Speaker 0 00:00:04 Hello everyone. And I'd like to welcome you to the 83 robot nation podcast. My name is Jim Beretta and I am your host we're broadcasting from Cambridge Ontario. Today. I'd like to thank and acknowledge our partner. The eight three, the association for advancing automation. The <inaudible> is the umbrella association for the RIA AIA, M C M a N a three Mexico. And these four associations combined represent almost 1300 automation manufacturers, component suppliers, systems integrators, end users, research groups, manufacturers, and consulting firms throughout the world that are driving automation forward. I'm going to introduce to you, we have a guest today and his name is Mike tidy. He is a leader of ATSs digital solutions division focusing on industry 4.0 solutions for global manufacturing companies. Mike has decades of experience in it solutions with the last 10 specifically focused on solutions for industrial companies. He has started and grown to software and services businesses within organizations, and is an advocate for digitization and process involvement in manufacturing companies. Mike has been with ATS for nearly two years working out of their Cambridge head office. So welcome to the podcast, Mike, Hey Jim, thanks for having me. It's great to be here. I'm glad you could make it on our discovery call. We both heard our dogs in the background in it, and we have to almost like cloned dogs that we both bought black poodles with, uh, a white crest on their chest. So if I hear some yipping in the background, I know that's your dog.
Speaker 1 00:01:38 Yes. Hold on. I'm not sure if yours is biting as much as mine is. So that's a, we're doing some training right now. So lots of fun on that.
Speaker 0 00:01:45 Excellent. Well, I wanted to also let everybody know in the audience that I actually used to work at ATS for a very long time. And if you could check out my bio sometime, uh, I had, uh, almost 16 years on the company, so I know ATS very, very well, and they are a very strong controls, a very robust group of vision people. So, um, I'm glad to have you on the call today. And I wanted to ask you and start the conversation with what's happening in manufacturing these days. Is it time to market? Is it collaborative technologies? What are some of the things that you're hearing from?
Speaker 1 00:02:17 Yeah, so, so there's a lot of sort of higher level sort of micro and macro trends going on. And maybe I'll sort of hit a few points here from my experience anyway, which is, uh, certainly automation, uh, automation equipment is getting way more complex, um, much more, um, independently operated a lot less requirement for operators to actually be involved in machines, a much higher level of complexity. Um, a lot more products being built from start to finish with full automation, without a lot of manual intervention for people, except in some cases feeding product in one eight one end and taking, uh, uh, finished goods out the other. But, uh, certainly the level of automation is really staggering. It's going on right now. And that comes with a lot of challenges. Uh, one it's challenging, you know, how many people you need and what sort of your costs of production are.
Speaker 1 00:03:04 And then secondly, we're also seeing a lot of challenges around skillset. So for people working with that equipment. So, uh, you know, it's kind of timely on our conversation on automation and automation technology, cause it's not just the machines, it's, it's how people are working. The machines is really changing. Uh, we're seeing shortages in skilled maintenance and operators. We're seeing higher a trend into digital technologies to help us to support some of those people, to be able to run the machines they've got. So, uh, some of the trends, the other trends besides sort of skills and then the advancement of the technology within automation, uh, we're seeing a lot of sort of inline testing. So while a product is being manufactured, we're seeing test stations and test data, uh, being involved in that to make sure that we don't get to the end with a finished good that's defective that we pick those out part way through the line, uh, you know, retool them.
Speaker 1 00:03:54 And then maybe we insert them back into the line so that we get a high quality output versus more, uh, more throwaway parts. Uh, we're seeing a lot of focus on data obviously, and I'm going to talk a little about that today, but lots of data, really big tail on that. And we're seeing a lot of proliferation software. So a lot of point solutions where people are trying to address whether it's scheduling with people or products or machines and operation quality. Uh, so just a huge proliferation for people on the floor of what technology is coming to optimize production.
Speaker 0 00:04:25 I'm getting the feeling from you that like the word fast and might maybe time to market are all becoming very important parts of automation, but let's maybe talk a bit more about IOT and industry 4.0, kind of from the old
Speaker 1 00:04:38 Overall trends that you see. Yeah, for sure. So, so as I just sort of mentioned earlier, you know, data is continuing to grow in importance and you see a lot of trends. If you look at, you know, some of the, you know, the think tanks around like gardeners and others talking a lot about data, see data is becoming more and more important and a lot more growing areas in that we still see a lot of sensors, uh, and more sensors coming into the market where people used to be in continuing to get information off PLCs. We're seeing a real growth in, in sort of independent sensors and data coming off, whether it's vibration or thermography and other things cloud and edge, uh, particularly cloud just like in every industry is really starting to come along. People talking about sort of cloud and edge and sort of fog computing, what that means to automation.
Speaker 1 00:05:18 So we're seeing a lot of interest in change in that adding to sort of the IOT or industry 4.0 story, a lot of data analytics, people wanting to know what's going on now. So sort of the days of pulling lots of data and sitting on it for a month and then looking at the end of the month and seeing how we did a rear view mirror, kind of analytics is not cutting it these days. People don't know now what happened. They want to know what happened last shift. They want to compare this shift to that shift operator to operator. So it's the sort of frequency in your point, speed of data is becoming even more important. We're also seeing a lot of hearing, a lot of conversation, things like digital twin and artificial intelligence and machine learning, a lot of collaborative technologies as well, sort of augmented reality and virtual reality.
Speaker 1 00:06:04 So I'm feeling lots of buzzwords out here, but they all kind of apply to manufacturing, a lot of integration between different machines and different software stacks. Also seeing, you know, some of the traditional stuff we've always focused on, uh, is still key. So things like, uh, we, um, you know, driving down costs, increasing production without having to add more equipment. So there's lots of that kind of work still going on, where we're still looking at the fundamentals of I can call it that. And then finally services is huge. So it's one thing to have a strategy and to talk about industry 4.0, but, um, really things like reliability engineering and leveraging subject matter expertise are growing. So it's a supporting the services around IOT, not just sort of the products or some of the trends we're seeing.
Speaker 2 00:06:49 That's a very broad overview. Thank you for that. And I'm going to make you explain a couple of the terms that I'm going to, but you're probably talking a lot to the chief technology officers out there of manufacturing companies and what's keeping them up at night. So it's probably the same thing. Now,
Speaker 1 00:07:04 Unfortunately, with automation, that's kept them awake with everything else in their sort of it lives. So, you know, security still continues to be a huge conversation point with all it professionals. You know, where's the data being stored who has access to it. Is it secure on the plant floor? Is it going to the cloud at our data centers secure who gets access to it, uh, data storage. So all of those sorts of security issues around data are still massive and they're a large conversation kind of keeping them awake at night. I think, uh, remote access is a big one as well. So if you sort of look at what's happened over the last few months with as much as I hate talking about COVID-19 cause it's, it's our new reality, but it seems to pop up in every conversation and, you know, people are now, we won't be able to fly that expert from Ireland to go to the plan tonight, Iowa, to help, to troubleshoot, you know, we're going to have to now do that somehow remotely SERMO technologies and remote access are coming in.
Speaker 1 00:07:57 And, you know, people are using tools kind of like the tool we're using that may not be necessarily set up for secure access, but it's easy. So you're seeing a lot of technologies being thrust into it, organizations that the, it folks may not want them using, like maybe didn't want using Microsoft teams or zoom they want using something that's more secure. If there has been a big picking on zoom in that one. And so that sort of conversation is continued. Competence, remote access is key now networking equipment. So how do we network maybe standalone equipment that used to be on the floor by itself? How do we build it into a larger network? Infrastructure is another big trend that I think it's keeping them awake. And then a lot of them have a digital strategy. So whether it's come from sort of a top down from the board of directors or whether it's something that the it team has come up with, or the operations team sort of enabling and building digital strategies is another big piece for most of these organizations. And, you know, they're being asked to deliver on all the buzzwords, just talked about and all the new trends and technologies, and it's a little overwhelming for them, I think to have to get dragged into some of these discussions and be the expert where, uh, there's just a lot of information, a lot of different technology out there to, to, to take them out, to do research on
Speaker 0 00:09:11 Mike. That's great. I was in a cab in New York city a couple of years ago, and we are recording this, uh, like in the middle of the COVID outbreak and the guy I happened to be in with though, we shared a taxi, he was talking about race car driving and how it's not about race car driving anymore. It's about the data. And he was talking about how, uh, his team was analyzing giga flops of information. And it's just astounding to me, where's all this data kept.
Speaker 1 00:09:38 Yeah. So it's kind of everywhere. And that's maybe part of the, that may be another CTO challenge that keeps them awake at night too. So, you know, you've got every, you know, basically everybody from Excel spreadsheets, I've been in a lot of manufacturing facilities where people are, you know, the, the main request of, of automation companies is can you give me a CSV, can you dump a bunch of data that I can look at later, how you got people using the cloud for analytics and for storage. You've got people with data centers on prem or globally either using them. So it's kind of everywhere. And, um, I think that again, depending on what the usage is for the data, uh, you're seeing a lot more, um, consolidation of that. So one of the sort of recent trends that seems to be getting a lot of, uh, visibility is MDM or master data management, I think is sort of the, the title for that.
Speaker 1 00:10:25 And it's really, you know, kind of, I'd say almost a move on from sort of whole data like concept that was big in a few years ago where we're looking at consolidating operational data and, uh, marketing data and production data and machine data into one place, then getting valuable insights out of it. And it's across locations and divisions and continents, and maybe even companies so that large, uh, exploration of data is, uh, is of interest as you can pull lots of insight out of it, but again, a security and who's got access to it. And while you're doing becomes some key parts. So I think you're right with the erase car analogy. It's the same thing everywhere. You know, data is King, but it's, what are we doing with it is really where I kind of get frustrated sometimes is, are we doing the right thing so that we're getting real value out of it? Are we just collecting data, you know, sorta for data's sake?
Speaker 0 00:11:15 Well, I'm pretty sure we are in many cases collecting excess data, but you almost have to say with each piece you import or whatever, what are we going to do? Is this going to make a material change to our business? What decisions can we make by collecting all this data? But I think lots and lots of manufacturers out there are kind of saying, let's take everything else, figure it out later, which that might be another podcast.
Speaker 1 00:11:34 So,
Speaker 0 00:11:38 Well, hold your feet to the fire. And I'm going to get you to explain to me what digital twin is. I think I understand, but I just want to hear it from you.
Speaker 1 00:11:46 Yeah. So, so I think digital twin is set, as we indicated earlier, we talked about is sort of an evolving space and, uh, it's topic. That's got a lot of conversation in the market right now. There's a lot of focus on there's some companies that have really done some great sort of early adopter work on this and have come up with some good solutions, um, my own organization, uh, we've done some work as well on that, but what it really is, is it's a way of, um, representing a physical asset, um, sorta which including the ability to sort of test it and emulate behaviors and scenarios and doing it on a virtual model. So it's, you know, it doesn't necessarily need to physically look like a picture of the, uh, of the asset that you're working on, but it digitally needs to match it. And characteristics certainly, you know, the aerospace industry has been doing this for quite a while with engines in particular, but what it does is it allows things like driving maintenance and stress testing and accessibility future-proofing design changes over the life of the asset.
Speaker 1 00:12:43 Um, it's typically integrated with a bunch of other systems, so your PLM or CAD maybe ERP system deployed or push additional information. And so it's really a, um, a way of looking at, uh, the reliability of the asset and being able to help to model that into the future. So, uh, I think the places we found it most effective are sort of in the mass production environment, so said like, uh, aerospace is a big one. Uh, there's a lot more challenges with digital twin. I think pulling it into the space of individual robots and machines that maybe aren't mass that are more customized, but that's also where the demand is going in the economy right now is people want to see those unique assets that they've built up, whether it's an assembly line or a, uh, testing line being modeled, uh, so that they can actually come in and see how efficient it's working and make tweaks to it before we spend months and months building the machine and actually having a solution.
Speaker 1 00:13:40 So it's a, uh, you know, it's a precursor of typically putting sort of screws together and we'll welding metal, uh, to build the line, uh, and, and customer demand is definitely increasing in that space. Uh, we've done some of it in the energy space where we built some digital twins for some machines who are using a nuclear, uh, which has been kind of interesting, but typically the end of the day, it's not just the technology, like you said, the data, it's what business outcomes you're driving. So how is that helping us with the design of the machine, the maintenance, the product support, uh, what's the customer user interface look like, or the experience you're gonna have with that? How do we optimize it? So there's a lot of elements that really around the design piece of it and then the ongoing. So hopefully that helps us along long answer to a short question.
Speaker 0 00:14:25 Okay. Well, I have a couple of other short questions that to you. So I wanted to also have you explain to the audience, because they're out there driving or exercising or whatever they're doing, what's the difference between a SCADA system and an MES system?
Speaker 1 00:14:38 Sure. So, so SCADA is, um, is sort of the traditional, um, methodology that we've been using for decades and decades around, um, not only focus on data acquisition and sort of a visual representation and storage of data, uh, you know, SCADA providers. And there's lots of them been around for decades and decades as a set of counter to some of them since, you know, probably it's back to the eighties and seven days did provide additional functionality and are continuing to work the way up the food chain. So they've kind of gone from that sort of data management piece to, um, more of a, uh, higher stack, including some analytics and trending and forecasting. Uh, any S on the other hand is sort of a higher level, uh, system that typically is responsible for control and driving the machine control to match the demand from the systems, which are typically like ERP, like our SAP systems.
Speaker 1 00:15:30 So what we're typically finding is the MES is sort of that high level of sitting between the machine and the ERP system and providing for full control and data acquisition for the machines on the shop floor. So what we're doing is you think of, you know, if you consider maybe a, uh, an automotive supplier who has an order for a vehicle, uh, they would push that order from the order would come into SAP or whatever system they're using. It would, uh, based on the model number, push, uh, features and requirements of what that car looks like, uh, into ERP ERP are then pushed into the MES and MES would then filter that down through a number of systems, including the automation systems to build the car. So, so let me ask, so it's typically viewed as the higher level that sort of sits on top of SCADA and it can contain SCADA like features and escape the system.
Speaker 1 00:16:20 So, um, the system that we actually provide, we do have a full stack MES in it, a suite, which is part of our solution. We'll talk about that a little bit longer, but, uh, basically we think about where MES plays. It can play sort of in the Greenfield space where there's clients who have nothing, uh, we're basically manually loading recipes into the PLCs to drive machines, or it can operate as kind of a broker. So middleware system between ERP and maybe that recipe management and the machine control. So, uh, and again, he asked is not monolithic. It's kind of a, a number of different systems and modules to work together to allow us to sort of automate the equipment and room and remove some of that manual intervention that used to go on.
Speaker 2 00:17:03 Thank you for that. And so what are some of the major things that are happening in automation systems deployment? I mentioned speed. You mentioned that automation is getting more complex, what else is going on?
Speaker 1 00:17:14 Yeah, so I think, yeah, the complexity is interesting word, and I think we kind of touched on this a little bit earlier, but things are getting much more complex and intricate and sort of predicting the automation space. Um, I think, I think what we are looking at is really services now is sort of the new, the new frontier that companies are really forced to be forced to look at. And I think that's that because we're seeing that growth in complexity with automation, there's really a huge growth in systems in services that have to feed that. So I know we're seeing demand for like, uh, augmented reality and virtual reality and training requirements to try push some of that knowledge into the organizations that may not have that deep skill, as well as we're seeing a real focus to sort of augment customer staff, to backfill for skills gaps.
Speaker 1 00:17:59 So, you know, it's interesting how many companies now are having to hire controls engineers, where they never did before, just because the machinery is so complex that they, they just have to have people on site. So I think a lot of companies and ATS is one of them are seeing that as a gap that they're filling. Another thing I think we've had to do is providing sort of directive and timely insight on to machine operators, maintenance and production staff. So companies are needing more tools to help to manage those procedures. And, uh, some of them we have to manage sort of remotely, um, with COVID. So if you think about, um, in the past, uh, there may have been one guy in a company who is the expert on, uh, controls, and he would fly all over the world solving problems for the manufacturer that happened in their equipment. Well, that, that can't happen anymore and the COVID world. So we're now in a place where having those remote technologies and having that skill set shared across multiple people is going to be more and more important. So, uh, again, just in reinforces that sort of AR VR shift that we're seeing right now in the industry,
Speaker 0 00:19:06 I think we're going to look back at this COBIT time and also kind of pull out some of the things that have pushed industries and, and, uh, into just acting differently. So you have a product in the automation industry called eliminate. And can you tell the audience what is unique about this software?
Speaker 1 00:19:23 Sure. So, so the product we have is called illuminated manufacturing intelligence, and a sort of like one of the earlier questions around industry 4.0, we're an industry four point sort of solution. Uh, we, it was developed by ATS and, uh, sort of our sort of higher level. Our tagline is a machine builders building software for machine operators. So ATS has got over 40 years of experience building automation equipment and servicing automation equipment. And what we've done is sort of taken all of that, uh, knowledge and skill that we've built up over decades of building really great machines and put that into a software that allows people now to operate the machines and, uh, provide lots of, uh, lots of, sort of benefits, which I'll hit in a second. So, um, our system works across a number of machines, not just ATS, so it works on virtual controllers and PLCs and our response to third party equipment, not, not just ATS.
Speaker 1 00:20:19 So it's really focused on discrete manufacturing. So, uh, we've got examples using medical devices, uh, consumer packaged goods, like tube filling and things like that. And that we work in the nuclear and energy industry, including solar panels, lots of consumer products, uh, electronic, uh, electric vehicles is a big focus for us, particularly battery manufacturing and control a lot of work in automotive and OEMs, uh, some work in aerospace, uh, radio pharma and pharma, and dosaging the scene machines. So a sort of a broad base of that discreet spaces where illuminating plays. And what's really unique about it and said is that we are, it was, the system was built by machine builders and operators. So we have a really powerful manufacturing platform that's based on, you know, tens of thousands of machines being designed and rolled out and supported globally by ATS. And we've taken that and wrapped it into a software package.
Speaker 1 00:21:12 So all we do is automation and manufacturing. So we have a deep understanding of that space, and I think we've reflected that as best we can in our solution. So sort of just to sort of stuff, a little bit of the pitch on that for the last piece on this. So, um, what is it we're an end to end manufacturing control solution. Uh, we include some functionality that basically goes from the PLC all the way to the ERP system. So we've got a full MES. Uh, we have all the solutions in vision and vision integration for operations. So faults, for example, um, we support testing. We have manual stations for those who are still doing manually include autonomous guided vehicles and control of those and management. We have predictive maintenance, uh, preventative maintenance, uh, data analytics and the data analytics package as well as simple reporting. And it's really a, you know, if you look at, I just described a lot of things there, but it's highly graphical, it's highly intuitive, um, very easy to use it doesn't require a lot to figure it out and get going quickly. So, uh, even though it's got a lot of complexity in it, uh, we try to make it really simple to use and deploy.
Speaker 2 00:22:18 So Mike, like who, what and where, uh, like if I let's say I have a 10 station automation system, it maybe wasn't built by ATS, uh, what kind of system can it fit on?
Speaker 1 00:22:30 Yeah, so, so basically we go sort of top to bottom. So we have, um, it works with the set on all types of machines and all types of controllers. Uh, so, uh, we're less concerned about what it is and we can, I don't think there's any until you've been stumped yet, which is kind of interesting, um, but it can be installed and configured remotely. So that kind of works well right now and sort of our COVID world. And we don't need to necessarily be on site to do it. We can remotely install and get it going. Uh, it runs on the customer's prem. So it's, uh, which at this point is quite often a preferred method per systems that are integrated with machines and controllers and a quarter production. So, uh, we're sort of an on prem solution for that. And it, we have single cell machines.
Speaker 1 00:23:11 So we've got clients who are, you know, they have a great idea. You started the entrepreneurs of the world who have a great idea for a product. Uh, they maybe get a couple of manual stations and have people building them, building their product by hand and working out the kinks. We use eliminate for that, as they progress into, into automating that process, we can grow with them. And when they go to full automated production lines and multi-site and global locations, we can also grow with that. So it sort of starts, it, it can start at baby steps and go right all the way through to full production. Or we can just jump right in and do full production. Let's say most of ours are full production cause that's where people tend to get most excited. But, uh, it's certainly a, um, a solution that scales from small to extra large and everything in between.
Speaker 2 00:23:57 So when it's aimed at then medium to large manufacturers and high value and high volume manufacturers.
Speaker 1 00:24:03 Yeah. Yeah. So, so, so said, we kind of got the sort of starting people are starting their digitization journey. So, you know, it's interesting, you know, some, some companies start with what the real vision that we want to be immediately digitized, and we want to be using all the newest technologies and other started a little more manual. So we tried to pick that up with, um, said multi location around the globe, um, using eliminate on everything they have from packaging and kerning machines to filling and, uh, assembly. Uh, we have this, when I say we have a global footprint of multinationals and also a lot of small to midsize boutique manufacturers and testing operations as well if testing that sells as well. So we, we support them all. We have a global services team, uh, that gets the customers up and running and does training and ongoing management. And then we'll continue. We have a reliability engineering team and some technical SMEs who can keep the systems running once they've installed them. So we know not everyone has that great it organization. That's great deep technical skills. So one of the things that differentiators for eliminate roommate is that we've really focused on how do we enable people with services and, uh, and, and sort of deep SME skills to get them up and running and keep them going.
Speaker 2 00:25:16 Thanks, Mike, a big challenge in automation is that it's really hard to connect machine vision, uh, say a fault that happens to an event. And, uh, I still remember this as my days, send an applications engineer and, and S and in sales where something happened and we had to get the high speed camera and check it out to have cracked the code on this
Speaker 1 00:25:39 From our earlier conversation. Yeah. So, so we've, uh, we've integrated a number of vision solutions. We've got about four or five in the portfolio now, but the one that people really resonate with and it's been around for almost, since we started working with illuminated is something we call a debug camera, very sexy marketing name for it, but it's basically exactly what you're talking about, which is, uh, what we do is it ties faults, defects, and other sort of trigger events, uh, on the machine, uh, or through the operation to a video image in real time. So, uh, you basically get a, a really focused short video clip of the issue acid occurs. Um, so basically if you've got a fault, um, you know, we use, you have a camera station on that machine. It captures when the fault occurs, it triggers and captures the image where, uh, we get a snip before the fault happens in a snippet after the fault, and while the fault is happening.
Speaker 1 00:26:33 So you're really getting this little parsed out, uh, video clip that, uh, is tied directly to the fault. So when you go back, either in real time, or at the end of the, to see what faults we had a, you can go back and say, okay, we had five of this types of fault, and we've got five video clips of that fault occurring, and they're all tied. They're all timestamped, they're there in the line of the actual production information. And it's just way more efficient than putting a guy or a gal to sit beside the machine and watch it all day and look for faults or look for defects or, or parts that aren't being produced or operational issues. Uh, you get that little snippet, you can analyze it and watch it over and over again, it's stored with the machine and the machine data, and then it's, uh, it will also, uh, be available in the future.
Speaker 1 00:27:19 If you look at one of the other things we've done around vision, so that's sort of the base one is debug what we've also done. Some really interesting work around a machine learning with quality control and defects. So basically what we can do is we take images of product, make an assessment of what good looks like, uh, using operator information and input. And then we can automate the process and actually have the operator not required. So, um, we've done some really exciting work with a number of clients around machine learning, which is now part of our portfolio. And again, another way of using vision vision's really, and, you know, it's probably one of the things we didn't talk about earlier, but it's just become a huge, uh, area as well. And, and that has been unique thing. You mentioned that illuminated, so we've actually tied vision into machine data. So they're not standalone systems, it's all part of the same report. It's all part of the same process,
Speaker 0 00:28:10 So we can retire the high speed camera. Right?
Speaker 1 00:28:15 Well, some of those areas that I think that a big part of it's a scalability thing. So some of those high speed cameras are, you know, 150, $200,000. So they're, you know, they're very big, very expensive piece of equipment that when you're building a machine is very critical, but when you're actually an operations mode, we quite often find that, uh, you know, it doesn't require that to roll in the big guns for it. Um, and, and certainly there are some instances in super high, high speed machines where they still use them. But, uh, you know, we've found that a lot of our ENL and machine builders are using, uh, the debug camera as part of eliminate to replace your $150,000 high speed, big gun camera. So, yes, I think we're maybe soon to retire in number two, that everything has a place.
Speaker 0 00:28:55 Absolutely. Um, so, you know, one of the big challenges, right? If, if you're integrating IOT and you're buying a product like the eliminate program, do I need to also hire like a data manager or a data decision person? Can you kind of go into that a little bit?
Speaker 1 00:29:10 Yeah. So we can try to be really directive about that, that we tried to build a roommate in a way that it really drives outcomes versus just data. So what we've tried to do is say, we, you don't need a data scientist, we have those, uh, you don't need analytics, people. We have those. So we've built things like, uh, analytics to review performance data and make recommendations right into the system. So, as an example of that, we have a predictive maintenance module, um, which instead of just presenting a whole bunch of data and probabilities to it actually looks at the machine at the components and identifies the most at risk elements of the machine. And it reports and dashboards and alarms and alerts. And basically it says, you know, if you've got a thousand grippers on your machine or a thousand servos or 5,000 servers, like they have one on one in one operation, we can actually look at that, identify the most highly impacted ones and say, you know, definitely look at the thousand, just look at these six cause he's six are really a problem.
Speaker 1 00:30:08 They're gonna have a problem in the next little while. So what it does, it looks at outliers and focuses your maintenance plans on, uh, actually taking action on the things that need to be done versus a whole bunch of different data points. So again, it's focusing on outcomes and results and kind of removes that need for deep analysis. And I think most companies do not want to hire data analysts or data scientists. They'd rather just be given outcomes and things to do. It's funny, it's kind of the fascination with technology. I think a lot of us has had was, you know, at one point it was just, how cool is it to get all this data? I think now people are like, you know, I don't want any more data. Just tell me what I need to do, you know, help me make my job easier. And that's really what we've tried to do is eliminate as focused on the outcomes, not on the, uh, uh, you know, the gee whiz, exciting stuff that went in.
Speaker 0 00:30:53 What are the things that we talked about prior to the call was about remote learning. And I think this is a huge thing because of course, he's got this massive skills deficit in advanced manufacturing. We have a lot of unemployed people right now. Um, but how have you cracked the code on remote learning as well with this?
Speaker 1 00:31:10 Yeah. And so we actually just, as funny timing is always interesting, literally a couple of months before, maybe three or four months before COVID came in, uh, we launched a product called smart coach. And, uh, basically what smart coaches is. It's a system that's built for remote learning and it's part of the, our sort of digital solutions portfolio. So what it does is it's, it's, it's bundling a bunch of existing technologies together. So we're going to do a little, uh, you know, repurposing, which I think is good. We don't have to create everything from scratch. And it uses sort of simple tools and voice prompts. And what it allows you to do is have your subject matter experts. Um, and also customer, uh, experts, uh, build up sort of a sizeable library of video and data, augmented trainings with kind of minimal investment from our ATS staff and very little ongoing support.
Speaker 1 00:31:59 So that using, uh, you know, so things like HoloLens and other virtual, um, devices for recording, we can then take, uh, take imagery and record people, performing tasks, uh, best practices bring in other data sources, uh, kind of capture some of that tribal knowledge that some of the people have on the floor, as well as taking some of the knowledge, maybe from an ATS person who built the machine and kind of pull that all together and make it in a really easily digestible format. So what that does is you, you pull together, um, you know, five minute video on how to, how to replace a part or how to do a piece of maintenance. Uh, we do it fast, done almost in real time. It's stored, uh, tying it into things like eliminate, and then whenever there's that machine event, that video can be easily popped up and pulled up and you can have basically any operator go out and do the work on it to do the maintenance tasks. So I think there's a, you know, it's already easy again, we've been doing sort of video and recording for a long time. And, uh, what I think has changed now, and I'm not sure that we practically just gotta work out a solution for it is just making it super easy. That basically an operator with certified minutes of training can make his own operator video on maintenance tasks. And that's a huge, uh, a huge, a huge change from where we've been in the past.
Speaker 0 00:33:19 It sounds very exciting, Mike, you know, you can take something like a, a change, you go to a robot end effector or something, put it in a video and you could use that thousands of times, right. Because, uh, changing out a robot and effector is probably very similar, across many different types of robots. So that's very exciting. So I'd like to ask you a question about maybe getting your crystal ball out and saying, what is the future for this type of software? It almost sounds like we're here now, but, uh, I'm already asking you what the future is.
Speaker 1 00:33:47 Yeah, yeah. And I think it's, yeah, we're definitely in, it's not like this, this is this type of software is permanent for awhile. And, you know, ATS, who've been in this business for 17 years of building great software, like this eliminates history. This is our 18th year of, of, of eliminate. So I think there's still a huge future for it. The one thing I would say, you know, there's a lot of players in the market. Uh, so manufacturers have a lot of choices. There's a lot of different companies to choose from. There's really no definitive market leaders or massively dominant players, uh, that have sort of that only in this space. And I think that that's good because it gives people a lot of choices. If you've got specific use cases you want to address their specific needs, um, you know, there's, there's, there's probably a software for that or an app for that.
Speaker 1 00:34:32 Um, but what it also does is it really forces those of us in the industry to continue to innovate and differentiate. So, you know, there's no room for complacency in this industry because one it's growing so fast and two there's other technologies they're sort of on the sidelines. Like we just talked about remote, remote learning and augmented reality and virtual reality and digital twin we talked on earlier. So there's lots of exciting parts coming into the industry that is going to force those of us that are playing this space to continue to really innovate and move forward. So, uh, and stay relevant. So I think that's great, uh, for the industry and also for us. And, um, I'd say for, for illuminates specifically, we have a really significant roadmap that we've built up over the last few years of new developments, uh, that are driven by industry trends.
Speaker 1 00:35:19 Uh, so we've talked about some of them, um, I'd say this also been driven by sort of consolidation in the market of players who've come and gone. And, uh, and just a lot of customer feedback. So, uh, based on that, because a ton of white space in this, uh, in the future for this type of software and all that's gonna do, continue to just serve customers to, to, to have way better choices and produce better product in the future. So I think we're, we're still in our sort of junior, if I think about back to our dogs, when we started our talk, our puppies are not Bobby's anymore. We're, um, we're, we're young adult dogs and, uh, I think there's still a lot of growth and excitement to happen in the industry. And, um, I mean, good Luminate will be a great part of it going forward.
Speaker 1 00:36:01 That's great. Well, thanks, Mike. And I'd like to thank you for taking time out of your day to chat with our audience. If some of the people out here have some questions, uh, how can they get in touch with you? Sure. So, uh, two things. So, uh, the, the, we call the product eliminate, but it's actually eliminate manufacturing intelligence. So if you go to <inaudible> dot com, you can find more information on us there. And if you want to reach out to me directly, uh it's and tidy MTI, D
[email protected], or you can reach me on my cell is (519) 694-8666, or of course, via LinkedIn. So any of those will work, but, uh, yeah, it's been, uh, it's been a pleasure document, Jim. So thanks for having me on you're very welcome. If you like this podcast, please rate us wherever you pick up your podcast.
Speaker 1 00:36:51 Five stars means a lot to us, but more importantly, tell your friends about it, send them an email. You can tweet us at the hashtag robot nation podcast. And if you'd like to get in touch with us, our email address is robot nation
[email protected]. If you have an idea and an interesting company or technology, you'd like to be a guest or nominate someone to be guests, please get in touch with me by sending me an email and we'll see you next time. Thanks for listening. Be safe out there. Today's podcast was produced by customer attraction, industrial marketing, and I do thank my nephew, Chris gray for the music Chris Coleman for the audio production, my partner and our partner, a three the association for advancing automation and painted robot who hosts our site and integrates Zoho into robot nation.ca.
