The Manufacturing Executive
The Manufacturing Executive

Episode · 1 year ago

Human Input in a Data-Driven World w/ Martin Cloake

ABOUT THIS EPISODE

Does technology have to be all or nothing? Is it possible that in our increasingly data-driven manufacturing environment, we're losing sight of the value provided by human input?

In today's episode, I talk about those questions with Martin Cloake, CEO at Raven.ai. Martin is an experienced executive and award-winning technology entrepreneur with a background in manufacturing, data science, IT, and operations management.

Here's what we discussed:

  1. How Martin merged his varied background experiences to create Raven.ai
  2. Creating balance between data and human insight
  3. Real stories of how Martin's clients blend the best of both worlds


To ensure that you never miss an episode of The Manufacturing Executive, subscribe on Apple Podcasts, or Spotify, or here.

First you need to work on your continuous approvement culture, and this needs to happen before even thinking about investing in technology. Continuous improvement. Once, once it's based into our culture, then view technology as a tool in the tool kit, and every manufacturer should be looking for the right tool for the job. Welcome to the manufacturing executive podcast, where we explore the strategies and experiences that are driving mid size manufacturers forward. Here you'll discover new insights from passionate manufacturing leaders who have compelling stories to share about their successes and struggles, and you'll learn from B tob sales and marketing experts about how to apply actionable business development strategies inside your business. Let's get into the show. Welcome to another episode of the Manufacturing Executive podcast. I'm Joe Sullivan, your host and a cofounder of the Industrial Marketing Agency guerrilla seventy six. So let me kick things off today by asking you this. Does technology have to be all or nothing? Do we have to either be on the Industry Four Poe bandwagon or off of it? And is it possible that, in this increasingly data driven manufacturing environment, we're losing sight of the value provided by human input. Today's guest has drawn on his career experiences to build a solution that tackles this issue head on. Martin cloak is the CEO and Co founder of Raven Dot Ai, rave and that as platform, delivers increased profits and x plus Roy to top global manufacturers by guiding actions with real time insights. Martin is an experienced executive and award winning technology and entrepreneur with the background and manufacturing, data science, IP and operations management. Martin holds multiple patents and as a mechanical engineering and business graduate from mcgillany University in Montreal, Quebec, Canada. Martin, welcome to the show. Yeah, thanks, thanks for having Martin. You've got a great story about how your software, Raven at Ai, came to be...

...and in particular, how your experience and sales earlier in your career at blinds to go influenced your decision to build this company. So I was hoping you could kind of start up by explaining what Raven DOT AI is in the first place for listeners and also to tell us a little bit about the journey that influenced your decision to create it. Absolutely so. You know, I at a high level. We help manufacturers produce more efficiently. That I can get in some of the details here, but you know, fundamentally, if many factors can understand what's happening, what has happened, it's much easier to take actions to performance in the future. So you know, we help manufactors make sure they know exactly what's happening at their plants and then we also help them take action. So you know, at the cores it's not that complicated, but you know, it was very much influenced by my early experiences working both in technic and in manufacturing. Now, that's great. I want to quote something you said in an article that you published late last year on Linkedin. So you said getting excited about manufacturers being data rich is like going on and on about how much paint Pablo Picasso had his studio. Our obsession with data is making us forget who and what actually provides a value. I think this was a really interesting and powerful statement and I'm just wondering if you could unpack that for us a little bit, if for sure, and maybe what I can do is I can kind of frame it within my experience. That cause me to found Raven. You alluded to the fact that I started off my career at blinds to go. So yeah, you know, my background is in high tech. I am at the core a high tech, you know, Engineer and entrepreneur and you know, I graduated from McGill University in the early S, you know, and tech and telecom had actually there wasn't as much activity in telecom and as recruited by blinds to go, which is kind of not what I had expected to go into. But it is a pretty need offering and blinds to go not sure what part of the country are yet. I'm in St Louis, Missouri. So okay, so I'm not sure if blinds to go is actually all the way out to sat Louis, but it's an east coast manufacture with a hundred plants and really innovative way to look at manufacturing kind of holistically. And one one of the first things that, you know, I did when I went to work at blinds to go is they got me to sell blinds in a blind store, which was a really, you know, somewhat shocking but kind of an ingenious way to, you...

...know, connect people to the reality of what it is to work in manufacturing. So now here I am graduate from the guilt McGill University, you know, full of confidence, which you know, I think that's what they give you, and good to McGill thinking that. I know, you know know how the world works. Being set to a lions store in total in New Jersey and and the idea is you work in a blind store until you get good at it and once you get good at it then you get to come back and work in the plants. So I just remember, you know, calling my wife within the first couple days there and saying like what's going on here? I'm in engineer and I'm selling blinds that are retail blind store. So, anyway, as it started to go and I started to realize, you know, a couple interesting things. First off, from a sales perspective, if somebody walks into a blind store, they're there to Bli blinds, right, so you should be able to then, you know, work with them and get them to, you know, buy a blind voice, which I realized pretty quickly. But the second thing was to really understand, you know, what is the impact on your customers if there's a quality problem. You know, it's at some point in manufacturing we see quality problems as a number, but you know, you I remember those cases where I miss measure a blind and it came back to the customer and they say, you know, you know you screwed it up. And and really as on the retail side, to live that first firsthand experience of what you know, the user experience of is a manufacturing it changes how you perceive those numbers if something's late and your customers asking for that. So that was really an interesting experience to sort of show me what the consumer side of manufacturing was. And I had never worked in retail sales. You know, I'd work tutoring math. So then after that I went to work in the plants and I did all sorts of roles in May in the plants, from quality engineer to product engineer, and one of the first roles I had was as a production supervisor of a couple lines. And again, you know, full of that McGill confidence, I thought I knew what manufacturing was. You know, I'm going to get my excel file and I'm going to optimize the process and it's going to be I had this one particular excel file that I really liked. It was as an awesome file I think it was about six or seven megs, which at the time open quite slowly, but it had all these neat you know, it was pretty neat file. And I was also playing with data from Cognos, which is interesting because, you know what, of my main advisors was one of the key people at Cognos before acquisition here. So anyway, ill...

...had all this data and I was seeing all these things and at some point I went to my plant manager and I said, Hey, you know, you look at my celf while I think I know, I'd like to show you any Martin. He says, yes, do you know the names of all two hundred operators at the plants? and Say I said No. He said, well, I want you to memorize the names of all two hundred people in your plants and then and then, once you've done that, you can come back and I'll look at your file and like what okay. So, anyways, I still at the stage where, you know, I'd begrudgingly listen to him and then I start. I started, you know, spending more time on the shop floor and then realize that just by walking around it was clear where the problems were. Somebody would ask me to help with a certain machine or they would ask for help or something. I just see stuff as I like a natural problem solver. And then what I realize is that the way to drive improvement in my lines wasn't with my ex cell file, was by walking around asking questions, looking at the process. And so my day more from a day that was more focused on analysis and excel, where I was basically walking laps around the plant. So my morning would start to get there ten to seven and I'd meet with some of my team leads and then I I'd go and say good mornings all my operators and they say, I'll good morning, missed mouth then, because it was I was in Montreal, so that, you know, Miss the mouth then was my name. And you know, I would notice something, I would thank them for coming in and they'd ask me to do things. So then I'd walk around and at some point I had close to fifty operators at the plant and you know, it took quite a while. It almost take me to first break to do that first pass and then I go, I go to break with them and then after break I'd go, I'd maybe go sit down to my desk because I absolutely had, you know, some duties to do there. But this method of just walking around, and right now I apparently, I don't know if you've heard of it's called management by walking around. There's actually an acronym and if you you can see it on wikipedia. So so sort of my perception of manufacturing was completely different going into it at after have, you know, working firsthand. But one of the things that I was frustrated with was the fact that I moved completely away from the data. So I moved from data centric to people centric and I didn't I didn't do anything with the data. But that was I was able to drive, you know, improve many of my lines just by being there and solving these problems. So, yeah, I'm not going to go on a ten minute monolog here, but...

...that was kind of, you know, setting the stage for, you know, the reason why I found it Raven. You know, my perception of manufacturing before getting to the shop floor and after getting to shopl are completely different. So here, after working there for a few years, I kind of got a sense for what it took to provide value to my operators, to, you know, I think the way the way that my you know, I perceived it is, you know, if I can basically as a supervisor at the time, it's my job to solve problems from my operator. What my operatives are doing is pretty clear. Keep the machines running and I need to walk around find those problems and fix it. You know, in in some ways the success of any business is not related to whether or not you have problems, is related to how well and how quickly you solve those problems. So anyways, so that was really the my time and manufacturing, which was was just super exciting and I as a competitive athlete, I almost got some of that same feeling on a shop floor when, when things are rolling it it really feels cool to be part of the team and to be successful. So anyways, after leaving I followed my wife to Ottawas when you're doing your PhD, to and I tried commuting to Montreal from Ottawa, which is a twohour drive, three hours with traffic, and I did that for about three weeks and then I said, you know what this is. So then I basically I put up my shingle and started our consultancy, which combined my background in high tech and manufacturing and started consulting for this little startup scene in Ottawa. So this was a two thousand and seven. So I got connected to a guy named Paul Lem who's a CEO of Spartan Bioscience, and you should check them in the news here. They have real time DNA testing for covid testing. So I consulted for and I did some, you know a design work for for spot and bioscience. And then there's a small company in town at the time with ten people called shopify, and we started hanging out a shopify's office and shop I was doing some pretty cool things and slowly this group and we called ourselves, I think, Young Entrepreneur Club at the time and now it's fresh founders. And slowly this club started doing some pretty cool stuff. So then shopify started blowing up and then Paul's company, Spartan Bioscience,...

...started, you know, doing really cool stuff. And then we have and somebody sold his company. One of my other friends sold his companies to survey monkey and things started going more and more and this little small group of US entrepreneurs in Ottawa began to sort of, you know, do some pretty amazing things. And I'm running my consultancy and serving them do some work. I get around a company in Silicon Valley and with another one of the people in the club. And then there's a point at which they kicked me out of the club. So the you know, they kind of presented to me that, you know, the lowest form of entrepreneurship is being consulted. So here I am hanging out in the entrepreneurs club, not really, you know, I guess I'm an entrepreneur as a consultant, and they say, well, it's you're not. You're not sort of the caliber of entrepreneur that we're looking for in this club, and I like really so. Anyways, and I'm still consulting for them, and at some point I start getting frustrated, going like yeah, I think, you know, being consultant is not really what I want to do. I think I want to start something and I happen to have this problem that I'm really passionate about solving, which is why don't manufacturers use data in the way that I think they should use data. So then the first things first. I go to my buddy Paul and say hey, Paul, I think he started this company in if you know, I think I called it Iot for manufacturing at the time, and I said Paul do you think I should do it? And he's like no, I don't, don't do it. You're a great consultant, you know, stick to what you're good as you know, you wouldn't be good at being an entrepreneur. And is like so as so I most of the time I listened to Paul, but in this case here, I chose not to listen to him. And so then I started up what I called machine celemetry at the time, and really the idea was, you know, how can you allow for manufacturers to use their data in a way that doesn't take out what's most special about being a leader in manufacturing, and what's most special is the time that you are working with your team to identify and solve problems. Anytime that's not spent doing those things, I feel, is time not will spent, and my frustration back then at how much, you know, leader time is spent filling spreadsheets reporting out data is just mind numbing. And you know there had to be a better way, and I would say even today way too much time is consumed by technology rather than unlocked with technology. So I founded the company. At the time as an engineer, I'd always heard that you're...

...supposed to name your company exactly what you do. So I called it machine telemetry because because we're taking telemetry from the machines at the time. And then I met up with my cofounder, Braden PhD, from Institute for Aerospace Studies University of Toronto, which is where, which is basically I always say it is. It's the rocket science program in Canada. It's the top. So I would say that and you know Braden, Braden is the rocket scientist and and he's eyes glaze over when he sees that. So yeah, and the idea here is that, you know, how do we combine the best of technology with actual experience running operations? And that's kind of been the formula for what we've built to date. So I said, you know, I say I we found it. We start to do some work for manufacturers and I say hey, Paul, you know, because it because I'd always go back to Paul, my buddy from Spartan bioscience, check it out, and he says, Oh, it's pretty cool, you should raise some money. Says Oh yeah, let's let's do it, and he's but your company name sucks. He said Yeah, yeah, machine telemetry is the worst name ever. You know, you should name the company Raven. And so you why? Paul, and he was telling me, with regards to the company name, like nobody's ever going to want to say hey, the guys from machine telemetry are here, you know. So he said Raven because Odin the Norse God, has these two ravens, Hugan Immune, and they fly around the world getting information for him, which is kind of what you do. Is said, boom, let's do it, you know. So changed the company name, reached out to some of my buddies who are, you know, now quite successful in Ottawa, and they're like this is this is really cool, and that kind of started. You know, we used the race of money and we've raised a bunch of money since from effectively this this network that that we've had from the starts in Ottawa, you know, that's now basically given back and supported our community. So so anyway, so I think you know, as mentioned. So so we raise some money and really the formula that we we have, we've had for building the business to date, is really by combining the best at modern tech has to offer with a, you know, deep understanding of manufacturing, you know, and we've built that to date so, for example, you know, on our executive team now we have Rob Lander, who was president CEO of Stackpole, international billion dollar publicly traded auto parts manufacturer who spent his career, you know, transforming manufacturing operations.

So for us it's really important to have that, you know, the core understanding of what it takes to transform manufacturing because in some ways that people aspect of change management is the same. People are the same. You know, technology has advanced, but we haven't, and really that's been the formula for what we've done so far. And now, you know, we serve global manufacturers from so no fee to Danaher to Hitachie, you know, and effectively, it's really with the same vision that we've always had, which is, how do you provide clear understanding of what's actually happening, which is a pretty big challenge and I can get into that later. And then, once with this information, how do you provide it in a way where they can actually take actions to improve and you know, the gold standard for application of modern technology in my mind, is ways or Google traffic. So you think about how this technology basically cuts to the chase. It does and dominate your attention. It doesn't you know, drown you in dashboards and you know, constantly needing to you know, you to look at it or interact with it on occasion. It provides guidance. If you listen to that guidance, you will perform at a much higher level. So that that is the gold standard. You know, we're not quite there in manufacturing yet where there is a place for dashboards and other than reports. But but really that that is the value that, you know, technology can provide, which is to help to identify problems and make it easier to solve them more quickly. Yeah, and just to clarify for listeners, when you said ways, you're referring to the the APP, right. That that's right, traffic APP. That where the input comes from, both, you know, real data, but then also like traffic data, but also input the people are physically providing by typing into the APP. Right, absolutely. And now, now, the way that it works is it gets data from your car and from traffic. People contribute data, but one of the core things is that data needs to be accurate. If that data is not accurate, you know, no matter what you do on top of that, inaccurate data, you won't be able to provide good guidance. One of the biggest challenges today is that, you know, in the industry, for those but are in manufacturing today, there are tons of...

...companies out there providing analytics maintenance software. Now, many of these systems rely on the data that's fed into them, and this data is, you know, has been collected from machines for decades. But the most valuable data is to understand when the process is not running. When the process is not running, the way to get it is typically through manually entered methods. You know, there's somebody writes it down, types it and excel or pulls a dropdown menu. So all of these systems sit on this this rickety structure where, you know, humans are entering data into the system, and the fact that they're all sitting on top of this means that data quality is one of the biggest issues that has has people haven't emphasized. So half of the problem with many of these systems is data quality and the fact that, you know, these other systems with analytics and dashboards are sitting on top of this poor data quality. People get burned. People get burned by getting pointed in the wrong direction. And what happens when you get burned a few times, you get disengaged. So you have these systems that almot that exist to present metrics that people don't trust on the shop floor and then at the core of people are going back to doing what they've been doing for thirty years, which is, you know, managing their data day based on instincts. So to solve the industry for to challenge, you need to make sure that it's sitting on a foundation of good data. And then, once people have confidence that the data's trustworthy, how do you give them a tool to allow them to take actions to identify and solve those problems more quickly? Yeah, so you really are bringing together kind of the best of both worlds. They're like. What are some ways that, just to try to make it tangible for listeners here, what are some ways that, you know, your tool, ravened at ai, harmonizes the best of, you know, the data being collected from machines and the human input? Well, at the core, I think what makes it really clear is that our clients work with us because they get a return on their investment. So there is a cost, you know, our technology costs a certain amount. So they need to see that return. So you know and what delivers that return changes based on where they are in their evolution.

So, in example, we serve a a Danaher plant in California. You know, large organization and, as I mentioned before, one of the biggest challenges is to know what's happened. So this particular plant had an issue where machines that were producing goods were down for a long period of time for unknown reasons. And if you were to look at the machine day that it would highlight the fact that they are down because they're broken. So you know at a high level the machines are broken. What do you do to a dress broken machines? Will maybe you get engineers to try to make the machines run more smoothly. Right. But what we saw when we deployed our technology was that when a machines breaks and goes down, there's actually three different segments of time. So there's the first segment of time where you're waiting for maintenance to arrive, the second one you're fixing the machine and the third one you're waiting for the operator to come back. So those three different segments of time require different things to to resolve. So you know, when we finally sliced it up and show them, it was shocking to see that they were losing six hundred hours machine hours per month because of waiting for maintenance. And this is not that they didn't have maintenance staff where we was just, you know, a slight misalignment of their schedule. So one of the amazing things is that the first time you see data presented accurately, the types of things that you need to do to drive gains early on are often mundane. And so in this case here, you know, they reduced their waiting for maintenance downtime by ninety percent, resulting in, you know, significant oe games. And if you were to see what they're actually doing on the shop floor, it is not revolutionary. It just comes like. This is the kind of thing where, if you were to define this problem and give operators, you know, this is the problem, this is your team, we're spending too much time waiting for maintenance, like they know how to solve this problem. Right. All you need to do is is and we don't solve problems, we just we just present them to people who know how to solve them on the shop floor. So one of the exciting things is that. You know, often the kinds of things we see early on are, you know, you're spending too much time waiting for maintenance, you're spending three times as much time...

...as you should on set up, your machine, stops and starts, waste too often. So there's tons of early gains as soon as you, I guess, in some ways, just flick on the lights for the first time, then that's that's really good example. You know, it's the the data alone doesn't tell enough of the story, you know, without the human input, right and interpretation of what it actually means. Absolutely and I think at some point here you'll you'll notice that I never use the word oee or all these other metrics here, because at some point the problem was they are spending too much time waiting for maintenance. Yeah, so let's eliminate that problem. And so much talk, you know, the conversation about industry, for I oh and you know I oet and Ai Cloud and all this kind of stuff. Here it's so far removed from, you know, supervisors and maintenance leaders having a conversation. But what kinds of things they can do together to reduce how much time they're spending waiting for maintenance? So in some ways the most effective industry fourt auto projects right now are called industry phototto projects, they're called continuous improvement projects. Are Not even projects, it's just part of continuous improvement. So for that particular example, like in none of our communication where we mentioning Smart Manufacturing, industry fourt auto cloud, that wasn't there. You know, like Daniher, has continuous improvement baked into their culture. They are looking for practical ways to drive gains and you know, we're happy to be a tool in their tool kit to support them to make these gains. And that's effectively what industry fourtauto is. It's not a movement, it's not this big transformation from one way to another. It is a pretty neat tool in the tool kit. But that's not the narrative that's presented, you know, on social media, not on Linkedin and all that are even in board rooms from you know, large manufacturers. In another one of your articles on linked in, Martine reference to delight study that showed how labor productivity and manufacturing have been growing very consistently and rapidly from like the late s until two thousand and seven or so, and then since then despite turning on what you referred to is the real time data fire house, that productivity is stagnated. No surprising for...

...me to see this. I'm curious if you could talk about why that has happened. Yeah, it was is pretty shocking to see. It's by delight and mappy. Okay, an organization manufactured, our organization, doing a study of Labor proctivity and and it's it's almost like the moment the iphone was released. You know, productivity growth basically flat line. But really the thing that changed was this appetite for real time data. So up until that point and we've always had data, you know, in manufacturing in particular. You know, data is just a fundamental part of our lives, but the way we consumed it was in a manageable amount. We would see performance, you know once a shift, as mentioned before, I we would spend our time walking around. So, you know, you see your daily shift report, you'd internalize the problem that you're trying to solve and you go and try and solve it. So what you know, as technology advanced, with the cloud and Iot and access to all this data, the way that we implemented, you know, solutions very much in a sort of copied our KPI report centric view of how to drive improvement. So, you know, rather than changing how you know, how we perceive how we should use data, we simply took a report that we used to see, you want to shift and just made it live. So the way you know, I would say, the way we are used to using data as once a day you get a report. Now, when you flip to a real time, if you make reports real times, there's a couple issues. One often with the once a day reports you have people getting in there to actually make adjustments so that the numbers are trustworthy. And the second one is our reports and Kpis typically aren't an effective way to guide action. So you know, you go back to my ways, example. Ways is not a series of DASHBOARDS and KPIS. It's simple instructions that tell you how to avoid traffic, and I think that's that's a big difference here. So you know, whereas we have this this technology that's a lot able to collect and present tons and tons of data and real time, our capability to consume data is, to will still quite low. So you know, how do you and I think we've struggled with this change. That has impacted the two things that I mentioned earlier, which is data quality and engagement. For, you know, projecting a are presenting a dashboard in...

...real time that has questionable data, that's not directive, is not helping people on the shop floor take actions to drive improvement. So but at the same time, you know, there's been this movement over the last ten years to invest in technology and there's been this you know, companies are investing massive amounts in pilots and different initiatives, often very, very large initiatives that are disruptive organizationally. So in many cases with these, with these implementations, the most complex part is in the technological part. It is change management. So by taking these systems, which just are fundamentally flawed and are not helping, you're actually disrupting organizations and creating disengagement on the shop floor, which does not help, you know, nurture and improve a continue some improvement culture. So, you know, if I were to think of the formula for manufactures, big or small, if you know, to deploy this technology properly, first you need to work on your continuous improvement culture, and this needs to happen before even thinking about investing in technology continuous improvement. Once, once it's based into our culture, then view technology as a tool in the tool kit and every manufacturer should be looking for the right tool for the job and should be very you know, where if tools that you know the team is rejecting your tool, there's a probably a good reason for it. So the second thing is that often, you know, because of the big the scale of how industry part or Portado is presented online, everybody thinks they need to go big where the reality is that the best way to deploy this kind of technology is to deploy quick and small and manageable and accelerate and, you know, double down on success. And you know it's amazing to see on Linkedin, but when anybody posts something that shows a whiteboard, so somebody is really proud of their white board and they say hey, I made a KPI Whiteboard, check it out Linkedin, and then people jump all over it. So there's the old school folks that say this is the way to go, it's so connected to...

...the operator, creates engagement, it's like it's perfect, and then there's that the tech book that go like no, what are you guys doing? You're stuck in the vent or second the S. that's not the way to do it. When the reality is there. They're both right. So one of the things that white boards do that technology often doesn't do is that it takes the operator and the production team along for the ride. So you start, it's white, there's nothing there. You come up with a concept. If the operators are anybody makes a suggestion, you can just, you know, take your whiteboard tape and draw something new and it. There's this neat way to create engagement and involvement with the people who are actually using, you know, the the technology the operators if they don't see value in it. You know, if operators don't see value in these systems, you're off to a bad start. So like the way that operators benefit from technology if the technology applies pressure to their leaders to fix their problems. So the great thing with white pores is it takes them along for the ride. Technology is likely a better way to implement the end state, but it misses that journey to get to that end state. So what we've seen with our clients has is that you can get the best of both worlds by starting with an extremely simple and basic implementation and take the operators along for the ride as they're beginning to, you know, request complexity. So where? And you know, the first phase is what is happening, and in some ways you can even create like if you're thinking of how to categorize time, if you categorize time in nine different ways, let's just categorize not time in nine different ways and let the operators push for added complexity. So once you know what's happening, then the next thing is you know what has happened, what's happening. That's kind of like the basics. And then the next phase is why is it happening? And as if you take operators along this journey, then your maintenance team and your supervisors and plant managers are along for the journey. And that's how you actually integrate technology into your continuous improvement culture. By coming in with this end state, which may look the same, and saying implement this now, you've...

...missed that part that actually, you know, creates that connection and glue between technology and continue some improvement culture, and without that connection it just doesn't fit. It's all really, really good stuff there, Martin. Is there anything that I didn't ask you that you want to add to this conversation before we put a wrap on it. Yeah, I think we covered it all here and yeah, it's it's an exciting time in manufacturing. I think over the last year with all, you know, the turmoil and change brought on by Covid you know, manufacturers are talking about digital transformation and industry phototo more than ever. I think there's a lot of talk, a lot of concern, there isn't a lot of practical guidance for, you know, many factors showing them how to actually take action here. So I think if we were to start summat up to one thing here, you know, it to start off with industry, for I know, for any size manufacturer, the best approach is to start quickly, start small, start simply and with that foundation, and this is with your operators, and with that foundation, double down on success and accelerate. If you start too big and you start from the top down, it's just going to, you know, be on the scrap heap of failed industry and phototo projects and kind of continue the pilot purgatory that's been going around for a while now. So I would also say that, you know, there's a huge appetite for this technology. It hasn't necessarily the solution hasn't been articulated in a way that's resonating with plant managers. But in the same way where you know there's an Uber moment where people, you know, would never jump into a car with a stranger to saying like actually, it's okay, I'm going to jump in a car with a stranger because they just got that value prop just right. You know there's going to be that Uber moment in manufacturing where Manu factors recognize that this is the way that they need to run their operations to actually keep up. This is the way to do it, you know, twice as efficient and spend half the amount of money. So there's that Uber moment coming when people are going to realize and in you know, because as of how simply this technology can be deployed nowadays, it's going to go quickly. So it's exciting to kind of see that coming and I'm not sure exactly what's going to trigger that that moment, but it's definitely conversations like this, where you know we're talking about practical applications of Industry...

Partoo, are going to help us get closer to that and it's definitely exciting to be in the space at this time. Martin, Great Conversation Today. This is really valuable. I think we're going to have a this will be a very popular episode, so I really appreciate you doing this. Can you give our audience a sense for how to get in touch with you and where they can learn more about raven? Yeah, so the main way to get in touch with me is on Linkedin and now on clubhosts. You got to check us out on clubhosts industry fort on clubhose. It's going to be a pretty awesome, beautiful and then raven dot ai is the URL forrer software. So that's right, all right, we'll fantastic. Martin, thanks again for taking the time to join me today. Really appreciate it. And then, for the rest of you, I hope to catch you on the next episode of the Manufacturing Executive. You've been listening to the manufacturing executive podcast. To ensure that you never miss an episode, subscribe to the show in your favorite podcast player. If you'd like to learn more about industrial marketing and sales strategy, you'll find an ever expanding collection of articles, videos, guides and tools specifically for bedb manufacturers. At Gorilla, seventy sixcom learn. Thank you so much for listening. Until next time,.

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