speaker 4: And there's a lot more checking, rechecking mistakes, quality issues.

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Speaker 4: There isn't the digital feedback that you have now with

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Speaker 4: the sensors down the line to check for quality. So

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Speaker 4: now we have three dvision systems that can dimensionally check

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Speaker 4: a chip. Is it big enough, is it unbroken? Check

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Speaker 4: it for color? Does it look like it got enough seasoning?

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Speaker 4: Did it overcook? Is it a little too brown? Did

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Speaker 4: we send it through the fry or too long? Is

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Speaker 4: it undercooked? Are we getting a bunch of chips that

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Speaker 4: are stuck together? There's some issue on the front end

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Speaker 4: that things aren't working right after we made the changes.

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Speaker 4: So all that can be detected in process now and

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Speaker 4: things can be stopped a lot faster and corrected. Where

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Speaker 4: twenty years ago, you're pulling something off the line, someone

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Speaker 4: is standing there analyzing all the lines running, and you

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Speaker 4: could be producing thirty minutes of bad product before you

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Speaker 4: catch it right, the potential for mistakes was a lot

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Speaker 4: higher and the risk of lost revenue was significantly higher.

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Speaker 2: Yeah, I wanted to get into the talking changeovers. Can

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Speaker 2: you talk then a little bit about okay, the sort

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Speaker 2: of business side of these decisions. So, if you can

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Speaker 2: catch mistakes faster, if you can make sure that the

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Speaker 2: seasoning is applied consistently quicker on a new run, what

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Speaker 2: opportunities does that open up for the OEM or the

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Speaker 2: company in terms of like, okay, now the bar to

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Speaker 2: make a decision. It's easier, we can try new things.

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Speaker 2: Talk about the connection between that technology and then the

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Speaker 2: confidence that a company has in trying new things.

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Speaker 4: Yeah, So when we're talking about how an operations person

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Speaker 4: or a plant manager would look at their production facility.

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Speaker 4: There's sort of a triangle between throughput, up time and quality,

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Speaker 4: and they're weighing these three factors against each other for

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Speaker 4: how they're producing to get up the number. It's a trilemma, right,

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Speaker 4: because you can never be better than your worst metric really, right,

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Speaker 4: So no matter how much you produce, if only fifty

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Speaker 4: percent is good product, you've only fifty percent effective, right.

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Speaker 1: Saving that for my next performance review.

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Speaker 4: Yeah, but it's it's like that in production. So what

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Speaker 4: we're looking at really is the loss of uptime guaranteed

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Speaker 4: when you are making changeovers. So you know you're going

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Speaker 4: to have planned down time as you do these changeovers,

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Speaker 4: so you have to outperform on your quality and throughput

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Speaker 4: side as this goes.

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Speaker 3: So, part of.

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Speaker 4: The digital transformation that you see in factories and the

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Speaker 4: digitization of factories is that you can pull a lot

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Speaker 4: more elements out of all of your various devices. In

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Speaker 4: two thousand and four, to use that example, if something

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Speaker 4: failed in one machine center in the factory, if you

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Speaker 4: were having an undercooking problem, you might not be able

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Speaker 4: to correct it unless someone got into the program made

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Speaker 4: some changes and then adjusted the time or an operator

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Speaker 4: caught it. And if you were two am and it's

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Speaker 4: the third shift, that operator might not be paying attention.

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Speaker 4: It might be their second job. They just have probably

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Speaker 4: not worked there that long usually, and the programmers also

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Speaker 4: worked during the day, so they might not be able

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Speaker 4: to fix it until the daytime. And now, because of

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Speaker 4: the digital feedback, we can create a sort of machine

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Speaker 4: learning environment potentially, or at least a closed loop environment

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Speaker 4: where we can use the quality data from three division

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Speaker 4: cameras later on in the system, or any camera system

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Speaker 4: we're using, and feed that back to the earlier machine

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Speaker 4: centers to say we're undercooking. Go longer, make that change,

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Speaker 4: and I'll validate as this next batch of product comes through. Right,

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Speaker 4: because it's usually a continuous process, so as I start

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Speaker 4: seeing good chips, I'll tell you that we're fine. And

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Speaker 4: it operates a little like cruise control in your car. Right,

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Speaker 4: So you've got a loop system. You want to go

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Speaker 4: fifty five, you're going fifty seven, you slow down, you

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Speaker 4: speed up until you get to a more ideal product,

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Speaker 4: which in the past, a really good operator does that

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Speaker 4: on their own. But truly post pandemic, we're at a

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Speaker 4: loss of good operators, and we're at a loss of

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Speaker 4: people that want to be operators in the first place.

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Speaker 1: As you're going through all these new technologies, I'm sort

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Speaker 1: of like mouthing to Joe, going wow, Yeah, for each

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Speaker 1: of these, I'm coming round to the golden age of

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Speaker 1: snack food idea, at least in terms of the actual

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Speaker 1: manufacturing process. But one thing I wanted to ask is, Okay,

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Speaker 1: so you described how new technology can kind of be

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Speaker 1: used as a safety net almost for risk taking and

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Speaker 1: punting on new types of flavors and makes it easier

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Speaker 1: in many ways to create these things. Is there a

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Speaker 1: way that technology can also inform the flavors being made? Like,

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Speaker 1: for instance, would you get data from a digital assistant

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Speaker 1: on the factory floor which would say, like, Okay, you're

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Speaker 1: not using up all I don't know, like the cheese

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Speaker 1: or the garlic when you're doing this particular product. Maybe

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Speaker 1: you want to look into doing a more cheese and

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Speaker 1: garlic heavy version of this. Do you get that kind

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Speaker 1: of feedback to.

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Speaker 4: Yeah, that kind of feedback can absolutely be flowed upward

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Speaker 4: towards the business process as we're collecting all this data,

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Speaker 4: So that tends to get visualized in dashboards for business

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Speaker 4: operations to look at and see what they're doing. That

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Speaker 4: wouldn't be an automatic decision, but absolutely we could see

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Speaker 4: that we might be able to produce different product because

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Speaker 4: we have a glut of some supply with how something

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Speaker 4: is working or how that goes. I think what informs

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Speaker 4: production more ends up being what leaves through the supply chain.

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Speaker 4: On the logistic side, what goes out of the warehouse,

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Speaker 4: what starts being ordered more, And that now can be

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Speaker 4: a driver versus I think in the past you would

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Speaker 4: have kind of a monthly production run planned out in advance,

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Speaker 4: and now I think it's much more day to day.

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Speaker 4: Someone comes in and finds out what they're producing that day.

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Speaker 2: So we've been talking largely in the chip idiom so far,

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Speaker 2: so to speak. But another snack category that is like

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Speaker 2: really blown up, and I know that you've worked in

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Speaker 2: this is nuts and particularly almonds and just the numerous

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Speaker 2: flavors of packaged almends. You could get, like I think

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Speaker 2: it used to be okay, you could get raw almonds,

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Speaker 2: maybe in a bag and maybe toasted almonds, and like

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Speaker 2: if you went to like you know, a hippie story,

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Speaker 2: you could maybe get like tomorrow or like soy sauce

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Speaker 2: almonds or something like that. But now there's just like

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Speaker 2: so many they've just really gone wild with the almond flavors.

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Speaker 2: What was that thing you were looking at, Tracy, Like,

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Speaker 2: what was the.

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Speaker 1: Oh, there's a brand of Korean almonds. I think it's

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Speaker 1: called Tom's or something like that, and I am slightly

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Speaker 1: upset with them. They come in all sorts of flavors

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Speaker 1: like tirami, sou honey, there's savory, spicy ones as well.

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Speaker 1: Whenever I see them at any store, mostly Asian supermarkets,

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Speaker 1: I buy, like, you know, at least five packs. They're

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Speaker 1: so good.

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Speaker 2: So talk to us what you've seen in the nut space,

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Speaker 2: the almonds space.

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Speaker 4: Yeah, California obviously well known for almonds, as everyone heard

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Speaker 4: about during the drought. That's right, So out here in

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Speaker 4: the Central Valley we produce a lot of almonds, and

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Speaker 4: the production factories that handle nut production out here not

428
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Speaker 4: just almonds, but pistachios as well. When you talk about

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Speaker 4: like the Wonderful Company, but when we're talking about almonds,

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Speaker 4: it's typically you're going to hear Blue Diamond Growers is

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Speaker 4: a co op of growers that own several factories through

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Speaker 4: the area, and typically what they had done is just

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Speaker 4: produced almonds and sent them off large amounts to Asia,

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Speaker 4: large amounts to everywhere else in the world. Because something

435
00:21:51,720 --> 00:21:53,959
Speaker 4: like ninety nine percent of almonds are grown in California,

436
00:21:54,040 --> 00:21:56,560
Speaker 4: So if you've seen almonds somewhere, they were probably grown

437
00:21:56,600 --> 00:21:59,320
Speaker 4: by a few of these large farms and sent off.

438
00:21:59,560 --> 00:22:01,680
Speaker 3: And in twenty twenty, during the.

439
00:22:01,600 --> 00:22:04,840
Speaker 4: Pandemic, obviously Asian markets straight up, as China shut down,

440
00:22:05,520 --> 00:22:07,760
Speaker 4: a lot of the service industry that they might have

441
00:22:08,040 --> 00:22:12,080
Speaker 4: sold to slowed down, and they made a business transition

442
00:22:12,119 --> 00:22:16,439
Speaker 4: into snacks really out of necessity at first, and then

443
00:22:16,760 --> 00:22:19,840
Speaker 4: it took off because people like yogurt covered almonds, and

444
00:22:19,880 --> 00:22:24,560
Speaker 4: they like lightly salted, smoked almonds, they like spicy almonds.

445
00:22:24,720 --> 00:22:26,600
Speaker 4: I think if there's a whole wall at the airport now,

446
00:22:26,600 --> 00:22:28,680
Speaker 4: if you go in to see all of these flavors.

447
00:22:28,720 --> 00:22:29,200
Speaker 2: Wh Yeah.

448
00:22:29,240 --> 00:22:33,000
Speaker 1: Absolutely, it was a massive mistake to record this episode

449
00:22:33,119 --> 00:22:35,040
Speaker 1: right at lunchtime. Can I just say that?

450
00:22:35,119 --> 00:22:38,240
Speaker 2: So on Amazon you can buy a pack that has Smokehouse.

451
00:22:38,680 --> 00:22:42,359
Speaker 2: This is just the Blue Diamond variety pack Smokehouse Bold,

452
00:22:42,359 --> 00:22:45,440
Speaker 2: which is with sabi and soy sauce, hob and narow barbecue, shiraja,

453
00:22:45,880 --> 00:22:49,159
Speaker 2: sweet tied chili, salt and vinegar, blueberry. It sounds a

454
00:22:49,200 --> 00:22:52,720
Speaker 2: little weird. I'm not gonna lie whole natural, lightly salted

455
00:22:52,800 --> 00:22:55,840
Speaker 2: and then toasted coconut and maybe, So how did this happen?

456
00:22:55,840 --> 00:22:58,760
Speaker 2: How is a company that specialized just in like we're

457
00:22:58,760 --> 00:23:02,280
Speaker 2: gonna sell balk almonds then able to whip out like

458
00:23:02,280 --> 00:23:03,639
Speaker 2: eleven different flavors like this.

459
00:23:04,480 --> 00:23:07,240
Speaker 4: They made the production choice to build new facilities and

460
00:23:07,280 --> 00:23:11,680
Speaker 4: new lines solely for snack production during this time. The

461
00:23:11,960 --> 00:23:14,560
Speaker 4: almonds don't stop growing. You have to harvest them, so

462
00:23:14,640 --> 00:23:17,119
Speaker 4: when you're dealing with growers, they have the product. So

463
00:23:17,160 --> 00:23:20,359
Speaker 4: they were essentially able to vertically integrate themselves into becoming

464
00:23:20,359 --> 00:23:24,080
Speaker 4: a snack producer. And once you've decided you're going to

465
00:23:24,080 --> 00:23:27,320
Speaker 4: make these little snack packs and you can make a

466
00:23:27,359 --> 00:23:30,600
Speaker 4: one ounce or a two ounce or six ounce bag

467
00:23:30,640 --> 00:23:33,919
Speaker 4: of almonds, you have your various lines and they're all

468
00:23:33,960 --> 00:23:37,560
Speaker 4: producing different sizes, and then all you're doing is roasting

469
00:23:37,560 --> 00:23:40,720
Speaker 4: your almonds and flavoring them and then cleaning that out

470
00:23:40,760 --> 00:23:43,119
Speaker 4: and changing it over and doing a different flavor. So

471
00:23:43,160 --> 00:23:45,000
Speaker 4: you could run lightly salted, and then you could run

472
00:23:45,080 --> 00:23:48,080
Speaker 4: regularly salted without a real issue there, right, and then

473
00:23:48,080 --> 00:23:50,359
Speaker 4: you could run maybe one more flavor after that before

474
00:23:50,400 --> 00:23:54,240
Speaker 4: having to mess with things before you damage the flavor right.

475
00:23:54,119 --> 00:23:56,399
Speaker 2: When you touch it. You mentioned, of course you have

476
00:23:56,400 --> 00:23:58,440
Speaker 2: to clean out the flavor because you don't want the

477
00:23:58,440 --> 00:23:59,600
Speaker 2: blue but you know you don't want to do the

478
00:23:59,640 --> 00:24:02,800
Speaker 2: blueberry run and then the shir rocher run right after

479
00:24:02,840 --> 00:24:05,960
Speaker 2: and there's still they're still residue from the blueberry run.

480
00:24:06,400 --> 00:24:09,959
Speaker 2: Has that process changed, the sort of switchover process of

481
00:24:10,119 --> 00:24:12,760
Speaker 2: getting one flavor in and one flavor out since you

482
00:24:12,800 --> 00:24:14,520
Speaker 2: started in the industry.

483
00:24:14,119 --> 00:24:17,080
Speaker 4: Absolutely it has, depending on what your product is and

484
00:24:17,119 --> 00:24:19,760
Speaker 4: what your your pipes look like. You're sending steam through

485
00:24:20,119 --> 00:24:24,040
Speaker 4: essentially doing what's called the clean in place process, so

486
00:24:24,080 --> 00:24:26,760
Speaker 4: you're sending your cleaning fluid or steam through the pipe,

487
00:24:27,080 --> 00:24:30,120
Speaker 4: and then you're changing out your nozzles and making sure

488
00:24:30,160 --> 00:24:32,480
Speaker 4: that clean, clean equipment is in there. Everything is stainless steel,

489
00:24:32,520 --> 00:24:36,080
Speaker 4: it's food grade, might get sprayed down, washed down. But

490
00:24:36,320 --> 00:24:39,600
Speaker 4: what we've been able to do now is detect a

491
00:24:39,680 --> 00:24:43,720
Speaker 4: cleanliness within the pipe that instead of running on timing,

492
00:24:43,920 --> 00:24:46,920
Speaker 4: we can say we know the pipe is clean when

493
00:24:46,920 --> 00:24:49,880
Speaker 4: we see these certain parameters go through or we can

494
00:24:50,200 --> 00:24:52,760
Speaker 4: we can understand that this sensor as it comes through,

495
00:24:52,800 --> 00:24:57,760
Speaker 4: it's it's now clean, and that's allowed gains of you know, somewhere.

496
00:24:57,840 --> 00:24:59,520
Speaker 4: If it was a thirty minute process, maybe you've been

497
00:24:59,560 --> 00:25:02,000
Speaker 4: able to gain four or five minutes. But if you

498
00:25:02,000 --> 00:25:03,560
Speaker 4: want to do that three or four times a day,

499
00:25:03,680 --> 00:25:06,879
Speaker 4: you've gained a lot of production because you're doing maybe

500
00:25:07,200 --> 00:25:10,880
Speaker 4: two hundred bags a minute, right, So when you math

501
00:25:10,960 --> 00:25:14,400
Speaker 4: that out, you're able to take these small We sort

502
00:25:14,400 --> 00:25:16,639
Speaker 4: of described it as the power of gaining one percent.

503
00:25:17,160 --> 00:25:19,960
Speaker 4: You gained one percent in twelve different places. You've gained

504
00:25:20,000 --> 00:25:22,720
Speaker 4: the ability to produce a whole other product for an hour.

505
00:25:23,080 --> 00:25:24,119
Speaker 3: Wow, you know, potentially.

506
00:25:39,880 --> 00:25:43,200
Speaker 1: So Joe brought up the Korean almonds, and of course

507
00:25:43,240 --> 00:25:47,120
Speaker 1: we started this episode mentioning kit kats among other things.

508
00:25:47,160 --> 00:25:50,360
Speaker 1: But it does seem like Asia, in some respects has

509
00:25:50,440 --> 00:25:55,240
Speaker 1: had a head start on experimenting with lots of different

510
00:25:55,280 --> 00:25:59,119
Speaker 1: types of flavors. Can you maybe talk about what enabled

511
00:25:59,240 --> 00:26:02,960
Speaker 1: them to do that? You know, places like Japan and Korea,

512
00:26:03,119 --> 00:26:07,600
Speaker 1: how are they manufacturing all these different varieties And is

513
00:26:07,600 --> 00:26:09,760
Speaker 1: it a case that we're sort of catching up to

514
00:26:09,800 --> 00:26:12,520
Speaker 1: that technology or is it a case that the market

515
00:26:12,800 --> 00:26:18,399
Speaker 1: was just more amenable to experimenting with these types of varieties.

516
00:26:18,600 --> 00:26:19,800
Speaker 3: I'd say it's a mix of both.

517
00:26:19,840 --> 00:26:22,720
Speaker 4: I think the factories when you're looking at production in

518
00:26:22,760 --> 00:26:26,160
Speaker 4: Asia tend to be newer, where the production in America

519
00:26:26,240 --> 00:26:29,240
Speaker 4: is retrofitted older factories from some of these legacy companies

520
00:26:29,720 --> 00:26:31,800
Speaker 4: that a lot of these factories have changed hands. There's

521
00:26:31,880 --> 00:26:34,399
Speaker 4: an ice cream plant down here that I think has

522
00:26:34,480 --> 00:26:36,879
Speaker 4: changed hands like six different times and makes Hagendaws bars,

523
00:26:37,280 --> 00:26:39,160
Speaker 4: but it's been owned by a bunch of different companies

524
00:26:39,200 --> 00:26:41,320
Speaker 4: and it's been around for fifty years. And you didn't

525
00:26:41,359 --> 00:26:44,919
Speaker 4: see that in the Asian markets. And I think that

526
00:26:45,800 --> 00:26:48,960
Speaker 4: when they came in there with American flavors to produce

527
00:26:49,000 --> 00:26:51,200
Speaker 4: for Asian markets, they didn't sell in the same way

528
00:26:51,200 --> 00:26:53,760
Speaker 4: that they wanted them to. So they started experimenting locally

529
00:26:54,040 --> 00:26:58,320
Speaker 4: by necessity to try and move product. I always think

530
00:26:58,320 --> 00:27:02,000
Speaker 4: of it similarly to Procter and Gamble makeing spicier toothpaste

531
00:27:02,040 --> 00:27:05,359
Speaker 4: for India, right, So they adjust to the local flavor.

532
00:27:05,400 --> 00:27:07,000
Speaker 4: And I think some of that's market research and some

533
00:27:07,080 --> 00:27:09,320
Speaker 4: of that's just sales data that they didn't perform, so

534
00:27:09,359 --> 00:27:11,800
Speaker 4: they had the other flavors, and I think it took

535
00:27:11,840 --> 00:27:16,119
Speaker 4: a long time for them to understand that America doesn't

536
00:27:16,160 --> 00:27:19,200
Speaker 4: just want to eat barbecue chips right, that we'd love

537
00:27:19,240 --> 00:27:21,199
Speaker 4: to get. I remember being very excited when I got

538
00:27:21,240 --> 00:27:23,359
Speaker 4: my first Lais all dressed bag of chips that I

539
00:27:23,359 --> 00:27:25,440
Speaker 4: found in the store, because I had seen online that

540
00:27:25,480 --> 00:27:27,760
Speaker 4: they were big in Canada. And I don't know why

541
00:27:27,760 --> 00:27:31,520
Speaker 4: that was exciting that I had the that's legitimately exciting.

542
00:27:31,680 --> 00:27:32,800
Speaker 1: I'm excited for you.

543
00:27:34,680 --> 00:27:35,280
Speaker 3: And they were good.

544
00:27:35,320 --> 00:27:37,359
Speaker 4: I mean I never bought them again, but they were good.

545
00:27:38,359 --> 00:27:40,120
Speaker 4: And I think a lot of people have that experience

546
00:27:40,119 --> 00:27:42,560
Speaker 4: where they want a snack variety, but they want it

547
00:27:42,600 --> 00:27:45,280
Speaker 4: to taste good. And I think that's the fear, is

548
00:27:45,320 --> 00:27:48,920
Speaker 4: that variety will sell, but the flavor won't get another sale.

549
00:27:49,200 --> 00:27:50,919
Speaker 4: And I think we've proven that sort of wrong, that

550
00:27:50,960 --> 00:27:52,639
Speaker 4: we can always just create more flavors.

551
00:27:53,119 --> 00:27:55,360
Speaker 1: Right, This is exactly what I was going to ask next.

552
00:27:55,400 --> 00:27:59,320
Speaker 1: So you mentioned you never bought that particular thing ever. Again,

553
00:27:59,440 --> 00:28:02,080
Speaker 1: it does seem like there is a consumption pattern here

554
00:28:02,200 --> 00:28:05,439
Speaker 1: where a lot of it is all about the novelty value.

555
00:28:05,560 --> 00:28:08,399
Speaker 1: So you know, a new flavor comes out, you try it,

556
00:28:08,560 --> 00:28:11,120
Speaker 1: you either like it or you don't. But even if

557
00:28:11,160 --> 00:28:16,119
Speaker 1: you like it. How much repeat business are these types

558
00:28:16,160 --> 00:28:19,959
Speaker 1: of experimental flavors actually getting. It feels like it's very

559
00:28:20,040 --> 00:28:23,000
Speaker 1: much about the cycle of launching new products.

560
00:28:23,640 --> 00:28:26,080
Speaker 4: Yeah, it's definitely about the cycle of launching new products.

561
00:28:26,119 --> 00:28:28,119
Speaker 4: And we've seen over the years that certain types of

562
00:28:28,160 --> 00:28:31,400
Speaker 4: flavors have tended to hold up. Like a chili lime

563
00:28:31,640 --> 00:28:34,439
Speaker 4: flavors on a lot of different chips have showed up

564
00:28:34,480 --> 00:28:38,160
Speaker 4: and stayed flaming hot varieties of every chip have stayed.

565
00:28:38,560 --> 00:28:41,120
Speaker 4: So it seems with spicier flavors they tend to stay.

566
00:28:41,360 --> 00:28:43,360
Speaker 4: A lot of the novelty dessert flavors I don't think

567
00:28:43,440 --> 00:28:46,160
Speaker 4: do very well that kind of bounce in and out.

568
00:28:46,280 --> 00:28:49,800
Speaker 4: You know, cinnamon churro chips sound great and then they're fine.

569
00:28:50,520 --> 00:28:52,520
Speaker 4: So I think if you trend it, if you walked

570
00:28:52,520 --> 00:28:54,000
Speaker 4: into your store and took a picture of your chip

571
00:28:54,040 --> 00:28:56,440
Speaker 4: aisle every week for a year, you'd see a lot

572
00:28:56,440 --> 00:28:59,480
Speaker 4: of different flavors, and you'd see some new flavors never leave,

573
00:29:00,480 --> 00:29:02,840
Speaker 4: and that those kind of get added into the production rotation.

574
00:29:02,920 --> 00:29:06,479
Speaker 4: And that's all built into the business analytics from these

575
00:29:06,560 --> 00:29:09,719
Speaker 4: companies that they can see their selling they move off

576
00:29:09,760 --> 00:29:11,600
Speaker 4: the warehouse shelves, you know, they have to move these

577
00:29:11,680 --> 00:29:14,120
Speaker 4: chips within a certain amount of time for freshness reasons.

578
00:29:14,560 --> 00:29:17,240
Speaker 4: If they're not leaving the shelves, they don't get made again.

579
00:29:17,520 --> 00:29:20,720
Speaker 2: This might be a really stupid question, but obviously in

580
00:29:20,760 --> 00:29:24,040
Speaker 2: the era of Instagram and DTC companies. You know, we

581
00:29:24,160 --> 00:29:26,360
Speaker 2: know about freedom Lay and these big companies, but there's

582
00:29:26,360 --> 00:29:30,520
Speaker 2: obviously a million smaller brands that try to maybe they

583
00:29:30,600 --> 00:29:33,320
Speaker 2: try to pretend to be a little bit healthier or

584
00:29:33,400 --> 00:29:36,240
Speaker 2: maybe you know, whatever it is. Is it easy now,

585
00:29:36,360 --> 00:29:38,640
Speaker 2: Like if Tracy and I had an idea for a

586
00:29:38,760 --> 00:29:42,880
Speaker 2: chip shape and a chip flavor, is there sort of

587
00:29:43,000 --> 00:29:46,720
Speaker 2: like off the rack infrastructure and capacity for us to

588
00:29:46,920 --> 00:29:49,520
Speaker 2: just sort of buy some time and space on a

589
00:29:49,760 --> 00:29:52,640
Speaker 2: line and sort of without having to do any infrastructure

590
00:29:52,640 --> 00:29:54,720
Speaker 2: investment ourselves, like have a chip brand.

591
00:29:55,280 --> 00:29:55,920
Speaker 3: Yeah? There.

592
00:29:56,520 --> 00:29:59,280
Speaker 4: So we call these companies coe packers, the same way

593
00:29:59,280 --> 00:30:02,280
Speaker 4: you would use for for beverage producers. And there was

594
00:30:02,320 --> 00:30:04,280
Speaker 4: a fun example I ran into the other day of

595
00:30:04,560 --> 00:30:07,920
Speaker 4: a company like this deciding they wanted to make a

596
00:30:08,040 --> 00:30:12,560
Speaker 4: pea protein mix of a cheeto. Right, the company had

597
00:30:12,560 --> 00:30:16,360
Speaker 4: this idea, this manufacturer built it and lo and behold,

598
00:30:16,400 --> 00:30:20,880
Speaker 4: Now you can buy pedos at costco. So they sold

599
00:30:20,880 --> 00:30:23,040
Speaker 4: this to Costco, had this idea, found this company to

600
00:30:23,040 --> 00:30:26,560
Speaker 4: make it, and the production facility was there for them.

601
00:30:26,600 --> 00:30:30,920
Speaker 1: So I'm sorry, I'm losing it a little bit at Sorry.

602
00:30:31,360 --> 00:30:32,920
Speaker 3: I know that's I wanted to bring it up because

603
00:30:32,960 --> 00:30:34,840
Speaker 3: I love the name. I thought it was so clever.

604
00:30:38,240 --> 00:30:41,320
Speaker 1: I would try to think, where did go next? Okay,

605
00:30:41,680 --> 00:30:48,080
Speaker 1: you've given pta? Sorry, what's the next big thing in

606
00:30:48,200 --> 00:30:56,160
Speaker 1: snack food manufacturer? Sorry? I'm so sorry, that's a serious question.

607
00:30:56,240 --> 00:30:59,240
Speaker 1: What Okay, So you walked us through all the technological

608
00:30:59,480 --> 00:31:03,920
Speaker 1: enhancements that have enabled us to develop these new types

609
00:31:03,960 --> 00:31:07,520
Speaker 1: of flavors and manufacture them in a more efficient and

610
00:31:07,760 --> 00:31:11,240
Speaker 1: faster way. You know, things like sensors detecting cleanliness, the

611
00:31:11,280 --> 00:31:14,920
Speaker 1: ability to observe the chips that you are producing for

612
00:31:15,080 --> 00:31:18,640
Speaker 1: quality in a sort of automated way. What's next? What's

613
00:31:18,720 --> 00:31:20,959
Speaker 1: really exciting in terms of that technology.

614
00:31:21,320 --> 00:31:24,080
Speaker 4: I think the thing that's moving the technology along now

615
00:31:24,200 --> 00:31:27,719
Speaker 4: is the movement of robotics into the production process in

616
00:31:27,800 --> 00:31:30,600
Speaker 4: as many many places as we can get it. So

617
00:31:31,080 --> 00:31:33,280
Speaker 4: where I'm starting to see it is so you would

618
00:31:33,880 --> 00:31:36,720
Speaker 4: now see robotic arms and most manufacturing facilities at the

619
00:31:36,840 --> 00:31:40,080
Speaker 4: end doing the palatizing process. So taking the finished boxes,

620
00:31:40,280 --> 00:31:42,160
Speaker 4: it's a big robot arm picks them up, puts them

621
00:31:42,160 --> 00:31:44,720
Speaker 4: on a pallette, arranges the palette, and then sends it

622
00:31:44,760 --> 00:31:47,720
Speaker 4: off to the forklifts or automated guided vehicle back to

623
00:31:47,760 --> 00:31:50,520
Speaker 4: the warehouse. Right, and then we're starting to move where

624
00:31:51,720 --> 00:31:54,800
Speaker 4: now we're even using robots to start picking packs of

625
00:31:54,840 --> 00:31:57,960
Speaker 4: things or stacking different parts of the process to get them,

626
00:31:58,000 --> 00:32:00,440
Speaker 4: you know, stacked ten rice cakes and get them in

627
00:32:00,440 --> 00:32:01,880
Speaker 4: a line so they can get wrapped and move to

628
00:32:01,920 --> 00:32:05,280
Speaker 4: the process. So when you start to think of the

629
00:32:05,320 --> 00:32:09,240
Speaker 4: technological innovation in a production line, you want to work backwards.

630
00:32:09,720 --> 00:32:12,920
Speaker 4: So you can start at the warehouse because you'll never

631
00:32:12,920 --> 00:32:15,400
Speaker 4: have a problem where you've created a bottleneck because you're

632
00:32:15,400 --> 00:32:17,960
Speaker 4: already at the end. Then you can move to the

633
00:32:18,000 --> 00:32:21,960
Speaker 4: palatizing process because in the wrapping process, because that's also

634
00:32:22,000 --> 00:32:24,840
Speaker 4: the end. Now we're starting to move further and further

635
00:32:24,880 --> 00:32:27,240
Speaker 4: through the production line, and we're having robots that move

636
00:32:27,680 --> 00:32:30,400
Speaker 4: alarmingly fast as you stand next to them, that are

637
00:32:30,400 --> 00:32:33,080
Speaker 4: doing a lot of these processes that maybe weren't done

638
00:32:33,120 --> 00:32:36,520
Speaker 4: by hand. But we're done with a series of blocks

639
00:32:36,520 --> 00:32:39,920
Speaker 4: and conveyors where a line would make the product turn

640
00:32:40,040 --> 00:32:41,440
Speaker 4: three times and then it would get it into the

641
00:32:41,520 --> 00:32:44,360
Speaker 4: right position to get into a bag and get everything moving.

642
00:32:44,640 --> 00:32:47,000
Speaker 4: And the speed that we're creating on the back end

643
00:32:47,080 --> 00:32:50,000
Speaker 4: has driven a lot of the speed demand on the

644
00:32:50,000 --> 00:32:54,440
Speaker 4: front end now because we've got more production capabilities and

645
00:32:54,520 --> 00:32:55,840
Speaker 4: we are ready to move.

646
00:32:56,240 --> 00:32:57,520
Speaker 3: And when I think.

647
00:32:57,400 --> 00:33:00,320
Speaker 4: About the future, it's just going to be more of

648
00:33:00,360 --> 00:33:04,920
Speaker 4: this variety. I think that the food manufacturers have looked

649
00:33:05,040 --> 00:33:09,160
Speaker 4: at things like ozembic as a challenge and the way

650
00:33:09,160 --> 00:33:11,440
Speaker 4: that they're going to tackle it us through novelty. Right,

651
00:33:12,280 --> 00:33:14,280
Speaker 4: Nesley just came out and talked about that we are

652
00:33:14,320 --> 00:33:15,120
Speaker 4: going to innovate.

653
00:33:15,280 --> 00:33:16,840
Speaker 3: And I don't know how.

654
00:33:18,720 --> 00:33:21,400
Speaker 2: Oh zempig is going to kill our demand for snack food,

655
00:33:21,440 --> 00:33:22,920
Speaker 2: but the way they're going to spond is by just

656
00:33:23,080 --> 00:33:28,280
Speaker 2: such chantalizing, incredible creative flavors that it will overwhelm the

657
00:33:28,680 --> 00:33:31,680
Speaker 2: medicinal suppression of our appetites. I'm excited for this cat

658
00:33:31,720 --> 00:33:35,440
Speaker 2: and most battle of whether they could just shear innovate

659
00:33:35,480 --> 00:33:37,360
Speaker 2: their way back to consumer demand.

660
00:33:38,080 --> 00:33:39,720
Speaker 3: It's worked for me so far.

661
00:33:41,480 --> 00:33:44,440
Speaker 1: I'm just thinking this is such a thematic all thoughts

662
00:33:44,520 --> 00:33:47,880
Speaker 1: episode because we talked about palettes chips, although a different

663
00:33:47,960 --> 00:33:51,040
Speaker 1: kind of chip and then we're also talking about ozembic

664
00:33:51,240 --> 00:33:55,600
Speaker 1: and automation processes. But Ryan, you alluded to this earlier.

665
00:33:55,640 --> 00:33:59,040
Speaker 1: But what is the labor picture like right now for

666
00:33:59,440 --> 00:34:02,800
Speaker 1: snack food manufacturing? And one of the reasons I ask

667
00:34:03,120 --> 00:34:06,320
Speaker 1: is I don't think my husband will mind me mentioning this,

668
00:34:06,440 --> 00:34:09,359
Speaker 1: but one of his after school jobs in the UK

669
00:34:09,880 --> 00:34:13,440
Speaker 1: was packaging chips and he said it was the most

670
00:34:13,480 --> 00:34:18,960
Speaker 1: boring job and tedious job that he's ever done. And

671
00:34:19,160 --> 00:34:20,680
Speaker 1: I think he did that for quite a while and

672
00:34:20,760 --> 00:34:23,680
Speaker 1: just spent hours and hours putting like bags of chips together.

673
00:34:24,560 --> 00:34:27,600
Speaker 1: But what is the labor picture like right now? How

674
00:34:27,680 --> 00:34:31,120
Speaker 1: easy is it to find workers for this type of job.

675
00:34:31,520 --> 00:34:35,080
Speaker 4: It's very difficult to find workers that want to staff

676
00:34:35,120 --> 00:34:39,120
Speaker 4: these facilities. It's a boring job and it's a stressful job.

677
00:34:39,160 --> 00:34:42,000
Speaker 4: Sometimes you know things are going down, you're getting hollered at,

678
00:34:42,080 --> 00:34:44,520
Speaker 4: you're holding up production depending on what you're doing, and

679
00:34:44,560 --> 00:34:47,279
Speaker 4: you may not know enough about the process, especially now

680
00:34:47,360 --> 00:34:50,279
Speaker 4: where the machines have gotten so difficult. It's not a

681
00:34:50,320 --> 00:34:53,719
Speaker 4: push button anymore. It's a iPad or it's a touch

682
00:34:53,760 --> 00:34:56,160
Speaker 4: screen that you have on the line that is controlling

683
00:34:56,200 --> 00:34:58,360
Speaker 4: things and you might need to click through several screens

684
00:34:58,400 --> 00:35:01,600
Speaker 4: and make adjustments or start or stop something to cycle

685
00:35:01,640 --> 00:35:04,239
Speaker 4: things to get everything back running. To go back to

686
00:35:04,280 --> 00:35:06,480
Speaker 4: our two thousand and four example, what you might have

687
00:35:06,520 --> 00:35:09,840
Speaker 4: seen in the past is staff at every single machine center.

688
00:35:10,600 --> 00:35:12,319
Speaker 4: So you would have someone at the fryer, you would

689
00:35:12,360 --> 00:35:15,480
Speaker 4: have someone at the bagger, at the palatizing station, someone

690
00:35:15,560 --> 00:35:18,040
Speaker 4: might be putting chips in a box like you mentioned Tracy.

691
00:35:18,880 --> 00:35:21,359
Speaker 4: And now what we're seeing is we can probably have

692
00:35:21,400 --> 00:35:25,800
Speaker 4: an operator controlling two to three lines potentially on an iPad,

693
00:35:26,000 --> 00:35:29,319
Speaker 4: walking around making sure that nothing's breaking, looking at and

694
00:35:29,360 --> 00:35:33,319
Speaker 4: acknowledging alarms on their system. And when we talk about

695
00:35:33,400 --> 00:35:38,319
Speaker 4: things like robotics in the palatizing station, so typically you're

696
00:35:38,320 --> 00:35:41,120
Speaker 4: going to use two to three people per shift to

697
00:35:41,160 --> 00:35:43,479
Speaker 4: do palatizing. If you don't have a robot there, someone's

698
00:35:43,520 --> 00:35:46,640
Speaker 4: stacking boxes or you've got a machine and what you're doing.

699
00:35:46,760 --> 00:35:50,279
Speaker 4: So one robotic arm eliminates somewhere between six and nine

700
00:35:50,360 --> 00:35:55,000
Speaker 4: jobs for a three shift factory per line. So these

701
00:35:55,040 --> 00:35:59,759
Speaker 4: products and these processes are removing operators. But out of this,

702
00:36:00,360 --> 00:36:02,799
Speaker 4: really these jobs are very difficult to hire for and

703
00:36:02,920 --> 00:36:05,640
Speaker 4: are always at a staff shortage. Every factor I walk

704
00:36:05,680 --> 00:36:09,000
Speaker 4: into as a hiring sign. We can interview today, you know.

705
00:36:09,480 --> 00:36:12,480
Speaker 2: So speaking of robotics, you know, one of the things

706
00:36:12,560 --> 00:36:15,719
Speaker 2: that i've sort of actually there was an interview with

707
00:36:16,040 --> 00:36:19,200
Speaker 2: Jensen Wong, the CEO of in video about this and

708
00:36:19,200 --> 00:36:22,200
Speaker 2: I've heard this from else is that the power of

709
00:36:22,280 --> 00:36:25,400
Speaker 2: robots will really be amplified with AI and I imagine

710
00:36:25,440 --> 00:36:28,160
Speaker 2: and the idea is right like that if you have

711
00:36:28,239 --> 00:36:31,120
Speaker 2: some sort of robotic arm picking up a bag, it

712
00:36:31,160 --> 00:36:33,200
Speaker 2: helps if there it has like the bag is at

713
00:36:33,200 --> 00:36:35,960
Speaker 2: maybe like a forty five degree angle that it can't

714
00:36:36,000 --> 00:36:38,160
Speaker 2: just like it has to like shift a little differently

715
00:36:38,280 --> 00:36:40,879
Speaker 2: or something like that. Can you talk a little bit

716
00:36:40,920 --> 00:36:45,640
Speaker 2: about that more like how AI or maybe machine learning

717
00:36:45,880 --> 00:36:49,080
Speaker 2: sort of makes it so that robots are becoming more efficient.

718
00:36:49,160 --> 00:36:51,880
Speaker 2: Is that something you see so that they can respond

719
00:36:51,960 --> 00:36:55,279
Speaker 2: to deviations or a weird chip shape comes through and

720
00:36:55,320 --> 00:36:58,000
Speaker 2: it doesn't automatically, you know, it's still able to pick

721
00:36:58,040 --> 00:36:59,480
Speaker 2: it up. Can you talk a little bit about this

722
00:36:59,520 --> 00:37:02,480
Speaker 2: sort of intersection of AI and robotics and whether you

723
00:37:02,520 --> 00:37:04,000
Speaker 2: see that as a force amplifier.

724
00:37:04,560 --> 00:37:07,440
Speaker 4: Machine learning certainly when we're talking about it is already

725
00:37:07,440 --> 00:37:10,359
Speaker 4: being used and you'd be surprised how often a very

726
00:37:10,360 --> 00:37:13,400
Speaker 4: slight tilt in something will disrupt a robot entirely. So

727
00:37:13,480 --> 00:37:16,080
Speaker 4: that does make sense. It's looking for a very specific shape.

728
00:37:16,120 --> 00:37:18,840
Speaker 4: Sometimes you have to really build the process out in

729
00:37:18,840 --> 00:37:19,239
Speaker 4: front of it.

730
00:37:19,280 --> 00:37:21,719
Speaker 2: Because this is what I've heard from someone recent This

731
00:37:21,800 --> 00:37:23,680
Speaker 2: is what I heard from someone recently about why we

732
00:37:23,719 --> 00:37:27,080
Speaker 2: still don't have, for example, like robotic like pizzerias, which

733
00:37:27,120 --> 00:37:28,880
Speaker 2: is just that they're still at the point where like

734
00:37:29,080 --> 00:37:31,239
Speaker 2: if there's like if one of the pies is like

735
00:37:31,280 --> 00:37:34,000
Speaker 2: a little bit smaller or a little bit bigger, et cetera, like,

736
00:37:34,239 --> 00:37:39,160
Speaker 2: the robots still aren't nimble enough or agile enough that

737
00:37:39,280 --> 00:37:41,640
Speaker 2: they would be able to respond, and then that would

738
00:37:41,680 --> 00:37:44,040
Speaker 2: break the whole process. And so the argument is, well,

739
00:37:44,040 --> 00:37:46,279
Speaker 2: if you have some sort of AI sensor vision and

740
00:37:46,320 --> 00:37:49,719
Speaker 2: stuff like that, then in theory it can respond to

741
00:37:49,760 --> 00:37:51,000
Speaker 2: these deviations faster.

742
00:37:51,480 --> 00:37:54,520
Speaker 4: Yeah, right now, where we are at with robots is

743
00:37:54,560 --> 00:37:57,400
Speaker 4: that you'll see when they start to make deviations. Sometimes

744
00:37:57,440 --> 00:37:59,839
Speaker 4: they can respond for the first few issues, but they

745
00:37:59,880 --> 00:38:03,200
Speaker 4: have trouble coming back to a home position to reset

746
00:38:03,239 --> 00:38:06,160
Speaker 4: when the product is good again. So they can sometimes

747
00:38:06,160 --> 00:38:08,279
Speaker 4: function for up to an hour in a sort of

748
00:38:08,600 --> 00:38:11,840
Speaker 4: failing state, and then they fail entirely. And then the

749
00:38:11,840 --> 00:38:14,040
Speaker 4: process has to be they have to be put back

750
00:38:14,080 --> 00:38:16,799
Speaker 4: onto where they are in the grid and relearn where

751
00:38:16,800 --> 00:38:19,080
Speaker 4: they are, and then you can fire it back up.

752
00:38:19,880 --> 00:38:22,839
Speaker 4: So with robotics, we still have a lot of limitations there,

753
00:38:22,880 --> 00:38:26,000
Speaker 4: and I think those limitations don't have to exist, But

754
00:38:26,360 --> 00:38:28,799
Speaker 4: for the mass produced robots that we're using right now

755
00:38:28,840 --> 00:38:31,839
Speaker 4: in production facilities, we still have those limitations. So I

756
00:38:31,920 --> 00:38:35,959
Speaker 4: don't think Jensen is wrong that we could start using

757
00:38:36,000 --> 00:38:37,960
Speaker 4: AI in these processes, and I think.

758
00:38:37,880 --> 00:38:40,960
Speaker 3: Robotics is probably the first place to look when we

759
00:38:41,000 --> 00:38:42,280
Speaker 3: talk about machine learning.

760
00:38:43,080 --> 00:38:45,840
Speaker 4: We're using that all the time, where we're a closed

761
00:38:45,840 --> 00:38:49,840
Speaker 4: loop process that the machine can determine. Dairies are especially

762
00:38:49,840 --> 00:38:53,600
Speaker 4: good applications for it. Cheesemaking, where you're heating the product

763
00:38:53,680 --> 00:38:56,600
Speaker 4: and moving it through. You're able to adjust times depending

764
00:38:56,600 --> 00:38:59,759
Speaker 4: on variations of the product, and you can visualize what

765
00:38:59,800 --> 00:39:02,520
Speaker 4: an ideal product would look like and know that and

766
00:39:02,560 --> 00:39:04,880
Speaker 4: make make adjustments. With all the different sensing units you have,

767
00:39:04,920 --> 00:39:07,120
Speaker 4: You're like, I need this, I need this moisture content,

768
00:39:07,200 --> 00:39:08,400
Speaker 4: I need this temperature.

769
00:39:08,480 --> 00:39:09,799
Speaker 3: I need it to get to at least this.

770
00:39:10,000 --> 00:39:11,960
Speaker 4: It needs to be this color, but it's not this

771
00:39:12,120 --> 00:39:15,840
Speaker 4: continue like all that's happening, but we are not to

772
00:39:15,880 --> 00:39:18,960
Speaker 4: a point where we're letting anything sort of make decisions

773
00:39:18,960 --> 00:39:19,399
Speaker 4: on its own.

774
00:39:20,080 --> 00:39:22,160
Speaker 2: By the way, there are a bunch more blue diamond

775
00:39:22,239 --> 00:39:25,560
Speaker 2: almonds than I said before, So anyway, there's various chocolate

776
00:39:25,640 --> 00:39:27,520
Speaker 2: covered ones too. All Right, I have one last question.

777
00:39:27,760 --> 00:39:30,800
Speaker 2: One thing we haven't talked about is the distribution side,

778
00:39:30,840 --> 00:39:34,000
Speaker 2: because I have to imagine that, Like, Okay, it's great

779
00:39:34,000 --> 00:39:37,480
Speaker 2: if the manufacturer can quickly change over their lines and

780
00:39:37,840 --> 00:39:40,640
Speaker 2: reduce not have to worry about downtime as much when

781
00:39:40,640 --> 00:39:43,680
Speaker 2: they're shifting from one stable process to another, But then

782
00:39:43,719 --> 00:39:46,600
Speaker 2: you have this explosion of skews, and I have to

783
00:39:46,640 --> 00:39:49,080
Speaker 2: imagine that makes a warehouse you know you have before

784
00:39:49,080 --> 00:39:50,759
Speaker 2: you might have had a warehouse that had two kinds

785
00:39:50,760 --> 00:39:53,560
Speaker 2: of freidos, and now you have a warehouse or probably

786
00:39:53,800 --> 00:39:56,400
Speaker 2: you know, one hundred kinds. What is required on the

787
00:39:56,440 --> 00:40:00,600
Speaker 2: sort of distribution side to handle the explosion of variety

788
00:40:00,719 --> 00:40:01,720
Speaker 2: of snack food.

789
00:40:01,960 --> 00:40:04,719
Speaker 4: Yeah, so we're seeing the rise of fully automated warehousing.

790
00:40:04,960 --> 00:40:07,239
Speaker 4: And essentially what a fully automated warehouse looks like when

791
00:40:07,280 --> 00:40:10,520
Speaker 4: you're dealing with Pallette sized product is you're going to

792
00:40:10,520 --> 00:40:12,680
Speaker 4: have a series of conveyors and you're going to build

793
00:40:12,719 --> 00:40:16,560
Speaker 4: it up in a multi story elevator situation, and you'll

794
00:40:16,560 --> 00:40:20,160
Speaker 4: have carts that are not operating dissimilarly to what you

795
00:40:20,200 --> 00:40:22,799
Speaker 4: would have in an Amazon warehouse where they're picking and

796
00:40:22,840 --> 00:40:26,040
Speaker 4: placing something and going, but they're taking small tots right

797
00:40:26,320 --> 00:40:28,640
Speaker 4: and we're dealing with palettes, so we're almost running it

798
00:40:28,719 --> 00:40:32,759
Speaker 4: like trains and elevators, moving to certain locations that are

799
00:40:32,800 --> 00:40:37,160
Speaker 4: then visible on a three D grid for the warehouse operator.

800
00:40:37,239 --> 00:40:39,240
Speaker 4: So it's typically we can have one or two people

801
00:40:39,239 --> 00:40:42,640
Speaker 4: operating this warehouse now and calling down for the packages

802
00:40:42,680 --> 00:40:45,319
Speaker 4: they need in an automated way, so saying, we're going

803
00:40:45,320 --> 00:40:48,160
Speaker 4: to fill this truck with these pallets in this order

804
00:40:48,280 --> 00:40:50,279
Speaker 4: so that when we get to the various stops that's

805
00:40:50,280 --> 00:40:52,840
Speaker 4: going to make it unloads in the proper order, and

806
00:40:52,960 --> 00:40:56,279
Speaker 4: all these different pallets are located in beIN one point

807
00:40:56,239 --> 00:40:59,760
Speaker 4: fourteen on floor five of our system and pulling through

808
00:41:00,480 --> 00:41:04,040
Speaker 4: where in the past at right now. In a sort

809
00:41:04,080 --> 00:41:06,840
Speaker 4: of a traditional warehouse, you'll have a sticker on the

810
00:41:06,840 --> 00:41:08,560
Speaker 4: outside of a palette and someone drives up with a

811
00:41:08,600 --> 00:41:10,680
Speaker 4: forklift and scans it and then pulls it in based

812
00:41:10,680 --> 00:41:13,319
Speaker 4: on what order they're pulling something from and it's a

813
00:41:13,360 --> 00:41:16,239
Speaker 4: lot more hectic to deal with multiple skews, and you

814
00:41:16,280 --> 00:41:19,319
Speaker 4: see a lot of forklift drivers moving palettes to get

815
00:41:19,360 --> 00:41:22,320
Speaker 4: to a palette behind it because of something that happened

816
00:41:22,360 --> 00:41:25,000
Speaker 4: where you don't see that in an automated situation.

817
00:41:25,960 --> 00:41:28,560
Speaker 2: Tracy, are you convinced now that we are indeed in

818
00:41:28,640 --> 00:41:30,480
Speaker 2: the Golden Ager snack vision.

819
00:41:30,520 --> 00:41:33,920
Speaker 1: In terms of technology? Yes, yes, well, and I am

820
00:41:34,160 --> 00:41:37,120
Speaker 1: very intrigued by the idea that maybe in an era

821
00:41:37,320 --> 00:41:41,280
Speaker 1: where people are eating less, if ozepic and the golp

822
00:41:41,400 --> 00:41:45,160
Speaker 1: one drugs become even more popular, that food companies are

823
00:41:45,160 --> 00:41:47,799
Speaker 1: going to have to offset that loss of consumption with

824
00:41:48,120 --> 00:41:52,360
Speaker 1: creating new and tempting varieties of things to try. However,

825
00:41:53,080 --> 00:41:57,040
Speaker 1: I will say not all of the flavors are evenly distributed.

826
00:41:57,160 --> 00:42:00,480
Speaker 1: And you and I were experimenting with this earlier, trying

827
00:42:00,520 --> 00:42:04,560
Speaker 1: to order particularly exotic varieties of stuff, and you can't

828
00:42:04,640 --> 00:42:07,880
Speaker 1: find them everywhere. You can get most of the things

829
00:42:07,920 --> 00:42:11,360
Speaker 1: off Amazon, but they're not in every grocery store. And also,

830
00:42:11,560 --> 00:42:14,640
Speaker 1: like prices are still pretty high. Like I don't know

831
00:42:14,760 --> 00:42:18,640
Speaker 1: if you've bought Lays or something recently, but it's kind

832
00:42:18,640 --> 00:42:20,600
Speaker 1: of insane how much it costs nowadays.

833
00:42:20,840 --> 00:42:23,840
Speaker 2: By the way, there's a lote Mexican street corn flavored

834
00:42:23,920 --> 00:42:26,080
Speaker 2: elements which you can buy from Blue Diamond. I'm gonna

835
00:42:26,080 --> 00:42:29,000
Speaker 2: buy them. Ryan Harlan, thank you so much for coming

836
00:42:29,000 --> 00:42:33,560
Speaker 2: on Odd Lacks. That was an absolutely fascinating conversation and

837
00:42:33,600 --> 00:42:36,879
Speaker 2: a great explainer of how technological innovation in the back

838
00:42:36,960 --> 00:42:39,640
Speaker 2: end truly gives us this wonderful world of variety. So

839
00:42:39,680 --> 00:42:41,040
Speaker 2: thank you so much. That was a lot of fun.

840
00:42:41,200 --> 00:42:42,000
Speaker 3: Thank you for having me.

841
00:42:42,200 --> 00:42:57,880
Speaker 1: Thanks Ryan, I've never been more hungry. Same Joe. You know,

842
00:42:57,920 --> 00:43:01,319
Speaker 1: you mentioned the Mexican street corn flavor almonds, so this

843
00:43:01,480 --> 00:43:03,719
Speaker 1: reminds me. I was in the supermarket the other day

844
00:43:03,760 --> 00:43:07,040
Speaker 1: and I saw there were Mexican street corn a lotave

845
00:43:07,120 --> 00:43:11,439
Speaker 1: flavored Cheetos, but and I was I was tempted. Okay, yes,

846
00:43:11,680 --> 00:43:14,239
Speaker 1: but I didn't buy them because I think it was

847
00:43:14,360 --> 00:43:17,840
Speaker 1: like eight or nine bucks for a pack a package,

848
00:43:17,840 --> 00:43:20,440
Speaker 1: and I just couldn't. I couldn't justify it, like the

849
00:43:20,480 --> 00:43:22,080
Speaker 1: novelty value.

850
00:43:22,239 --> 00:43:24,200
Speaker 2: Just like maybe a one off though. Tracy, by the way,

851
00:43:24,239 --> 00:43:27,719
Speaker 2: I did order some kit Kats and Dorito's before this,

852
00:43:27,800 --> 00:43:29,759
Speaker 2: so maybe Tracy, you can, you know, and I'll bring

853
00:43:29,800 --> 00:43:31,320
Speaker 2: them into the office.

854
00:43:31,200 --> 00:43:34,560
Speaker 1: You'll subsidize my chip cons I'm saying, now you get

855
00:43:34,640 --> 00:43:37,080
Speaker 1: those just like a good industrial policy.

856
00:43:37,200 --> 00:43:39,880
Speaker 2: Shit return return the favor of me buying.

857
00:43:40,640 --> 00:43:42,400
Speaker 1: I will bring you, Actually, I will bring you some

858
00:43:42,480 --> 00:43:45,040
Speaker 1: of those Korean almonds, because now that we've been talking

859
00:43:45,080 --> 00:43:46,400
Speaker 1: about them, I really want to go get some.

860
00:43:46,640 --> 00:43:48,879
Speaker 2: But just this idea, so like, there were so many

861
00:43:48,920 --> 00:43:51,279
Speaker 2: fascinating things in the conversation. For one thing, I just

862
00:43:51,400 --> 00:43:54,239
Speaker 2: loved like how clear Ryan was about explaining how all

863
00:43:54,280 --> 00:43:57,320
Speaker 2: these processes work. That was very cool. But then you think, Okay,

864
00:43:57,320 --> 00:44:00,279
Speaker 2: you have this factory and you try to get some

865
00:44:00,320 --> 00:44:03,239
Speaker 2: sort of like maximum like ninety nine percent up time

866
00:44:03,360 --> 00:44:07,560
Speaker 2: or maximum groupput. You can understand once you've achieved a

867
00:44:07,560 --> 00:44:11,799
Speaker 2: certain level of throughput, why you'd be really hesitant under

868
00:44:11,840 --> 00:44:14,799
Speaker 2: the old regime two thousand and four to like let's

869
00:44:14,840 --> 00:44:15,600
Speaker 2: try something new.

870
00:44:15,719 --> 00:44:18,799
Speaker 1: Yeah, this is what I find really fascinating. So the

871
00:44:18,920 --> 00:44:22,440
Speaker 1: idea that in this one respect, in the world of

872
00:44:22,440 --> 00:44:26,960
Speaker 1: snack foods, it feels like technology has enabled risk taking. Yeah,

873
00:44:27,000 --> 00:44:29,799
Speaker 1: and so often we kind of see it go the

874
00:44:29,880 --> 00:44:33,480
Speaker 1: other way, like people's perfect stuff and then they kind

875
00:44:33,520 --> 00:44:36,560
Speaker 1: of like double down on efficiencies and then they get

876
00:44:36,560 --> 00:44:40,520
Speaker 1: sort of like hardwired into doing one thing perfectly. It's

877
00:44:40,560 --> 00:44:44,120
Speaker 1: really interesting to me that in this world we're using

878
00:44:44,120 --> 00:44:48,359
Speaker 1: technology to enable variety in a more efficient and maybe

879
00:44:48,400 --> 00:44:49,040
Speaker 1: interesting way.

880
00:44:49,200 --> 00:44:51,759
Speaker 2: Totally, and I love like the simple example that you

881
00:44:51,760 --> 00:44:53,799
Speaker 2: don't think about, Like, Okay, let's say you have to

882
00:44:53,920 --> 00:44:56,160
Speaker 2: clean the pipes, which of course you do between different

883
00:44:56,160 --> 00:44:58,960
Speaker 2: flavor runs before you're like, well, the solution and making

884
00:44:59,000 --> 00:45:01,720
Speaker 2: sure you have clean is to just let the washing

885
00:45:01,760 --> 00:45:04,560
Speaker 2: process go along. So maybe thirty minutes, but what if

886
00:45:04,560 --> 00:45:06,440
Speaker 2: you could do it in twenty minutes and you just

887
00:45:06,480 --> 00:45:08,680
Speaker 2: those extra ten minutes are a waste and then you

888
00:45:08,719 --> 00:45:10,680
Speaker 2: can like, you know, it's only seems like ten minutes,

889
00:45:11,000 --> 00:45:13,040
Speaker 2: but what was it the rule of one percent? If

890
00:45:13,080 --> 00:45:15,920
Speaker 2: you can find twelve one percent efficiency gains, then you

891
00:45:15,960 --> 00:45:18,640
Speaker 2: can get an extra hour of throughput through factory, and

892
00:45:18,680 --> 00:45:20,399
Speaker 2: if you're doing hundreds of pallets.

893
00:45:20,200 --> 00:45:21,160
Speaker 1: Y, it's exponential.

894
00:45:21,520 --> 00:45:23,000
Speaker 2: Yeah, so it's pretty cool.

895
00:45:23,080 --> 00:45:27,200
Speaker 1: But also, you know, Ryan mentioned that game Factoria and

896
00:45:27,480 --> 00:45:29,759
Speaker 1: the idea that okay, it's a video game, it kind

897
00:45:29,760 --> 00:45:31,440
Speaker 1: of I've never played it, but it kind of looks

898
00:45:31,480 --> 00:45:35,200
Speaker 1: like some city but for factories, and you create your

899
00:45:35,239 --> 00:45:39,200
Speaker 1: own simulation of a manufacturing process. The idea that that's

900
00:45:39,239 --> 00:45:41,880
Speaker 1: actually happening in real life. So you can come up

901
00:45:41,920 --> 00:45:45,520
Speaker 1: with an idea and then you can use software to

902
00:45:45,800 --> 00:45:48,680
Speaker 1: basically figure out how you would produce this with your

903
00:45:48,760 --> 00:45:53,799
Speaker 1: current resources in the cheapest and fastest way. Presumably. That

904
00:45:53,960 --> 00:45:57,040
Speaker 1: seems really interesting too, and a big difference to I

905
00:45:57,040 --> 00:46:00,120
Speaker 1: guess how things were previously manufactured, where you would come

906
00:46:00,200 --> 00:46:02,120
Speaker 1: up with an idea, you'd have to do all this

907
00:46:02,239 --> 00:46:05,640
Speaker 1: research and development, lots of customer testing, but you'd also

908
00:46:05,719 --> 00:46:09,359
Speaker 1: have to talk to the factory floor and figure out

909
00:46:09,400 --> 00:46:11,600
Speaker 1: how you would make these new things.

910
00:46:11,800 --> 00:46:13,839
Speaker 2: Totally. I want to like see one of the first

911
00:46:13,840 --> 00:46:15,560
Speaker 2: of all, I want to play Factoria, or at least

912
00:46:15,600 --> 00:46:17,960
Speaker 2: check out the trailer. It looks good, it looks really fun.

913
00:46:18,360 --> 00:46:21,680
Speaker 2: And then also, yeah, this idea I think what digital

914
00:46:21,760 --> 00:46:23,879
Speaker 2: twin I think was the term huge. So you could

915
00:46:24,000 --> 00:46:26,840
Speaker 2: just create an entire digital version of the factory and

916
00:46:26,880 --> 00:46:28,960
Speaker 2: if the simulation is good enough, then you just run

917
00:46:29,000 --> 00:46:31,319
Speaker 2: the whole test in the digital twin. That's a really

918
00:46:31,320 --> 00:46:34,960
Speaker 2: cool idea. Also, this idea that like that point at

919
00:46:35,000 --> 00:46:36,840
Speaker 2: the end that robots still aren't very good, that if

920
00:46:36,880 --> 00:46:40,080
Speaker 2: you could eventually it becomes a problem if too many

921
00:46:40,120 --> 00:46:41,600
Speaker 2: of the bags are tilted and then they have to

922
00:46:41,600 --> 00:46:45,760
Speaker 2: be manually reset. Seems like there's potentially low hanging fruit there.

923
00:46:45,880 --> 00:46:48,160
Speaker 2: If I don't know, AI can make it so that

924
00:46:48,160 --> 00:46:51,720
Speaker 2: the robot can just automatically do whatever. That's really exciting.

925
00:46:51,800 --> 00:46:54,879
Speaker 2: Let's we should have Ryan back in twenty thirty four

926
00:46:55,040 --> 00:46:59,480
Speaker 2: to talk about the state the state of snack food manufacturing.

927
00:46:59,520 --> 00:47:02,480
Speaker 1: I'd be in that, and then maybe at that point

928
00:47:02,520 --> 00:47:05,720
Speaker 1: we really will be in the golden age of snack foods,

929
00:47:05,719 --> 00:47:09,279
Speaker 1: of cheap and abundant and interesting things to eat.

930
00:47:09,640 --> 00:47:10,040
Speaker 2: Can't wait?

931
00:47:10,120 --> 00:47:11,160
Speaker 1: All right? Shall we leave it there?

932
00:47:11,239 --> 00:47:12,000
Speaker 2: Let's leave it there.

933
00:47:12,120 --> 00:47:15,160
Speaker 1: This has been another episode of the Odlots podcast. I'm

934
00:47:15,200 --> 00:47:18,240
Speaker 1: Tracy Alloway. You can follow me at Tracy Alloway.

935
00:47:17,880 --> 00:47:20,680
Speaker 2: And I'm Joe Wisenthal. You could follow me at the Stalwart.

936
00:47:20,960 --> 00:47:24,840
Speaker 2: Follow our producers Carmen Rodriguez at Carmen erman Dashel Bennett

937
00:47:24,880 --> 00:47:28,400
Speaker 2: at Dashbot and Kelbrooks at Kelbrooks. Thank you to our

938
00:47:28,400 --> 00:47:31,920
Speaker 2: producer Moses Onam. For more Oddlots content, go to Bloomberg

939
00:47:31,920 --> 00:47:34,600
Speaker 2: dot com slash odd Lots. We have transcripts, a blog,

940
00:47:34,640 --> 00:47:37,440
Speaker 2: and a newsletter, and you can chat about these topics

941
00:47:37,480 --> 00:47:40,759
Speaker 2: and more with fellow listeners. In the discord, discord dot

942
00:47:40,800 --> 00:47:41,959
Speaker 2: gg slash.

943
00:47:41,640 --> 00:47:44,719
Speaker 1: Odd Logs and if you enjoy odd Lots, if you

944
00:47:44,760 --> 00:47:47,560
Speaker 1: want us to bring Ryan back on in twenty thirty four,

945
00:47:47,640 --> 00:47:50,480
Speaker 1: then please leave us a positive review on your favorite

946
00:47:50,480 --> 00:47:54,799
Speaker 1: podcast platform. And don't forget if you are a Bloomberg subscriber,

947
00:47:54,840 --> 00:47:58,120
Speaker 1: you can listen to all of our episodes absolutely add free.

948
00:47:58,200 --> 00:48:00,120
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Speaker 1: account with Apple Podcasts. Thanks for listening.