The Manufacturing Executive
The Manufacturing Executive

Episode · 11 months 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 yourcontinuous approvement culture, and this needs to happen before even thinking about investing intechnology. Continuous improvement. Once, once it's based into our culture, thenview technology as a tool in the tool kit, and every manufacturer should belooking 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 toshare about their successes and struggles, and you'll learn from B tob sales andmarketing experts about how to apply actionable business development strategies inside your business. Let'sget 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 guerrillaseventy 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 onthe Industry Four Poe bandwagon or off of it? And is it possiblethat, in this increasingly data driven manufacturing environment, we're losing sight of thevalue provided by human input. Today's guest has drawn on his career experiences tobuild a solution that tackles this issue head on. Martin cloak is the CEOand Co founder of Raven Dot Ai, rave and that as platform, deliversincreased profits and x plus Roy to top global manufacturers by guiding actions with realtime insights. Martin is an experienced executive and award winning technology and entrepreneur withthe background and manufacturing, data science, IP and operations management. Martin holdsmultiple 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 experienceand sales earlier in your career at blinds to go influenced your decision to buildthis company. So I was hoping you could kind of start up by explainingwhat Raven DOT AI is in the first place for listeners and also to tellus a little bit about the journey that influenced your decision to create it.Absolutely so. You know, I at a high level. We help manufacturersproduce more efficiently. That I can get in some of the details here,but you know, fundamentally, if many factors can understand what's happening, whathas 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 attheir 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 verymuch 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 thatyou published late last year on Linkedin. So you said getting excited about manufacturersbeing data rich is like going on and on about how much paint Pablo Picassohad his studio. Our obsession with data is making us forget who and whatactually provides a value. I think this was a really interesting and powerful statementand 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 offrame it within my experience. That cause me to found Raven. You alludedto the fact that I started off my career at blinds to go. Soyeah, you know, my background is in high tech. I am atthe core a high tech, you know, Engineer and entrepreneur and you know,I graduated from McGill University in the early S, you know, andtech and telecom had actually there wasn't as much activity in telecom and as recruitedby blinds to go, which is kind of not what I had expected togo into. But it is a pretty need offering and blinds to go notsure 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 allthe way out to sat Louis, but it's an east coast manufacture with ahundred plants and really innovative way to look at manufacturing kind of holistically. Andone one of the first things that, you know, I did when Iwent to work at blinds to go is they got me to sell blinds ina blind store, which was a really, you know, somewhat shocking but kindof an ingenious way to, you...

...know, connect people to the realityof what it is to work in manufacturing. So now here I am graduate fromthe guilt McGill University, you know, full of confidence, which you know, I think that's what they give you, and good to McGill thinkingthat. I know, you know know how the world works. Being setto a lions store in total in New Jersey and and the idea is youwork in a blind store until you get good at it and once you getgood 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 coupledays there and saying like what's going on here? I'm in engineer and I'mselling blinds that are retail blind store. So, anyway, as it startedto 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 ableto then, you know, work with them and get them to, youknow, buy a blind voice, which I realized pretty quickly. But thesecond thing was to really understand, you know, what is the impact onyour customers if there's a quality problem. You know, it's at some pointin manufacturing we see quality problems as a number, but you know, youI remember those cases where I miss measure a blind and it came back tothe customer and they say, you know, you know you screwed it up.And and really as on the retail side, to live that first firsthandexperience of what you know, the user experience of is a manufacturing it changeshow 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 consumerside of manufacturing was. And I had never worked in retail sales. Youknow, I'd work tutoring math. So then after that I went to workin 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 Ihad was as a production supervisor of a couple lines. And again, youknow, 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 tooptimize the process and it's going to be I had this one particular excel filethat I really liked. It was as an awesome file I think it wasabout six or seven megs, which at the time open quite slowly, butit had all these neat you know, it was pretty neat file. AndI was also playing with data from Cognos, which is interesting because, you knowwhat, of my main advisors was one of the key people at Cognosbefore acquisition here. So anyway, ill...

...had all this data and I wasseeing all these things and at some point I went to my plant manager andI said, Hey, you know, you look at my celf while Ithink 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 youto memorize the names of all two hundred people in your plants and then andthen, once you've done that, you can come back and I'll look atyour file and like what okay. So, anyways, I still at the stagewhere, you know, I'd begrudgingly listen to him and then I start. I started, you know, spending more time on the shop floor andthen 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 forhelp 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 mylines 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 wasmore focused on analysis and excel, where I was basically walking laps around theplant. So my morning would start to get there ten to seven and I'dmeet with some of my team leads and then I I'd go and say goodmornings all my operators and they say, I'll good morning, missed mouth then, because it was I was in Montreal, so that, you know, Missthe mouth then was my name. And you know, I would noticesomething, I would thank them for coming in and they'd ask me to dothings. So then I'd walk around and at some point I had close tofifty operators at the plant and you know, it took quite a while. Italmost take me to first break to do that first pass and then Igo, 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 calledmanagement by walking around. There's actually an acronym and if you you can seeit on wikipedia. So so sort of my perception of manufacturing was completely differentgoing into it at after have, you know, working firsthand. But oneof the things that I was frustrated with was the fact that I moved completelyaway from the data. So I moved from data centric to people centric andI didn't I didn't do anything with the data. But that was I wasable to drive, you know, improve many of my lines just by beingthere and solving these problems. So, yeah, I'm not going to goon 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 floorand after getting to shopl are completely different. So here, after working there fora few years, I kind of got a sense for what it tookto provide value to my operators, to, you know, I think the waythe way that my you know, I perceived it is, you know, if I can basically as a supervisor at the time, it's my jobto 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 andfix it. You know, in in some ways the success of any businessis not related to whether or not you have problems, is related to howwell and how quickly you solve those problems. So anyways, so that was reallythe my time and manufacturing, which was was just super exciting and Ias a competitive athlete, I almost got some of that same feeling on ashop floor when, when things are rolling it it really feels cool to bepart of the team and to be successful. So anyways, after leaving I followedmy wife to Ottawas when you're doing your PhD, to and I triedcommuting to Montreal from Ottawa, which is a twohour drive, three hours withtraffic, 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 shingleand started our consultancy, which combined my background in high tech and manufacturing andstarted consulting for this little startup scene in Ottawa. So this was a twothousand and seven. So I got connected to a guy named Paul Lem who'sa 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 forand 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 peoplecalled shopify, and we started hanging out a shopify's office and shop I wasdoing some pretty cool things and slowly this group and we called ourselves, Ithink, Young Entrepreneur Club at the time and now it's fresh founders. Andslowly this club started doing some pretty cool stuff. So then shopify started blowingup and then Paul's company, Spartan Bioscience,...

...started, you know, doing reallycool stuff. And then we have and somebody sold his company. Oneof my other friends sold his companies to survey monkey and things started going moreand more and this little small group of US entrepreneurs in Ottawa began to sortof, you know, do some pretty amazing things. And I'm running myconsultancy and serving them do some work. I get around a company in SiliconValley and with another one of the people in the club. And then there'sa point at which they kicked me out of the club. So the youknow, they kind of presented to me that, you know, the lowestform of entrepreneurship is being consulted. So here I am hanging out in theentrepreneurs club, not really, you know, I guess I'm an entrepreneur as aconsultant, and they say, well, it's you're not. You're not sortof the caliber of entrepreneur that we're looking for in this club, andI like really so. Anyways, and I'm still consulting for them, andat some point I start getting frustrated, going like yeah, I think,you know, being consultant is not really what I want to do. Ithink I want to start something and I happen to have this problem that I'mreally passionate about solving, which is why don't manufacturers use data in the waythat I think they should use data. So then the first things first.I go to my buddy Paul and say hey, Paul, I think hestarted this company in if you know, I think I called it Iot formanufacturing at the time, and I said Paul do you think I should doit? And he's like no, I don't, don't do it. You'rea 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 asso I most of the time I listened to Paul, but in this casehere, I chose not to listen to him. And so then I startedup 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 ina 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 teamto identify and solve problems. Anytime that's not spent doing those things, Ifeel, is time not will spent, and my frustration back then at howmuch, you know, leader time is spent filling spreadsheets reporting out data isjust 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 ratherthan unlocked with technology. So I founded the company. At the time asan engineer, I'd always heard that you're...

...supposed to name your company exactly whatyou do. So I called it machine telemetry because because we're taking telemetry fromthe 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 programin Canada. It's the top. So I would say that and you knowBraden, Braden is the rocket scientist and and he's eyes glaze over when hesees 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. SoI said, you know, I say I we found it. We startto 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 Spartanbioscience, 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, machinetelemetry is the worst name ever. You know, you should name thecompany Raven. And so you why? Paul, and he was telling me, with regards to the company name, like nobody's ever going to want tosay hey, the guys from machine telemetry are here, you know. Sohe said Raven because Odin the Norse God, has these two ravens, Hugan Immune, and they fly around the world getting information for him, which iskind of what you do. Is said, boom, let's do it, youknow. So changed the company name, reached out to some of my buddieswho are, you know, now quite successful in Ottawa, and they'relike this is this is really cool, and that kind of started. Youknow, we used the race of money and we've raised a bunch of moneysince 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. Soso anyway, so I think you know, as mentioned. So so we raisesome money and really the formula that we we have, we've had forbuilding the business to date, is really by combining the best at modern techhas 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 CEOof 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 tohave that, you know, the core understanding of what it takes to transformmanufacturing 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. Andnow, you know, we serve global manufacturers from so no fee to Danaherto Hitachie, you know, and effectively, it's really with the same vision thatwe've always had, which is, how do you provide clear understanding ofwhat's actually happening, which is a pretty big challenge and I can get intothat later. And then, once with this information, how do you provideit 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 waysor Google traffic. So you think about how this technology basically cuts to thechase. It does and dominate your attention. It doesn't you know, drown youin dashboards and you know, constantly needing to you know, you tolook at it or interact with it on occasion. It provides guidance. Ifyou 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 therein 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 canprovide, which is to help to identify problems and make it easier to solvethem more quickly. Yeah, and just to clarify for listeners, when yousaid 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 thepeople are physically providing by typing into the APP. Right, absolutely. Andnow, now, the way that it works is it gets data from yourcar and from traffic. People contribute data, but one of the core things isthat 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 challengestoday is that, you know, in the industry, for those but arein 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 notrunning. When the process is not running, the way to get it is typicallythrough manually entered methods. You know, there's somebody writes it down, typesit and excel or pulls a dropdown menu. So all of these systemssit on this this rickety structure where, you know, humans are entering datainto the system, and the fact that they're all sitting on top of thismeans that data quality is one of the biggest issues that has has people haven'temphasized. So half of the problem with many of these systems is data qualityand the fact that, you know, these other systems with analytics and dashboardsare sitting on top of this poor data quality. People get burned. Peopleget burned by getting pointed in the wrong direction. And what happens when youget burned a few times, you get disengaged. So you have these systemsthat almot that exist to present metrics that people don't trust on the shop floorand then at the core of people are going back to doing what they've beendoing for thirty years, which is, you know, managing their data daybased on instincts. So to solve the industry for to challenge, you needto 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 thema tool to allow them to take actions to identify and solve those problems morequickly? Yeah, so you really are bringing together kind of the best ofboth worlds. They're like. What are some ways that, just to tryto make it tangible for listeners here, what are some ways that, youknow, 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 clientswork with us because they get a return on their investment. So there isa cost, you know, our technology costs a certain amount. So theyneed to see that return. So you know and what delivers that return changesbased on where they are in their evolution.

So, in example, we servea 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 weredown for a long period of time for unknown reasons. And if you wereto look at the machine day that it would highlight the fact that they aredown because they're broken. So you know at a high level the machines arebroken. What do you do to a dress broken machines? Will maybe youget engineers to try to make the machines run more smoothly. Right. Butwhat we saw when we deployed our technology was that when a machines breaks andgoes down, there's actually three different segments of time. So there's the firstsegment of time where you're waiting for maintenance to arrive, the second one you'refixing 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, itwas shocking to see that they were losing six hundred hours machine hours per monthbecause of waiting for maintenance. And this is not that they didn't have maintenancestaff 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 datapresented accurately, the types of things that you need to do to drive gainsearly 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'reactually doing on the shop floor, it is not revolutionary. It just comeslike. This is the kind of thing where, if you were to definethis problem and give operators, you know, this is the problem, this isyour team, we're spending too much time waiting for maintenance, like theyknow how to solve this problem. Right. All you need to do is isand we don't solve problems, we just we just present them to peoplewho know how to solve them on the shop floor. So one of theexciting things is that. You know, often the kinds of things we seeearly 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 ofearly gains as soon as you, I guess, in some ways, justflick 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 actuallymeans. Absolutely and I think at some point here you'll you'll notice thatI never use the word oee or all these other metrics here, because atsome 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 andAi 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 kindsof things they can do together to reduce how much time they're spending waiting formaintenance? So in some ways the most effective industry fourt auto projects right noware called industry phototto projects, they're called continuous improvement projects. Are Not evenprojects, it's just part of continuous improvement. So for that particular example, likein none of our communication where we mentioning Smart Manufacturing, industry fourt autocloud, that wasn't there. You know, like Daniher, has continuous improvement bakedinto their culture. They are looking for practical ways to drive gains andyou know, we're happy to be a tool in their tool kit to supportthem to make these gains. And that's effectively what industry fourtauto is. It'snot 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 thenarrative that's presented, you know, on social media, not on Linkedin andall that are even in board rooms from you know, large manufacturers. Inanother one of your articles on linked in, Martine reference to delight study that showedhow labor productivity and manufacturing have been growing very consistently and rapidly from likethe late s until two thousand and seven or so, and then since thendespite 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 curiousif you could talk about why that has happened. Yeah, it was ispretty shocking to see. It's by delight and mappy. Okay, an organizationmanufactured, our organization, doing a study of Labor proctivity and and it's it'salmost like the moment the iphone was released. You know, productivity growth basically flatline. But really the thing that changed was this appetite for real timedata. 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 ofour 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 tosolve 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 asort of copied our KPI report centric view of how to drive improvement. So, you know, rather than changing how you know, how we perceive howwe 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 youknow, I would say, the way we are used to using data asonce a day you get a report. Now, when you flip to areal 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 toactually make adjustments so that the numbers are trustworthy. And the second one isour reports and Kpis typically aren't an effective way to guide action. So youknow, you go back to my ways, example. Ways is not a seriesof DASHBOARDS and KPIS. It's simple instructions that tell you how to avoidtraffic, 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 presenttons and tons of data and real time, our capability to consume data is,to will still quite low. So you know, how do you andI think we've struggled with this change. That has impacted the two things thatI 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 todrive improvement. So but at the same time, you know, there's beenthis movement over the last ten years to invest in technology and there's been thisyou know, companies are investing massive amounts in pilots and different initiatives, oftenvery, very large initiatives that are disruptive organizationally. So in many cases withthese, with these implementations, the most complex part is in the technological part. It is change management. So by taking these systems, which just arefundamentally flawed and are not helping, you're actually disrupting organizations and creating disengagement onthe shop floor, which does not help, you know, nurture and improve acontinue some improvement culture. So, you know, if I were tothink of the formula for manufactures, big or small, if you know,to deploy this technology properly, first you need to work on your continuous improvementculture, and this needs to happen before even thinking about investing in technology continuousimprovement. Once, once it's based into our culture, then view technology asa tool in the tool kit and every manufacturer should be looking for the righttool for the job and should be very you know, where if tools thatyou know the team is rejecting your tool, there's a probably a good reason forit. So the second thing is that often, you know, becauseof 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 wayto deploy this kind of technology is to deploy quick and small and manageable andaccelerate and, you know, double down on success. And you know it'samazing 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 jumpall over it. So there's the old school folks that say this is theway to go, it's so connected to...

...the operator, creates engagement, it'slike 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 theS. 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 thattechnology often doesn't do is that it takes the operator and the production team alongfor 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 somethingnew and it. There's this neat way to create engagement and involvement with thepeople who are actually using, you know, the the technology the operators if theydon't see value in it. You know, if operators don't see valuein these systems, you're off to a bad start. So like the waythat operators benefit from technology if the technology applies pressure to their leaders to fixtheir problems. So the great thing with white pores is it takes them alongfor 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 whatwe've seen with our clients has is that you can get the best of bothworlds by starting with an extremely simple and basic implementation and take the operators alongfor the ride as they're beginning to, you know, request complexity. Sowhere? And you know, the first phase is what is happening, andin some ways you can even create like if you're thinking of how to categorizetime, if you categorize time in nine different ways, let's just categorize nottime in nine different ways and let the operators push for added complexity. Soonce you know what's happening, then the next thing is you know what hashappened, what's happening. That's kind of like the basics. And then thenext phase is why is it happening? And as if you take operators alongthis journey, then your maintenance team and your supervisors and plant managers are alongfor the journey. And that's how you actually integrate technology into your continuous improvementculture. By coming in with this end state, which may look the same, and saying implement this now, you've...

...missed that part that actually, youknow, 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 stuffthere, Martin. Is there anything that I didn't ask you that youwant 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 excitingtime in manufacturing. I think over the last year with all, you know, the turmoil and change brought on by Covid you know, manufacturers are talkingabout digital transformation and industry phototo more than ever. I think there's a lotof talk, a lot of concern, there isn't a lot of practical guidancefor, 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 starttoo 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 andkind 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 forthis technology. It hasn't necessarily the solution hasn't been articulated in a way that'sresonating with plant managers. But in the same way where you know there's anUber moment where people, you know, would never jump into a car witha stranger to saying like actually, it's okay, I'm going to jump ina 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 recognizethat this is the way that they need to run their operations to actually keepup. This is the way to do it, you know, twice asefficient and spend half the amount of money. So there's that Uber moment coming whenpeople are going to realize and in you know, because as of howsimply this technology can be deployed nowadays, it's going to go quickly. Soit's exciting to kind of see that coming and I'm not sure exactly what's goingto trigger that that moment, but it's definitely conversations like this, where youknow we're talking about practical applications of Industry...

Partoo, are going to help usget closer to that and it's definitely exciting to be in the space at thistime. Martin, Great Conversation Today. This is really valuable. I thinkwe're going to have a this will be a very popular episode, so Ireally appreciate you doing this. Can you give our audience a sense for howto 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 Linkedinand now on clubhosts. You got to check us out on clubhosts industry forton clubhose. It's going to be a pretty awesome, beautiful and then ravendot 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, Ihope to catch you on the next episode of the Manufacturing Executive. You've beenlistening 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 tolearn more about industrial marketing and sales strategy, you'll find an ever expanding collection ofarticles, 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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