Evocon turns complex production data into clear, real-time insights with visual, user-friendly OEE software that empowers manufacturers to boost efficiency and eliminate waste. With fast plug-and-play installation and powerful dashboards, Evocon helps teams improve productivity, engage operators, and drive continuous improvement from day one.
In this episode, we speak with Martin Lääts, Head of Product at Evocon, about transforming production data into performance improvements using modern tools. Evocon, a visual-first OEE software platform, aims to provide real-time visibility into manufacturing processes. Martin discusses the company’s journey since its founding, emphasizing transparency in production and the importance of digital tools in modern manufacturing. The conversation covers the significance of Overall Equipment Effectiveness (OEE) as a key performance indicator, the challenges of translating data into actionable improvements, and the role of simplicity and user engagement in software adoption. Martin also shares insights on the evolving role of AI in manufacturing and the importance of operator involvement in implementing new systems.
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Show Notes
Takeaways
- The mission of Evocon has always been about transparency in production.
- OEE is a critical KPI that measures manufacturing efficiency.
- Data must be actively used to drive improvements in performance.
- Simplicity in software design enhances user engagement and results.
- Unexpected uses of Evocon can lead to significant cost savings.
- Operators play a vital role in the success of new systems.
- Younger workers are more engaged with user-friendly technology.
- Choosing software based solely on price can lead to poor outcomes.
- AI has potential in manufacturing but requires structured data.
- Immediate results can be seen after implementing Evocon.
Chapters
00:00 – Introduction to Evocon and OEE
02:57 – The Evolution of Factory Performance Management
05:47 – Understanding Overall Equipment Effectiveness (OEE)
11:45 – Challenges in Implementing OEE
14:33 – The Importance of Simplicity in Data Utilization
17:44 – Unexpected Uses of Evocon
20:27 – The Role of Operators in System Implementation
24:25 – Engaging the Younger Workforce
29:31 – Market Misconceptions and AI in Manufacturing
33:26 – Onboarding and Trial Process for Evocon
Transcript
Beau Hamilton (00:00.795)
Hello everyone and welcome to the SourceForge Podcast. I’m your host, Beau Hamilton. The topic we’ll be unpacking with today’s guest is how to turn production data into real performance and how the right tools make that all possible. With me is someone deeply embedded in that transformation and that is Martin Latz, head of product at Evocon. Now Evocon is a modern visual first overall equipment effectiveness software platform. That’s OEE for the acronym lovers out there.
Their software is designed to help manufacturing teams everywhere utilize the latest technology available today here in 2026 to get real time actionable visibility into their various processes. We’ve got a lot to talk about. We’re to talk about what’s broken on the shop floor, why so many quote unquote efficiency tools fail in real factories and how Evocon tries to get around those sort of pain points with their heavy emphasis on design, usability and real world adoption.
So we’ve got a lot to talk about. Let’s bring in Martin to share more about the company. Martin, welcome to the podcast. Glad you can join us.
Martin Lääts (01:03.576)
Welcome. Welcome, welcome. Thank you for having us.
Beau Hamilton (01:06.415)
So let’s begin. First of all, tell us about yourself. How are you doing? What’s your role at Evocon and what’s your journey been like so far?
Martin Lääts (01:15.15)
I’m doing great. But in terms of the journey, it’s been long. I’ve been involved with Evocon since 2013. it’s been a very rewarding journey doing different things, holding different roles and, most importantly, working with great people. So yeah, throughout the years I’ve, yeah, I’ve done marketing, sales, help with support, also design, but now recently transitioning more to the product strategy and making sure that our clients continue to get value out of the system and actually get to actions that have an impact.
Beau Hamilton (01:58.993)
That’s great. Yeah. So you were one of the kind of early, almost kind of founders or pioneers with the company. I the company’s been founded in 2010. Is that right?
Martin Lääts (02:08.398)
Yeah, the company initially was founded in 2010. The first version was developed in 2007. And then in 2016, we actually formed a new company, which I’m also a co-founder of. That’s the time when we transitioned to offering a cloud-based service. Initially, it was a on-premise service. So yeah, so that’s like the long story short.
Beau Hamilton (02:31.873)
Okay. Yeah, that’s exciting. So, you found it. So you kind of started this, you had a kind of concept for your business plan, the idea here in 2007, and officially were founded in 2010. And, know, kind of broadly speaking, to tackle a lot of the kind of production inefficiencies that were noticed at the time. Let’s elaborate on that. Can you talk about some of those challenges? What core production pains did Evocon originally set out to solve and then maybe you can talk about how the mission has sort of evolved over the last decade and a half?
Martin Lääts (03:09.742)
I think the mission hasn’t really changed because it’s always been about transparency. Answering the question of what is actually going on in production. I mean, that was the initial question that one of the founders had when they were working actually in the factory. So the question that he had was, so basically…
In short, why is the production output different for my night shift and my day shift? And there weren’t really straight answers because whenever he went to different people, they all gave their own version of it. So it wasn’t clear. And I mean, over the years and talking to many, many different customers in manufacturing, the question really is the same. So what’s really going on?
What it differs is maybe the question itself. So many actually served the same question that the founder had was why are my shifts performing differently? But it can also be a question of what is the actual uptime of my machines? What are the main causes of the machine stopping? Are my machines actually running at optimal speeds? So they all stem from the same pain point or the same kind of
same problem area, which is transparency. I don’t know what is actually going on and I need to, I want to know so that I can actually improve. yeah, so in short, the mission has stayed the same. What has evolved is us being able to shed light into different areas of this transparency question. So yeah, that is what has changed over the last decade.
Beau Hamilton (04:58.097)
Gotcha. Yeah. Well, and then, so the mission stayed the same, but it’s kind of utilizing, helping factories and shops utilize the latest technology. I mean, you mentioned the cloud kind of adoption, but obviously we’re in this AI revolution. And there’s just all sorts of different kind of technological breakthroughs and innovations that have happened over the last decade and a half, right?
probably helping companies utilize. I think there’s naturally sort of a moving goal goalpost when it comes to making factories more efficient. You mentioned this sort of like divide between daytime operations and nighttime operations. That sort of just brings me into the bigger picture of just where things are today. Could you help summarize the current state of factory performance management today in 2026? Like what does it look like?
Martin Lääts (05:56.81)
Well, it’s definitely changed in the past years. think COVID had quite a big impact. Whereas before, it was largely still pen and paper. I think today, the consensus is that we need to actually implement digital tools. So I think in 2025, we don’t have to actually sell the idea of
of using digital tools to optimize production anymore. It’s more so it’s kind of, it’s common sense. So I think that is a very hopeful sign for us. And I think it’s also impacted or reflected in our results because we’ve had strong growth in the past few years. But generally speaking, obviously it depends. Each team is different. Some teams have had previous experience with digital tools. So they are
maybe a bit more further down the road, but also a caveat, if they’ve failed in the past, then they’re more cautious. Whereas if anyone, any client is really coming from the pen and paper world, you know, they’re sort of vanilla in a good sense that they are actually, they are ready and set and they want to improve. So I think it really depends, but I would say it’s very hopeful. And I see, I see a great promise that
The inefficiencies can actually be rooted out by using the tools.
Beau Hamilton (07:28.849)
Absolutely. Yeah, I think you need to be thinking about kind of the very least be thinking about some of the platforms that are out there, like the one you guys offer and how they utilize it. I think there’s a lot of confusion about like maybe where to start, right? Now I wanna go kind of talk about that acronym OEE, Overall Equipment Effectiveness. I know that’s a new acronym for me. I know you’re quite familiar with it obviously and what it is concerned with.
Could you explain OEE in simple terms for our viewers who maybe might not be as familiar with the topic? are most manufacturers aware of it, you know, to begin with? And do they realize how important it is to track these efficiencies and kind of follow the principles of what OEE represents?
Martin Lääts (08:18.106)
OE is really the gold standard of KPIs in manufacturing. And in a nutshell, tells you percentage-wise how far you are from your theoretical maximum. So you have your resources, and these are machines, and usually OE is measured by a machine. So every machine has a theoretical maximum in terms of outputs. So that’s 100%.
and then usually the actual OE lands somewhere in between. So the way it’s calculated, it has three components. So it looks at basically how the machines are utilized from three different perspectives. There’s availability, there’s performance and quality. But I think it’s better if I also bring an example. So let’s imagine we have a bottling line that bottles soda.
So you choose whatever your favorite soda is. And let’s imagine we’re running a shift, a 10 hour shift, so quite a long one. And we aim to produce, or we know that the machine is capable of producing 1,000 bottles per hour. So that would mean that our maximum potential is 10,000 bottles for one shift. So that is, in that case, we would be 100%. And then…
When we start looking at the actual operation, then first we look at availability, which defines what was the actual working time of the machine. So 10 hours was our ideal, but let’s say there was a line stoppage for 45 minutes due to a breakdown. Then we had a 30 minute break for lunch, another 30 minutes for training and 15 minutes for cleaning.
And so that basically takes two hours of our time away. so we, we, for availability, we lose 2000 bottles. So that’s like the first component. Um, then, then we also look at performance. So imagining that the 1000 per per hour is the ideal maximum, but let’s say we had some slowdowns and we actually only were, were capable of producing, uh, 7,200 bottles. So.
Martin Lääts (10:41.134)
Math says that then performance is 90 % because 7200 divided by 8000 is 90%. So we lost another 800 bottles due to performance issues. And then finally, we also look at quality. So out of these 7200 bottles, how much we were actually able to were basically sellable. And let’s say then we rejected 100 due to they being underfilled.
And then another hundred to two crooked labels or faulty caps. And basically then we shared another 200 bottles from our ideal. And then we end up in that shift producing 7,000 bottles. So that means 7,000 out of 10,000 is basically 70%. So OEE then says that our utilization or our machine OEE was 70%.
and we lost 30 % of our capacity somewhere. And then we know the components. Now, many, many customers do know the importance, but I think where they err is that they stick to the number and they don’t really go deeper into what were the reasons behind the numbers. yeah.
Beau Hamilton (12:00.879)
Right. Yeah. No, that’s a, first of all, great explanation. I appreciate the example there. That does kind of help illustrate and paint a picture of what this does. And I think like, you’re totally right. think it’s, it’s, I imagine manufacturers are very aware of kind of some of the metrics and the numbers of output and, maybe the surface level of like the factors that influence how they’re able to maximize production. But maybe they don’t really understand how
powerful it is to really track it consistently and look at the root causes and get extrapolate and really get into the granularity of what’s causing many floor to not maximize their output. So that being said, even when companies do track it, think turning that into improvement isn’t always…
always super easy and straightforward. know many factories, they already collect a lot of data, but they struggle to improve performance. In your experience, working hands on and talking with lot of these clients and customers, where do OEE efforts most often break down or maybe fail to translate the data that’s collected into real sustainable improvement?
Martin Lääts (13:20.014)
I think it boils down to different reasons. But let’s say if someone is starting off and they don’t get the gains, it’s usually one of a core reasons. And one of the things that we’ve seen is that if there is no actual champion driving this improvement.
If you’re missing a person who’s taking ownership of this improvement process, it’s very unlikely to actually see the gains. And then another reason is actually not having, not taking the data and incorporating into like their processes. So data is only useful if it’s used and the best way to actually sustain improvement is to have it part of your processes. That means.
your daily meetings, your weekly meetings, your monthly meetings, you need to use it consistently to actually see improvement. So I think those two are really, really the core things that we see clients failing. And then there’s one, which is maybe a weird one is expecting change to happen without actually taking action. So we have behind me,
An example from one of our clients who previously just they had windows. So the picture actually is from that floor or from their office. they had windows into production, but they didn’t really see what was going on. So now the Evo Contact displays there are the actual windows into production. And so the third sort of mistake is…
is hoping that by putting up those screens, everything will magically improve. I mean, so it’s not really the case. You need to actually use the data. We can provide it, but the actions must be taken.
Beau Hamilton (15:32.805)
Yeah, so it’s one thing to collect the data, but you need to turn those numbers into something that’s actionable. And then I think that is a step forward, having those kind of seeing the data displayed. But again, you need to actually find a way to really utilize it. I mean, my mind goes to, it’s almost like relating it to like your personal kind of life. It’s like having a vision board of things you want to do and things you’re reminded of.
and goals you’re working towards, but you need to get a little bit, dive in a little bit deeper and actually kind of write down steps to what you’re doing to work on to achieve that actual goal and kind of get more, again, get more granular with the vision. I have a slight, I don’t know, I’m curious if the complexity is kind of an issue with making this kind of
data actionable. know you guys like in terms of with Evocon, you guys focus a lot on simplicity and ease of use. What are you, what are the benefits of that philosophy? But also talk about some of the maybe the cons too. And you don’t want to make your platform too, too simple to the point where it’s, it’s like glossing over important information, right? So how do you strike the right balance there?
Martin Lääts (16:50.914)
Yeah, absolutely. mean, simplicity and ease of use is fundamental because what we hold dearest is that if someone looks at their data or looks at it, they need to quickly understand what’s going on. Because if you have to spend too much time on actually then understanding what you’re looking at, then there’s likely that you don’t actually stick with the system.
So yeah, we’ve put a lot of effort into ease of use and simplicity. Obviously from my point of view, I see mostly pros to this because the hypothesis we have is that if the users want to use the system, then that means actions are being taken. So, and if actions are being taken, you can expect improvements. But if your people don’t want to use the system, then…
You know, it’s very unlikely that you get any results and also reduces time spent on training and just more, more time getting results. And at the end of the day, it’s cheaper for everyone. I, yeah. And I think it’s simplicity. Yeah. It’s a, it’s a word. It’s a tricky word because if, if you say a system is simple, then many might assume simplicity means
Beau Hamilton (18:02.737)
Yeah, no, that does make sense.
Martin Lääts (18:17.206)
not having the features or not having enough actionable data. No, but simplicity actually means that you take the complexity and you make it understandable. So that is really the essence of simplicity in my view. The con that you asked for also, obviously this means that we cannot do everything that our clients wish we would do.
Because simplicity also means that we keep the system standardized. So we cannot make a tailored solution for everyone. I mean, we don’t. So from the client perspective, the con is definitely that we don’t have, we don’t develop any or all features possible. We develop the features that actually have the impact.
Beau Hamilton (19:05.296)
Sure, but I’m sure you work with clients individually to help them maximize the most out of the platform with the tools you do have that I’m sure will be kind of more relevant based off of, for different companies, different industries will utilize certain tools more than others. Right? mean.
I feel like simplicity does feel pretty underrated though. I think that, again, yeah, it comes down to just having that detailed information, having the right tools to act on it without turning people off. You got to make it user friendly and simplicity goes hand in hand with that. beyond classic OE tracking, what are some of the maybe unexpected ways you’ve seen Evocon be used?
Martin Lääts (19:55.005)
The unexpected ways usually come from the clients who just like they’re hungry for getting improvements and they find ways of using the system in unconventional ways. so that is, those are the areas where we actually see sort of clients being really creative. But obviously there are some features that we’ve developed which are
more, let’s say loose or they have, they have functionality that, I think cater to the creative people, just like if, if you, if you have a problem, you just think of a way that you can solve the solution or can actually provide the solution. So there’s a Coca-Cola butler in Ben Africa who, who you use one of their features to, to track.
how much material was going in and then how much basically bottles were coming out. But that is not a, that is not a solution we, we, we sell. They just found a way to collect the data and use it. And what they discovered was that they were actually from their supplier getting less material that they were paying for. So it was like a very, very interesting find. And, and then there’s also like another interesting example is.
Beau Hamilton (21:15.749)
Yeah.
Martin Lääts (21:21.742)
So we have a small feature or yeah, I would say a small feature to be able to connect different process metrics into our system. So what that basically means is that you’re looking at a metric that is critical for your process. Let’s say you’re using gas for your production, gas uses per amount. So that client was comparing their production with their gas usage and they had
three identical lines producing tiles. But then what they found was that one of the lines was using, I think it was like 15 % more gas than the other ones. And thanks to that, they were actually able to detect a leak and fix the leak. But since they were running that line three shifts a day, 300 days a year,
And I think that what they say was that that leakage cost them around 2000 euros per shift. So if you do the simple calculation, that’s how I’m right. Almost 2 million euros lost. Yeah, it does that. And actually it’s a good example of small things adding up over the year. So any small thing you find about your shift and you fix it, then from a yearly perspective, it adds up quickly.
Beau Hamilton (22:29.925)
Wow, yeah, that really adds up. Yeah. Huh.
Beau Hamilton (22:47.387)
Totally. Yeah. mean, that’s what it’s what you’re all about. What platform like yours is all about, right? It’s just making like small kind of small improvements that add up and compound over time. mean, I think we’re kind of past the days where, you know, you’re making these like these like it’s like advancements that are like the equivalent of the printing press or the loom or something, you know, so you kind of have to work on these kind of smaller improvements, but small improvements that really add up and make big differences over time.
But I think that’s great with the, I love the examples there and it’s kind of fun to hear about how manufacturers are personalizing it away or making it their own, but also finding out some interesting observations. Now, what’s, I want to zoom in a little bit with the individuals utilizing the platform like yours. What’s the role of the operators on the shop floor when a new system like yours is being implemented and used?
Martin Lääts (23:17.176)
Yeah, absolutely.
Martin Lääts (23:42.254)
I mean, they are crucial. Without them, the system wouldn’t work. Because we collect the signals from the machines and we visualize them, but the operators are those who actually provide the context. basically behind me, the display that you can see has different colors, especially the red ones there, and they indicate
downtime. for example, there is not much use in knowing, I mean, there is use in knowing why the line stopped for let’s say two hours as I gave the example before, but you really need to know why. Because if you know the why, you can take an action. So getting them engaged and using the system is absolutely imperative. And also track
educating them on what OE is and why it’s tracked and how it can help. So yeah, I cannot stress their importance enough. They need to be respected and engaged.
Beau Hamilton (24:50.373)
Yeah, absolutely. know it might be the business leaders or the managers deciding what system to maybe embrace, but it’s the operators that are the ones actually have to learn the system. And they’re the ones that determine whether or not it sticks and is actually really utilized. And yeah, do, think for the YouTube viewers, it’s neat to see the background behind Martin and get a visual look at.
at what the platform looks like. I do really like kind of the dark theme there, the colors do help kind of, you know, show what information, the important information that stands out. Now, Martin, I know you’ve obviously been in industry long enough and you’ve worked with enough clients to understand kind of the key metrics for adoption and adopting a platform like yours. What have you learned about, you know, operator engagement? What actually like changes
behavior and motivates teams to use a new digital tool successfully.
Martin Lääts (25:50.83)
Well, the first and obvious thing is the ease of use. It needs to be, because an operator needs to keep their line running. As the example I gave is like, they need to work like an eight hour shift, a 10 hour shift. So if you go in there and say, hey, Bo, know, on top of that 10 hour shift, here’s a system that you need to use.
Beau Hamilton (26:18.704)
Right.
Martin Lääts (26:19.278)
You will use it if it makes your life easier or if it’s like really simple. So that is definitely one of the more important things that you need to have a system that they want to use. And I think there’s a change now generationally that I would say younger people are starting to also enter the manufacturing and they are, they have grown up with actual
like engaging apps and whatnot. So they don’t want to use something that is unsexy. put it this way. so yeah, simplicity is definitely one, treating operators with respect and, and giving them the ownership of the data collection process. That is also, absolutely crucial. I said before, training them and explaining why a tool is needed. That is actually a tool for them to report about actual issues.
Because we also see that at clients when they have no data monitoring or production monitoring culture in place, there’s a lot of finger pointing, you you did that or, your fault, not mine. But then actually that system will show, you know, what happened and when and why, and that is very important. And then there’s simple things of just rewarding them.
giving them praise for these small things. I remember a client in Columbia, it was not like a really high tech industry that they are operating in and they implemented just, think, a leaderboard and the best operator getting candy in the morning. And it had a huge impact. So it doesn’t need to be big or anything. You need to respect the operators and that goes a long way.
Beau Hamilton (28:14.203)
Totally.
Martin Lääts (28:14.414)
And then finally, I think, we’ve not only visually, but we’ve tried to implement a humanizing element into the system. So we have mystery evocon at every shift. You just glance at the display. In the background, it’s not visible, but in the corner, there’s mystery evocon. He either smiles, is neutral, or is sad, depending on basically how your shift is going. So giving that human touch is also very helpful.
Beau Hamilton (28:44.529)
Oh yeah, that’s, that is a great feature. mean, yeah, it kind of goes back to that, that affirmation. Like it’s, it’s amazing how just simple kind of words of affirmation recognition of, of a deployed doing a good job or, you know, the, team doing a good job goes, it goes a long ways, you know, and it’s like just basic kind of communication and, and, business dynamics, but it’s, it’s still often kind of forgotten, uh, these days, especially, especially with remote work, which I guess is not.
necessarily what’s pertaining to your industry so much. another interesting observation you mentioned was the younger workforce coming in and being more attuned to the UI and ease of use. then that’s interesting, though, is to about the new younger generation coming in. So you’ve noticed a bigger kind of a
maybe a better adoption and interaction with some of the software that you’ve implemented on the workforce with some of these younger workers.
Martin Lääts (29:49.934)
Yeah, I’ve heard stories from, you know, from our sales guys and the client facing team that it has been mentioned in the, know, when they’re doing the implementation or even getting after the trial that, you know, people expect something that is engaging. And it has also come up when we’ve had, you know, we’ve been head to head with another system or multiple systems at a client. Then that gets mentioned because, you know,
Beau Hamilton (29:57.659)
Yeah.
Martin Lääts (30:20.248)
Yeah. People want to use something that is, you know, engaging.
Beau Hamilton (30:20.965)
Yeah, well…
Familiar to yeah, it makes sense. mean, we, spend so much time on our phones and work with apps and naturally we’re gravitated towards, um, certain, you know, uh, visual cues and, a common kind of visual elements that, um, every, every app should utilize, including, you know, Evo cons there. um, yeah, man, it’s, that’s that I feel like we can have a whole podcast discussion around to kind of the younger generation on the, on the work, work floor and how they’re interacting with it. But.
My next question for you is, I know you’ve mentioned earlier on that the market often kind of misreads what operators maybe really, really need. So I want to pose this question to you. What’s one thing you feel the market often gets wrong and that you’re maybe determined to do differently with Econ?
Martin Lääts (31:14.766)
I think this is not only kind of limited to our industry, but like when we make a decision or when we see clients making decision on, so we need a system and what are the criteria for making a choice? Then I think what the wrong thing to do is purely making choice based on price because the, again,
Like coming back to the topic that we had before is you actually, in addition to price, you need to make sure that the system will be used because you might get a cheaper system or you might actually pay way more because you will get like loads of features. But what is the cost of not the people not using the system? That’s it’s like, so that is, that is, think really, really crucial. So I would hope. I actually, there are,
We see quite often or more often to be fair that clients do bring it out that, okay, we choose you because you are that visual or you’re that user friendly. And there’s actually another example that brings to mind from a Danish customer where we’re actually head to head. think it was two other systems.
local ones. And after the free trial, we actually learned that they had a matrix to evaluate, like ease of use, ease of completing this operation, understanding the data. And they had these multiple questions lined out and they had their operators and production managers evaluate each system based on usability. their decision criteria.
Obviously, I think there was a finance person who was also looking at price, but I had never seen that in any other client. And that was very surprising. And I would hope that more companies would think from that perspective. Let’s implement the system that the people want.
Beau Hamilton (33:26.769)
Right. Based off of how it’s actually going to help and the feature set as opposed to, just focusing on the price. I think that, yeah, you don’t want to get, I don’t know, I think that having a financial person involved is obviously, that’s going to happen. Like it’s an important factor to consider price. But yeah, you got to let the overall results kind of speak for themselves and show their value. One area.
Martin Lääts (33:53.55)
Absolutely.
Beau Hamilton (33:54.989)
One area we haven’t focused in on too much. We’ve kind of mentioned it here and there, but it’s on everyone’s minds and it is AI, artificial intelligence. know expectations in AI and manufacturing are very high, especially when, I don’t know, thinking about robotics, when robotics really start integrating with it. But that’s kind of besides the point. I’m just curious, like from your perspective, where does AI fit into your vision for improving factory efficiency? it starting to make real
improvements now or do we still have kind of a ways to go before we get to see a lot of these these real efficiency gains that that we hear some some influential you know public speakers talk about?
Martin Lääts (34:38.798)
Yeah, I would hope there is a black and white answer to this. I’m pretty sure there are companies who already, mean, manufacturers who are already utilizing AI, meaning like machine learning models and generative AI. But I’ve yet to see real strong value added applications. I mean, to be fair, are all, we are,
Beau Hamilton (34:43.664)
Yeah.
Martin Lääts (35:08.01)
also in the like figuring out phase. Because I feel that the expectations are really high. The expectation is that today you now pop in a question and then you get an answer. mean, that’s the hope. But it implies that you have a lot of data, well-structured data, quality data, and then you also sort of, you’ve done it for a while.
Beau Hamilton (35:25.638)
Yeah.
Martin Lääts (35:37.102)
And there’s actually a system behind using that data. so we, mean, our current view on it is that there are definitely low hanging fruit that we can kind of solve applying, you know, generative AI, especially, but it will take time. yes.
Beau Hamilton (35:58.287)
It’s still early days. Yeah. know it’s a, it, it kind of, it’s almost like if we had an LLM like chat GPT interface, for, for like manufacturing, hardware, like physical products. feel like that’s kind of like the expectation and hype around this, but it’s like, there’s so much work that needs to be done still before we get to that point. I mean, it’s one thing to cater kind of use software, to cater kind of.
software related tasks like text based information and maybe generating photos and videos but like being able to kind of have that like was it the Star Trek replicator like vision where we’re able to kind of just Like have something created At an instant is still a ways off and it’s but it is still and it’s also comes it kind of talks it relates to that bubble talk that everyone’s talking about right now and how There’s so much kind of
hype and upside that there’s this bubble brewing and it’s like so much money is being thrown at it because of all these future possibilities. But the timeline, it’s all it comes down to the timeline and how close we are there. think we all know the direction things are going, but like how soon we’re able to see some of these results is a different story.
Martin Lääts (37:11.832)
I think, definitely, I mean, the first application is just assist. Assist with the analysis, summarize information, analyze language. I mean, those are the obvious things that will be done. And I think it will already have an impact by these broader applications.
Beau Hamilton (37:17.444)
Mm-hmm.
Martin Lääts (37:37.634)
As I said, I’m pretty sure some of the more high tech companies with like loads of people and already years of experience are doing great work, but the majority in other industries, I mean, they’re still, as I said in the beginning, they’re still transitioning away from pen and paper. So we need to give them time. And then hopefully we’ll see some results.
Beau Hamilton (38:05.083)
Do you find that your, is there a, I don’t know, a possibility or a vision of ever like seeing, of seeing Evocon kind of be adapted to maybe like a physical product of like a integrated, I know it’s like you’re being able to integrate with displays and having your information displayed. But I guess like the transitionary period of this like robotics feature where you’re able to take software and combine it with robotics. Is that something that kind of,
could be utilized with Evocon.
Martin Lääts (38:39.808)
I don’t see that right now. No, no, yeah.
Beau Hamilton (38:41.571)
Yeah, just out of curiosity, just kind of came up. Because that is kind of something that I hear talked about is being able to combine some of these software tools directly with the physical robotics. And obviously, there needs to be lot of advancements with the hardware front, on the hardware front with some of these robotics. yeah, just out of curiosity. Yeah, we’ll see. I like it. You probably wouldn’t be able to tell me anyway. I was trying to fish.
Martin Lääts (38:58.179)
Mm-hmm.
Martin Lääts (39:01.902)
We’ll see. We’ll see.
Martin Lääts (39:08.59)
No, no, I mean, I can be completely fair in saying that’s that’s what he’s off.
Beau Hamilton (39:13.265)
You
Well, it is super interesting to talk about this. could, again, lead a whole conversation around this topic. But yeah, it’s great to hear that you’re about what you can do on the software front and just to improve all the efficiency, really just maximize efficiency with these smaller improvements that add up and compound. I think at this point, we have a lot of interested
customers, prospective customers, just people in the industry curious about Evocon. They want to hear more, but maybe you could talk about kind of the onboarding process maybe. How do they get started? Like is there usually maybe like a pilot period, a trial period? they go for a full rollout right away or something else? How does that work?
Martin Lääts (40:05.406)
Yeah, we offer a free trial and we’ve been doing this for years now. think we were probably the first ones to do it. So yeah, the trial is the first free trial is the first step to take. Rarely do we see a full scale rollout. And I think we wouldn’t even advise on doing that because it’s better to get the trial, do it on you know, pilot, look at it as a pilot, choose a line.
Usually we suggest, I mean, the client comes with a bottleneck. So some line or machine that is actually the, like the problem, run it on the machine. is the problem. See the results. even in the first 30 days. our trial is 30 days. and then basically our suggestion is to just use it, understand how it works, see the results, and then start thinking about, the broader, rollout if you’re satisfied.
Because if you’re not, you can send or any clients can send the hardware, back to us. And I mean, we’re good. but usually how it goes is yeah, there was a pilot, like clients who are more strategic, think of very carefully how they choose their pilots. So they usually choose, for example, the factory where they have a, the manager who is very driven in terms of getting improvements.
And I’m speaking of an example where there’s multiple factories involved, but it can, you basically can use the same principles and apply it into one factory setting. So either you choose, um, you choose a factor where, you know, you have, you know, support, then you choose a machine or a line where you have these overachieving cooperators. Uh, and then there’s where you run the trial because then you can actually the other people can observe what’s going on. They can get like.
sort of familiar with the idea. And then once it actually, they see results and then you kind of get the other people involved and then you start rolling out on a larger scale.
Beau Hamilton (42:12.805)
How long does it take to typically start to see results? I know it’s probably a case by case basis, but.
Martin Lääts (42:18.19)
Well, I think you can start seeing results immediately when the system is up and running. So let’s say you contact us today. We understand, we basically outline, so what is your machine, what you want to do. You could have the system up and running in three days if everything goes well. And then basically once the system is implemented and on the factory floor, usually takes a few hours,
max, if there’s any, if there’s no it concerns or, or something similar. And basically you can start results, seeing results. Once your first shift is ongoing, that is being tracked by Eva con because basically the, again, referring to the image that is behind me. Once you get that image going, you’re getting results because you’re seeing what is actually going on. And by the end of the first shift, let’s say you had an idea of, you know, how much you wanted to produce in that shift.
then at the end of that shift, will know how much you actually produced and you can start looking at the patterns. can, you can look at the green and the red and the yellow, but again, referring to things in my background and you will get a sense of what is going on. So it’s really quick to value. I would encourage everyone to just try it out. There’s really nothing, there’s no commitments or anything. There’s nothing.
Beau Hamilton (43:42.449)
That’s impressive, yeah. mean, to see immediate sort of results. For those interested in learning more about Evocon, where can they go? Where’s the best place to get started?
Martin Lääts (43:52.13)
homepage. That’s where all the information is absolutely. Yeah, try to also keep the homepage as simple as possible. So everything is there. Eocon.com and you can sign up, contact us and we’ll be in contact as well.
Beau Hamilton (43:53.295)
homepage.
Beau Hamilton (44:01.873)
Is that evocon.com?
Beau Hamilton (44:11.419)
Perfect, evocon.com. Well, Martin, thank you so much for everything you shared with us. I really enjoyed this conversation and would love to have you back and just keep talking about some of these kind of automation improvements to see kind of what new features and capabilities your platform has and just keep in touch, you know. Let’s have you back.
Martin Lääts (44:33.548)
Absolutely. Thank you. Good to be here. Thank you both.
Beau Hamilton (44:35.153)
Of course. All right. That’s Martin Lääts, head of product at Evocon. Martin, it’s been a pleasure. Thanks again. And thank you all for listening to the SourceForge Podcast. I’m your host, Beau Hamilton. Make sure to subscribe to stay up to date with all of our upcoming B2B software-related podcasts. I will talk to you in the next one.