The Exchanges

Q How'd you get connected to them and what was the composition of the round? Was it mostly institutional? Was it strategic?

A A little bit of a, a little bit of both. Um, originally they actually reached out to me four or five years ago from the kind of industrial side because they wanted, um, to just learn about You know, new advancements of manufacturing technology, data and AI in manufacturing. So I just built a relationship with them of just giving advice to the industrial side. And then when they started the growth fund, started a discussion there, and it wasn't really until, you know, August last year, when I reached out and said, Hey, I think we're at the point where we're going to do around. Are you interested in, in looking at this? And that's when conversations, uh, really started. Uh, so in total, I mean, they were the lead, but we also had, uh, customers who invested. We also had, uh, Flat Capital, which is the investment company of the Klarna CEO and another partner, recurring capital partner who also came in and pretty much all existing, uh, investor had participated in a note prior to that, that converted into this round as well.

AI assessment note: “originally they actually reached out to me four or five years ago”

Q Right. And I'm curious. So are there specific manufacturing processes? I know you've mentioned a couple, um, are there specific manufacturing processes or industries that Odin is particularly well suited to address? And if so, could you share more examples of that?

A Yeah, it's really the ones that are kind of operator dependent. So like paper and pulp is a fantastic one. Really all types of plastics processing, like compounding of the actual material itself, extrusion, which is how you make anything that's long out of plastics. That includes wire and cable, packaging, building products. Um, we've started doing more and more metals. It's like metal drawing and different types of, uh, casting processes. Um, it tends to be a lot about how the data behaves in the process, because at the highest level, manufacturers care about the same thing. They want to know Utilization of machines, performance of machines, quality of the process, material consumption, and maybe some other efficiency metrics. And then underneath that kind of high level, we all care about the same thing. They have different like metrics associated with achieving those results. And so there are industries that are more Discreet in nature where like the data can be difficult to track from one step to the process ones from one step to another, uh, that makes it difficult for us to really show value on the whole stage. So that's why we tend to buy us more towards kind of continuous batch, continuous processes. Cause that's where we have a lot of variance, a lot of, um, a lot of cost of quality as well. Oftentimes it's also like the machine landscape needs to be pretty fragmented. …

AI assessment note: “paper and pulp is a fantastic one. Really all types of plastics processing”

Q Gotta love a perfect storm. So with this series B funding, what are you going to be, uh, investing in for your, yourself with the company? Like what, what are you going to be growing?

A A lot of exciting things. I think like there's, I'd put it in a couple of different categories. Um, one category is just scaling process AI. Um, and that means investing in the team and iteration on, you know, you know, continuing to iterate and advance that technology. But also in the delivery capability, the speed at which we can deliver it, um, the amount of recommendations we have available, the value of those recommendations, and just go to market for it. Um, the other categories that we're really focused on are what we call data enrichment. So how we actually use machine learning to fix some of the inherent problems with manufacturing data. And I can give a specific example, uh, cause since you're tying in data from multiple systems, those systems don't really have an accurate view of the world. And especially some of them might come from manual entry. So like you have one system that is reporting the raw values coming from the machines and you have another system where an operator is saying what is happening. Like I started product X at time, a, and I started product Y at time B and Every operator does that differently with like a variance of five to 30 minutes. And so if you just take that data, combine it with the time series data, it's garbage. Instead of having the operator do that, which is a non-value added task for the operator, we can have an inference model that…

AI assessment note: “one category is just scaling process AI... other categories that we're really focused on”

Q Could you unpack a little bit more, um, what the day-to-day role is so people have a better understanding of what's going on inside a manufacturing facility and what people are trying to ultimately get onboarded into and then run?

A Absolutely. Like at the highest level, a day in on a manufacturing floor should all be the same, but they never are. I heard customers always say that it's a little bit like Groundhog Day. Every day there's something different, like it should be the same, but everything is a little bit different. And you tend to have, you know, engineers that are there to solve big problems, make big improvements, new initiatives, and you have operations that are trying to get the, you know, Trains to run on time. And that's everything from a plant manager. Who's got the kind of helicopter view of the facility down to the operators who are, uh, you know, where the rubber meets the road on the line. And so they have work orders that they're going to run of different products that they're going to make. And they tend to have on a given line, maybe five to 15 different machines from different machine makers that they need to coordinate in order to make that product. And in the best case example, It just runs and they make things the same way all the time, but that isn't really the case because the lines are probably different. You know, one of them got a replacement part that changes some of the dynamics than another one. And so you end up with every product or every skew in order to be done as well as possible needs to be done differently on each line. Like some of them can run at 1500 feet per m…

AI assessment note: “plant manager. Who's got the kind of helicopter view... down to the operators”

Q about a survey, um, from twenty-twenty-three. So you surveyed around 300 manufacturing leaders on topics like labor, um, inflation, demand changes, and investing in sustainability. Did you have any particular findings from that, that you've reflected on in the last year that have like really proven to be true and, uh, have informed your opinion on the growth of the company and what you're going to be providing for customers?

A I think the, the labor one is the main one. I think, I mean, we've been around for, for quite a while and the labor issue was always A kind of frog in boiling water issue. Um, from like, 2014, 2015, when we started talking to customers, it was always an issue that they knew about, but it never became a big enough pain point that they had to act now. The pandemic changed that. Um, I think that changed people's expectations of jobs. I think the competition for labor went Elsewhere. I think the biggest problem for manufacturers when it comes to labor is that people are leaving the manufacturing industry as a whole. They might be going to work at an Amazon warehouse instead of, um, in operations. And so I think the labor one is, is the most consistent theme that we've seen in our kind of state of the manufacturing service. Everything else is kind of cyclical, whether it's, you know, sales constraint or, or production constraint. And we've seen insane cycles. Between like 2019, 20, 21, 20, 22. And now again, I think I saw last month, like this was the first time in a couple of years that the, like a supplier index and manufacturing went above 50 where they're actually seeing, you know, things are starting to go in the right direction. But that hasn't actually been bad for us because whether you're trying to make things faster or you're trying to make things cheaper, we can kind of, …

AI assessment note: “I think the, the labor one is the main one.”

Q Right. Is there a specific vertical within manufacturing that you go after? How did you set out on your current customer set and where are you looking to expand it to?

A Yep, absolutely. I'd say right now you can kind of think of Our industries, they tend to be what I call operator dependent. To give some specific examples, paper and pulp is a big one. Extrusion or plastic extrusion or a lot of plastics processing in general, chemical mixing. You can kind of think of manufacturing as two ends of the spectrum. You've got the most discrete processes in the world where you make, you know, individual units like a circuit board. You've got continuous processes like oil and gas or like petrochemical processing. We tend to fit somewhere in the middle. Whereas kind of batch continuous manufacturing where you've got a product mix, you've got a process that you want to optimize, and you've got high variability in the process in the product mix as well. Um, and a lot of these industries, if we take paper and pulp as an example, you've got huge cost of quality. Um, And you tend to have a pretty long lead time on knowing whether what you made was good or bad, because you take samples, you run into a lab, you find out hours later whether it was good or bad. So you end up in a situation where things are definitely running, and they're running well, but they're not running as well as they can, and especially you end up with a lot of variance in how different operators are doing things. And in that variance, you can find a lot of insights about how you can twea…

AI assessment note: “paper and pulp is a big one. Extrusion or plastic extrusion... chemical mixing.”