Mike Bowers: 0:03

You need an IoT platform that can add the data, enrich the data, reshape the data to, to work the way that you want it to work. So that's a challenge, but it's not hard if you have a good IoT platform that can take the data and reshape it and enrich it, add more data to it. And so that, so that's where the companies are now turning to these IoT platforms to do those, kind of gather the data, transform it, enrich it, improve it, standardize it, and then deliver it to where it needs to go automatically.

Tom Raftery: 0:34

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Sustainable Supply Chain Podcast, the number one podcast focusing on sustainability and supply chains, and I'm your host, Tom Raftery. Hi everyone. And welcome to episode six of the sustainable supply chain podcast. My name is Tom Raftery. I'm excited to be here with you today. Sharing the latest insights and trends in supply chain sustainability. Today, we're talking to Mike Bowers, we're talking about manufacturing and IOT platforms to help enable sustainable manufacturing. In next week's episode, we'll be talking to Erin Gilchrist from Intellishift about sustainable shipping. In two weeks time, we'd be talking to Ethan Soloviev from HowGood about sustainability and food supply chains. But as I said today, it's Mike Bowers.. Before we kick that off, though. I want to take a moment to express my gratitude to all of our amazing supporters. Your support has been instrumental in keeping this podcast going. And I'm really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like-minded individuals who are passionate about sustainability and supply chains. Supporting the podcast is easy and affordable with option starting as low as just three euros or dollars a month. That's less than the cost of a cup of coffee and your support will make a huge difference in keeping this show going strong. To become a supporter. You simply click on the support link in the show notes of this or any episode, or visit Tiny url.com/SSCpod. Now. Without further ado. I'd like to introduce my special guest today. Mike, Mike, welcome to the podcast. Would you like to introduce yourself?

Mike Bowers: 2:18

Sure. Thank you for having me. My name is Mike Bowers and I'm the chief architect at Faircom. Faircom is a database company and they hired me to innovate their database product line, to create the next generation database. Faircom has been about in business about 40 years, well over 40 years, and I have been doing software development for almost 40 years. And, and so it kinda works out nicely. I, I focused over a decade of my career in manufacturing. And had worked at the Mitsubishi Silicon America. Worked at Freightliner, worked at in agriculture manufacturing, building these big one ton bales and one ton, one ton bale machines. Lots of automation, lots of robotics in integration of equipment And so when Faircom brought me on board Faircom, had started a new product line called Racom Edge, to, to create an IoT platform to collect data more efficiently and deliver that data to any, to any major system in the factory. So we could collect data using all these protocols and transform it and deliver it. That's gonna be the theme I think of this discussion today because to create sustainable manufacturing, we need to get that data and we need to do it much more, much more efficiently at much lower costs so factories can take advantage of it and and create a more sustainable manufacturing environment

Tom Raftery: 3:44

Okay. If, if I were to ask you, Mike, what is kind of key to factories becoming more sustainable, what would you say that would be?

Mike Bowers: 3:55

Yeah, I think the the key, the key points are, is to, is to reduce waste caused by defective products, to reduce power consumption, to reduce equipment failure, to improve equipment productivity, to replace equipment with more efficient and more effective equipment. And to, to do that, collecting the data about what, what is really going on is key to making all of the equipment more efficient and more sustainable. And so I believe, you know, a lot of people say data is the future, blah, blah, blah, but it really getting the data is, is critical to know what's really going on. And I learned that in my very first career, very first job in Mitsubishi Silicon America. I wore the bunny suits, you know, I had, we went into the factory and we had to put the bunny suits on and they were all clean rooms and we did a ton of work collecting data to make the equipment more efficient, to make it like I said, reduce power consumption was a, is a big deal in the semiconductor industry. And we had defective products. We had lots of defective products and the equipment was failing all the time. And so we, my job was to gather the data out of those machines to track really what's going on so that we can make that equipment more efficient and effective. But that was expensive. I was expensive. You know, hiring people like me is not cheap., so, That was early in my career, so I've been passionate about how can we automate the collection of, of data so that you can create sustainable factories.

Tom Raftery: 5:31

So were you trying to put yourself out of a job, Mike

Mike Bowers: 5:34

you know, not really what I wanna do, , that's definitely not what I wanna do, but what I wanna do is make add more value. So, right. The problem is these pieces of equipment for manufacturers have all kinds of proprietary data interfaces. So those interfaces being unique to every piece of equipment makes it very expensive to get data out of the equipment. Over, over the years, there are dozens, well, even more than dozens. I mean, there's scores, many hundreds of protocols that people have tried to create to say, this is the one, this is the protocol that will solve the problem. We can finally just plug and play this equipment in. Data will flow. And until recently, it really hasn't ever taken off. Every, basically the equipment manufacturers say, well, this is our magic protocol that works for all of our equipment, but very few factories have the luxury, or it's not realistic to have one vendor supply all their equipment. It just is not real. So you can't rely on one magic protocol for one vendor. So. There's, it's a real challenge out there and I, what I want to do, instead of spend my time trying to write binary bit code to go and say, okay, this bit means this, this byte means that, let me convert, you know, and get down to that level, which is really tedious. It's not fun. I wanted to bring automate all of that data collection using modern technologies and so that I could, as a programmer, add more value. So it's not replacing programmers, it's putting them to better use because now programmers can take all the data that's being collected automatically and and do value add to it. We could do analytics on the data. We can really, do overall equipment efficiency calculations and measurements, and figure out what really makes a difference in a factory. That's where the value is, not the protocols. So really it's, it's just getting, getting the people who cost a lot of money to do more valuable things.

Tom Raftery: 7:37

Sure, sure, sure. I, I was just, I was, I know I was being facetious about it, but I was just thinking, you know, when you were collecting the data initially, I dunno, were you going around with a clipboard and observing dials or were you downloading data from the machines and then uploading it somewhere else manually? You know, what, what, what was the system that was in place versus, you know, what you've automated to where it is now?

Mike Bowers: 8:02

That's a great question. And in the, in all, because I'm a, a software programmer, it's all been electronic automation for me, so I've never, but, but you have a great point. A lot of operators are walking around with the clipboards writing down, you know, have processes and writing down numbers, and then somebody puts them in spreadsheets and, and then the engineers crunch the spreadsheets. And so that's really inefficient. And you can't collect much data doing that, which means you really can't calculate equipment efficiency very well. So that's why they hire someone like me to come in and automate the data collection using electronic interfaces. But those protocols are just so different in every piece of equipment. It's extremely expensive. A project to, to do one, one factory. I was involved with building a whole new factory from scratch. My job was the automation and just the automation alone is millions of dollars to hire people like me to come in and program all that. The hidden cost is that once you program it all and you think you're done, you're never done, you get a new piece of equipment, then you gotta go and start over again on that one. And then, then they say, well, I want this new piece of data. If I only had this data, you know, the, the The CEO or the, or the CMO comes in and says, if I have this data, then I can do the calculation and we can save this much money. So then you go back and try to collect it again. Then you, you go and you calculate how much will it cost to go gather that data and, oh, another $200,000. And the guy goes, well, 200,000, forget it. It's too expensive. And then there's risk. A lot of software projects fail. You know, you start them and then the person leaves or the person doesn't really know what they're doing, or it just, there's cost overruns and people give up and say, forget it. It's just, I can't keep spending money after this automation. So the thing that stopped manufacturing is this proprietary protocols and the expense of getting the data out of them. So that's, that's been my mission for, for the last almost 40 years of. Can we just plug, is there a way to do this, to plug and play this equipment in and, and avoid all that headache?

Tom Raftery: 10:07

And is there

Mike Bowers: 10:08

Yeah. Yeah. And the , there's been a lot of protocols over the years trying to do it. A few protocols though, I think have finally achieved the reality. And there's OPC. OPCUA is a, is a new protocol, and that one is picking up steam, but it's still, it's, it is really complicated. They've overcomplicated it quite a bit. So complication means more money, means harder, more money for the equipment manufacturer to put it in their equipment. So OPC is not my favorite one because of the costs for the manufacturer and for the automation engineer like I am to go and get the data out. It is standard. They, I think they finally achieved a level of standardization there. But then over the years I've learned that in, in the IT world we achieved the same results in a simpler way and the IT industry. And that's, that's where I moved my career from manufacturing to IT in the middle of my career. And that's where I am now. I'm in both now 'cause I, I'm building software for manufacturing. But the the, the key is to have an open standards that are truly open and to keep things super simple. And there's two standards that have really achieved that. One is called MQTT. It's Message Queue Telemetry Transport, but it's just a fancy way of saying publish messages. This push technologies is a really important concept where you can, when a piece of equipment says, I've got some data to share, it just pushes it out there. A lot of other protocols, including OPCUA and it's a polling technology. You have to go to the equipment and say, do you have some data for me? It's like a little kid in the car saying, are we there yet? You know, , you're just going gimme some data. Gimme some data, and it's really painful to write the software, but when you push it, the equipment just says, here, take it. I got it. That alerting is easy. Commands are easy.

Tom Raftery: 12:00

Sounds more efficient as well.

Mike Bowers: 12:02

it's very efficient. And so that protocol was invented by IBM 20 some years ago to be the simplest MessageQ technology and they, they achieve that. I worked with message queuing in the semiconductor industry and that's how we integrated our factories using a more complicated proprietary message queue. But all of the semiconductor industry uses message queues to, to integrate all their factories because it's the right way to go. I mean, it's so easy to, to do the work and, but the other piece to the puzzle besides this push technology, which is, by the way, MQTT is open source. You can get free ones, you can get brokers from, from vendors like Faircom has a mission critical MQTT broker built into our, our IoT platform because the open source ones really aren't for mission critical use cases. But the, the critical thing on top of MQTT is JSON. JSON is a, is a a way to format the data, but what, what makes JSON really the winner and, and it's completely taken over the IT world. Everything is done in JSON today, and the reason is that humans can read it. They may think, well, that's okay. Why is that a big deal? Well. If you, if you look at the bits and bytes that come out of equipment and, and you try to read those, it's just, it, humans have a really hard time reading that stuff. It's just, it is gobbly gook, you know, and it super hard. It's, it's just hard to parse it, hard to, to, to use it, to convert it. And hard means expensive, hard means project risk, it means it means potential for failure. I've spent so many days troubleshooting these binary protocols and going, if that bit is on, then expect these bytes to come out and do this and do that. It's just, and then something else happens. It's not working and you can't read it, so you put it anyways. It's a nightmare. JSON, you just look at it and any, anyone can read it. Every single human on the planet can look at that and go, oh, there's a tag that says temperature. There's a value that says 21. I know what that is. And you can troubleshoot it. And it's easy to plug, you know, plug and play everything together. So when you combine MQTT and JSON, you get plug and playability with at very low cost. So the, but the trick is how do you get it from that proprietary binary protocol that's in the equipment to MQTT to JSON, and that's where you get an IoT platform. So like Faircom's IoT platform, is called Faircom Edge 'cause it's supposed to run on the edge of the computing environment. So basically it runs in the factory. You plug it into the, to a piece of equipment and you tell, you tell a platform. This piece of equipment speaks Modbus, or it speaks the Siemens S seven protocol, or Alan Bradley's ethernet IP protocol, whatever protocol or speaks OPCUA, whatever protocol you like, you plug it in, you tell it what what protocol it speaks. You say, okay, I want these pieces of information. So you just say, I want temperature, I want air pressure, I want vibration, I want, you know, and you, you spell it out, and then the platform does all the binary stuff for you. It convert automatically converts all that binary stuff into JSON and then JSON becomes your standard for communicating in your factory. And then the platform could take the JSON and then deliver it to any system that used in your factory. So it could go to your manufacturing execution system, your ERP systems, your SCADA systems Thingworks, you know, whatever, whatever systems you have. The platform is this universal translator, gets it outta the machine, puts it in a universal format like JSON, and then delivers it out to the targets, and it guarantees delivery. You don't have to worry about because it's mission critical. You know, it stores the data. It's there. You can go back weeks, months in the past and see the data. You can read it. You can say, oh, that message had was bad from this piece of equipment. That's why we got a bad result over here because you could query it. So that's, that's one of the unique features of the Faircom product. Because we are a database company, we have embedded our database product right inside of our IoT platform. So you can, you have the entire history in a SQL queryable way to go query anything in the past, see what happened to get the data out. And we also support IT protocols. IT loves sql, so they love to use the SQL query language, but IT also likes they love JSON. So. It's, we support JSON, they can come and get data through JSON. They can query our whole system using JSON. They can also come and get the data through MQTT, which is a push system. So we can push data out to any IT system like the cloud. All the cloud supports MQTT as their primary way to get manufacturing data into the cloud. So the, so the platform is this universal translator that delivers data where it needs to go at very low cost.

Tom Raftery: 17:01

And what kind of data you looking for or is a manufacturer looking for? You know, what, what, what's the kind of the, the, we've gone through the the standard for message delivery, but what's the standard for deciding whether something is efficient or not? Whether it's working well or not?

Mike Bowers: 17:54

Right. So you wanna for example, equipment failure is a big deal because it's, some equipment can cost you a million dollars or more. And wanna know if it's working well or not. Vibration data is is really key to that. So, sometimes equipment manufacturers include vibration sensors built in, but a lot of times companies like to stick vibration sensors right on the equipment and then collect the data, collect the vibration data, and track it over time, and then they can see the range of normal behavior. And as that data starts to skew out of the normal behavior and gets into unknown numbers, numbers that are out of bounds. It's easy to say something's going wrong. And so it's a trigger. Some people, some manufacturers that we work with are getting really clever. They're using machine learning. So they train, they take the data coming from these vibration sensors or other sensors like temperature, pressure, humidity, et cetera, and they take that data, send it to me to be trained. And they usually send that to somebody who's an expert in working with machine learning data. Like, like a data scientist. They, they gather the data, they, they train an algorithm to detect what is normal and what is not normal. And then the algorithm just runs it tells you, oh, something's going wrong. And so they, so all kinds of data can be used to detect when a piece of equipment is not working at it efficiently or needs to have maintenance. It's, it's pretty easy once you, once you can collect the data, it's really easy to say, oh yeah, this data is out of range. Let's go fix the equipment. So that's a real common one. You can monitor power consumption. So an equipment that starts to go out of range, it starts to perform more poorly, often consumes more energy. So that's, it's also good to tell you when your equipment's going bad, but of course it, it performs when it consumes more energy you wanna stop that. So that's where you wanna ma monitor your power consumption. And then that gives you visibility so that when you replace the equipment or when you tune the equipment, you can reduce its power consumption. The other thing is you wanna monitor throughput through your equipment. So throughput is measured in a, in a lot of different ways. It depends on what the equipment's doing. If you have a stamper for example, a stamp process might, might be simple as one stamp and, and you're done. Or it might be more complicated where stamps turns, stamps turns. There's all kinds of ways to know, or all kinds of things a stamper can do, for example. Well, if you want to know when a process is done, you can monitor the equipment itself and you'll have triggers from the equipment. Sometimes it's really nice, the manufacturer says, I finished process, and they send an event over MQTT and then you know it's done. When you know something's done, you can track start time and end time and that gives you efficiency of how long is it taking that piece of equipment to manufacture that, that, or do that particular job. And then you can track that with other pieces, other equipment in the factory. You can track the efficiency of the, each piece of equipment as it's moving through. And you could detect bottlenecks, you can detect over time when something's go slowing down and that stampers starting to move more slowly 'cause it's getting older and you can see it used to not be a bottleneck, but now it is a bottleneck 'cause it's going slower. So the data can tell you all of that very easily, as long as you can collect it very cheaply. That's the key point.

Tom Raftery: 21:26

Sure. And bring it home for me. We are on the Sustainable Supply Chain podcast. What has any of this got to do with sustainability?

Mike Bowers: 21:36

Yeah. Well, there's a lot of aspects to sustainability. Of course, power consumption, making the environment safer and better. Power is a big part of that. So using data to reduce power consumption. Making equipment is expensive and consumes parts and, and a lot of times the equipment has rare metals in it and those kind of things. So getting the most life out of your equipment increases the sustainability of the, of the of your factory and the equipment itself. Overall, equipment efficiency is important because you wanna make your factory more sustainable. You want to reduce the times, the cycle times through the factory. You wanna reduce the cost of making the product and you wanna reduce bad product. So, 'cause if you create bad product, then that's wasteful. Either you're throwing it away and not delivering it to your customers. Or in the case of recalls, you deliver it to the customers and then you have a recall, which is not good 'cause it's then you've increased everybody involved in, in fixing that product. And that's where an IoT platform is super helpful. We're working right now, I'm working with one of the large American manufacturers of automobiles, one of the top three. And recalls are a huge deal for them. And they need an IoT platform that can track the data, collect the data in a mission critical way, so that when it comes time, when they find out that a product in the field, or a part in the field is not working well and they need to replace whether, whether it's an airbag or whatever that is. They need to know which, which vehicles have that part, which, which parts were defective. All that's data and, and it's can be automatically tracked by an iot platform and then that gives them a more sustainable way to detect when problems are happening and then fix them when they do find out that you know, and not, and not over fix. Can you imagine, you know, if there's millions of vehicles going out with the defect, it could put a company outta business if they have too much recalls. There's an air, there's an airbag company that went out of business because of that a few years ago, and I actually worked with their competitor to implement this, this very thing to make them more sustainable and stay in business I guess it's one form of sustainability, in one way. So, yeah, they, they gathered all this data so that they could detect when they're having problems and, and make a more sustainable product in the environment and and reduce recalls.

Tom Raftery: 24:05

Sure, sure. Any to, to my mind, anytime a factory is making itself more efficient, reducing waste, any of these kind of things, it's a sustainability win. And very often I think factory owners, brands, whatever it is, embark on projects through the lens of maybe cost savings, that kind of thing. You know, they, they want to reduce waste as a cost saving measure, but it ends up A, saving them cost, yes, but b, also making them more sustainable because they are reducing waste. They are getting the same or greater outputs. For, you know, the same inputs, you know, are they, so they're, they're, they're, they're maximizing their outputs for either the same or, or less inputs. So I, to my mind, that's a big sustainability win.

Mike Bowers: 24:59

I totally agree with that and and I think data is truly the key, but it's low cost data collection makes it realistic. Otherwise it's just too expensive and people just don't do it.

Tom Raftery: 25:10

Sure, sure. sure. Can you talk to any, I mean you mentioned the, the automotive company can you talk to any other, case studies or customer wins ex for example, where, you know, a company has embarked on a project and had a great outcome as a consequence?

Mike Bowers: 25:27

Oh yeah. So there's a, a company we worked with in Brazil. They produce the the rims for vehicles, and they're the largest rim producer of vehicles in the world, and they actually do a lot of work with vibration sensors. It's amazing what you can learn with vibration. So they, the, the challenge they were having is collecting enough vibration data fast enough. The software they were using would collect like one reading a per second. And that wasn't, that wasn't, that's nowhere near fast enough. And so.

Tom Raftery: 25:59

no.

Mike Bowers: 26:01

They need faster data collection and then they were sending it to the cloud. And one of the challenges there is when it goes to the cloud, you got all these complexities because the network between your factory and the cloud can go down, the cloud service can go down. The latency, which is the time between you send the data and arrives in the factory, can cause problems, can slow everything way down. And that's why they were having all this trouble, but running an IoT platform on the edge of the network right there in the factory, collecting it very quickly, at the source gave them what they needed and what they were doing with this vibration data, they were sending this to, they have a, they have a company in Germany they work with for machine learning, and they were training the software, which is just, they just feed it the data. It runs through a statistical analysis and then it, it creates a statistical profile that says, oh yeah, if this kind of data's coming in, we have a problem. That's, in a nutshell, that's what machine learning's all about. Just learning what statistical profile is good, what's bad or what's in between. And they, and so they send it to that german com German company, they figure out, they train the models, they bring it back and run it on their IoT platform. And and they are using vibrations to now detect equipment failure and, and fix the machines before they create defective products and do, and they can tell when the machine is getting out of alignment, then they can go and do maintenance on the machine. So again, the machine lasts longer and the product that it produces is better.

Tom Raftery: 27:31

Nice. Nice, nice. And what, what would be, do you think the main kind of challenges for organizations looking to embark on a project like this?

Mike Bowers: 27:40

Yeah, the first is, of course, I mentioned it several times already getting the data at low cost without having to hire programmers. The second problem is standardizing that data. So we talked about JSON being a standard but there's one step further. The companies want to take. And, and they want certain types of data to be in their standard. So every company that I've worked with has their own standards for, oh yeah, we're gathering this data. I want the, I want this factory ID to be in the data. I want this line, this, this, this, this stage that it's working in the, the pod that is creating it. I wanted to have all that, all that data collected along with the measurement and the, the machine that produces it has no idea about what factory it's in, what line it's in. Right? So you need to be able to go and add that data automatically in the IoT platform. And you, and then the companies wanna standardize on that. So we, this American automobile manufacturer I've talked about, they have spent over well, probably eight years now. We started working with them five years ago. But they were working on this before we started with them, and they standardized their JSON structure and this is their companywide. Of course, it's a massive corporation, right? But this is their companywide standard that all data must be in this JSON format. We must include this piece. You know, these, they have a laundry list of pieces of data that every piece of equipment must produce. But again, not every vendor can do that. So you need an IoT platform that can add the data, enrich the data, reshape the data to, to work the way that you want it to work. So that's a challenge, but it's not hard if you have a good IoT platform that can take the data and reshape it and enrich it, add more data to it. And so that, so that's where the companies are now turning to these IoT platforms to do those, kind of gather the data, transform it, enrich it, improve it, standardize it, and then deliver it to where it needs to go automatically.

Tom Raftery: 29:42

Okay. And where to next for all this? I mean, we've seen the, the rise over the years, the sh the shift from clipboards to IoT platforms, you know, where? Where's all this headed next, do you think?

Mike Bowers: 29:57

you know, the, the artificial intelligence world is, is overhyped in a lot of ways, but there it's real. That, that you, machine learning is the, is the next generation. And I think as people realize that it's not magic and it's it's really not scary, it's just machine learning is statistical profiling. It's just that a machine does the statistical profiling for you. You feed it good data and you say this is good. You feed it bad data and, and it knows it's bad. And then it profiles that, and then you can send the algorithm and put it on a platform and, and detect things. So it's not rocket science, but I think we'll get more and more machine learning, doing more intelligent factories. So when they say intelligent, it doesn't mean they actually think. There's no in intelligence in any of the computer software that's a human builds an algorithm in there. It's just there'll be more data scientists and more data engineers, creating more of these machine learning learned algorithms that you can plug and play and get more value out of your data. So I see more and more of that happening. So the future I see for a factory is you'll, you'll standardize and on an IoT platform so you can collect your data, transform it. Enrich it, standardize it. Then you can quickly send it off, send that data to to programmers to use it in human algorithms, and then send it to data engineers to go create machine learning algorithms to improve your factory. And it's all based on the data. It's data-driven manufacturing, which is what the semi-conductor industry has been doing. When I first started my career, they were doing it then, you know how successful they are, and the rest of manufacturing is still stuck in the eighties, and we need to get them into, you know, the, the 2020s, the 2030s, and the technology's here, just, it's just, you just need to start using it.

Tom Raftery: 31:51

Cool, cool, cool. Now I remember, I remember learning stats in college and Oh boy, if I can get a machine to do my stats for me, I'm very happy. Absolutely. Yeah, yeah, yeah. So.

Mike Bowers: 32:01

That is machine learning in a nutshell.

Tom Raftery: 32:03

Thanks. It's no fun. So let the machines do it. Absolutely.

Mike Bowers: 32:07

yeah,

Tom Raftery: 32:08

Mike, we're we're coming towards the end of the podcast now. Is there any question I haven't asked that you wish I had or any aspect of this we haven't touched on that you think it's important for people to be aware of?

Mike Bowers: 32:19

You know, I think we've done a good coverage of everything and I. I do see this manufacturing 4.0, industry 4.0. You know, that next generation is important. Your competitors who are embracing IoT platforms to gather the data and make value out, get, get, get good value out of it, they're gonna have a, a competitive advantage. And in the, and so, but the cool thing is you can get an IoT platform that can do this job for you. And like you said, do the statistics for you, so you don't, you don't have to hire the, the stat, the stats engineer to do all this and, and just dip your feet into it. It's not super, it's not super expensive. It reduces the risk of, it increases success. So, finally, there are platforms and I designed the Faircom Edge platform to be plug and play and to be easy so that you can be successful. That's so important. So that's kind of the message I'd like to leave with everyone is IoT platforms are real. Go research them, go look at them, try them out and, and don't get left behind because this is not just a slogan or something. I'm not just marketing person here. I really believe in this, you know? Because this is real. This is happening right now and all the big vendors are doing it. Finally manufacturing it besides semiconductor is embracing this. You know, and and like I said, that one American manufacturer of automobiles, they're ahead of their competition. And now their competition is reaching out to us and going, can we do this too? But they'll. That they're, because they're trying to catch up. So this is, this is really happening. Finally, with MQTT, JSON and IoT platforms, it's finally there.

Tom Raftery: 34:00

Fantastic. Fantastic. Mike, if people would like to know more about yourself or any of the things we discussed in the podcast today, where would you have me direct them?

Mike Bowers: 34:09

Yeah, go to Faircom.com. It's like fair F-A-I-R-C-O-M. And just go to Faircom.com and look at our webpages. You'll see all, you'll be able to read very easily and quickly see what we have and see how it can solve these manufacturing problems for you.

Tom Raftery: 34:25

Fantastic. Mike, that's been really interesting. Thanks a million for coming on the podcast today.

Mike Bowers: 34:30

Thank you. I appreciate it.

Tom Raftery: 34:33

Okay, thank you all for tuning in to this episode of the Sustainable Supply Chain Podcast with me, Tom Raftery. Each week, thousands of supply chain professionals listen to this show. If you or your organization want to connect with this dedicated audience, consider becoming a sponsor. You can opt for exclusive episode branding where you choose the guests or a personalized 30 second ad roll. It's a unique opportunity to reach industry experts and influencers. For more details, hit me up on Twitter or LinkedIn, or drop me an email to tomraftery at outlook. com. Together, let's shape the future of sustainable supply chains. Thanks. Catch you all next time.