Bryan Merckling: 0:03

If you are going to try to improve yield, profitability, and compliance you've gotta have the right data to do it. That's how we create a true state-of-the-art manufacturing environment, a, a manufacturing 4.0 environment, if you will, that that is more focused on reducing waste and, and reducing rework.

Tom Raftery: 0:30

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 12 of the sustainable supply chain podcast. My name is Tom Raftery, and I'm excited to be here with you today sharing the latest insights and trends in supply chain sustainability. On today's podcast. I'll be talking to Bryan Merckling from Thinaer and we'd be talking about IOT. And then look out for next week's show where I'll be talking to Madhu from Schneider Electric about Schneider Electric's own supply chain improvements and how they made their own supply chain more sustainable. The week after I'll be talking to Eric Linxwiler from Trade Beyond about ESG and regulations. And then the week after that, I'll be talking to Pat McCullough, CEO of Produce Pay about minimizing food waste. Back to today's show. My special guest today, as I mentioned was Bryan Merckling. Bryan, Welcome to the podcast. Would you like to introduce yourself?

Bryan Merckling: 1:39

I would thank you for having me. Bryan Merckling, I'm the CEO and founder of Thinaer, a Dallas based AIOT company. We will explain a little more about what that means.

Tom Raftery: 1:49

Sure. So let's go for it then, Bryan. What is an AI IOT company?

Bryan Merckling: 1:57

Well, AIOT is, it's what they're tagging the intersection of AI and IOT. So we've been focused on that specific space for a few years now. We think there was a little bit of a a little bit of a jump the gun with AI in the manufacturing industry. So we've circled back and created a new class of data to support AIOT and we'll, that's what we'll talk about today.

Tom Raftery: 2:23

Okay, so let's dig into that. Tell me a little bit more about that. What is this new type of data?

Bryan Merckling: 2:30

So we've created a new class of data I said earlier with that companies have kind of jumped the gun with, with ai. The way we like to explain it, think about if you took your car in to a, a mechanic and the mechanic asked you the year of your car and when the, the oil was last changed. And then started making very expensive repairs to your automobile. We'd be horrified. We wouldn't, we wouldn't allow that. But manufacturers around the world are making decisions with about 25% of the data that's available to them, and that's because of all the dark spots, the analog spots, the, the machines that aren't intelligent today. So. Very expensive, very advanced decisions being made, not, not only by humans, but by AI with a, a very, very small subset of the data that's available. So Thinaer is about digitally transforming everything in your operations from the machines on the manufacturing line to key assets that run around to raw materials, the environments, the supply chain, making sure that you do have this holistic picture that allows humans to make better decisions today and allows AI to, to not make skewed decisions. If you are going to try to improve yield, profitability, and compliance you've gotta have the right data to do it. That's how we create a true state-of-the-art manufacturing environment, a, a manufacturing 4.0 environment, if you will, that that is, that is more focused on reducing waste and, and reducing rework.

Tom Raftery: 4:10

Okay. And how do you do that? Are you a hardware company? Are you a software company? Are you both? Neither? Where? Where do you fall there?

Bryan Merckling: 4:23

What's what's funny is when I founded the company, I said, we will never do hardware. We're only gonna be a software company. We're gonna build the platform, we're gonna build the analytics, and we're gonna let our customers go buy the hardware. We found that customers aren't very good at sourcing digital hardware around the world, figuring out what they need to apply to a machine to create a digital twin of a, of an older aging machine. They weren't very good at finding the right sensor or beacon to do real time locationing. So we created a group within Thinaer who does source those things from around the world, and we white label them so our customers could, could purchase them directly from us. Basically just come to us with a use case and we would have everything you need from hardware to software to outfit that use case and deploy it for you. My next, my next stake in the ground was we'll never be a deployment company. We are a software and now, and now we sell hardware, but our customers aren't very good at deploying either. It's very expensive for them and, and they're, they're busy with other things in their business. So we found that even after they purchased the system from us, it would take them a long time to get it deployed. So, so today we are end to end everything you need. You can literally point at a manufacturing location and we, we'll take it from there. We can help you decide what hardware's required, source that hardware for you, configure that pre-configure all of that hardware. We can even then go in and deploy all of the infrastructure and then launch the software, the platform and everything you need to create the use cases and, and ultimately the analytics that you're looking for to create new business insights. End to end. So started as software only, but we have, we have evolved into everything you need. Just point at the building.

Tom Raftery: 6:18

Despite your best intentions.

Bryan Merckling: 6:21

It was not our intention, but it has, it has turned out to be much easier for our customers.

Tom Raftery: 6:26

Sure, sure. I get that. And. Why did you start the company? What was the, the genesis behind that?

Bryan Merckling: 6:36

I had a, a company called Webify where we had, we had re-engineered, the way software could talk across network and across business boundaries. And webify was ultimately acquired by IBM and I transitioned into a director of worldwide software strategy at IBM. And part of my role at IBM was to identify emerging technologies around the world that would be meaningful enough for IBM. And meaningful means hundreds and hundreds of millions of dollars. Billions if you can get into the billions. Once IBM understands you know, technology is emerging and there'll be that much impact on the world and the industries, they know that they can carve out a certain percentage of it. So we got pretty excited. Hundreds of millions were exciting. Billion was pretty exciting, but IOT came along. AI was just getting started. In fact, Watson was ramping up and there was an intersection coming. And the numbers were in the tens of billions, right? 70 billion plus for that area. So, had an opportunity to work with Watson at IBM, but when it was presented to me, I told 'em I had a better idea. I'm gonna go start my own IOT company and, and be prepared or help people prepare for that intersection of, of AI and iot when it comes.

Tom Raftery: 8:02

Nice. Okay, great. And so you go into a customer's site, you survey it, you figure out what is needed, you source, you lay it out for them, if necessary. Set it up. Start the platform, what happens next?

Bryan Merckling: 8:25

Well, we actually start, there's a step ahead of that. We try to understand the business problem they're they're having or the business problem they're trying to solve. And most of the time in manufacturing it, it boils down to gotta improve yield and profitability. We've got a problem with machines breaking down that shut down the line downstream. And of course all of that impacts yield. So we usually start there. We wanna understand what type of problems that they're dealing with. The, the next step, even before we go on site, is to get a map of their facility. And, and we usually start with a map that, that shows the footprint of a, of every manufacturing environment they may have. It needs to be highlighted for us, or we work with the customer to highlight it. What today is completely manual. What today is analog and what today is, is digital. What has a digital footprint and we'll see 25% or less as a digital footprint for the most part. So, you know, 70 to 75% of their operations are generally dark. And so we start with the use case and, and, and we start with the business problems. We look at how we would solve the business problems, and then we look to find what our starting point is, what's digital today? And then we help them understand how a holistic data picture is how we really get to better business decisions today and, and more successful AI initiatives tomorrow. And that backs us, we kind of come full circle then into how we would solve the problem. So it's these sensors and we have this universe of sensors that are readily available off the shelf. 40 plus KPIs so we can track real time location, we can track temperature, humidity, vibration, voltage, amperage, rotation angles, is there water, is there light? So we'll work with them on the best sensor available from around the world. And, and we're not a sensor, I still consider us to not be a hardware company. So I am not trying to sell you whatever sensors are in my are in my inventory at the end of a quarter, I'm only focused on solving the business problem. So we'll go source and you know, just in time we'll go source the sensors that we need to solve your business problems. We will pre-configure them. We will put them into your system ahead of time so that you're simply using a quick app to associate them with different assets, raw materials, environments. So it's, it really is full service. Once, once we've helped you decide what sensors are required, we also help you decide what infrastructure is required. Same thing there. We are not an infrastructure company, so I'm not trying to just push infrastructure. I'm trying to push what we need to solve the business problem that you have ultimately. In fact, we've even done partnerships with the world's leading access point. So, Aruba, Cisco, Meraki, Juniper, we can even turn them into infrastructure. So if you've already invested in those things, we can repurpose them or add layer on new purpose, and new ROI to those devices you already have. So now we've helped you and we've, come to a mutual conclusion about what your business problems are, what you're trying to solve. We've helped you decide how to get rid of all of the blind spots that exist with sensors. We've helped you understand how most efficiently to deploy the infrastructure. And now we're onto the software. How do we deploy the platform? How do we deploy sonar, which is our visualization layer so that you can make quick use of your data. Sonar is how we democratize the data. We make that this new class of data, this new data about everything going on in your operations. We make that available to even the least technical people on your staff. It's so easy to use. It's table driven, it's graphics. It's a graphical view of all of your data. It's got really easy to use triggers and alerts engine so that you can let everybody get immediate value out of your data while your data scientists and potentially us are working with you on how you would apply AI to this new class of data. But, your people are making decisions based on a hundred percent of the data or maybe 90% of the data, not 25% of the data. Your AI is being fed with the data that it needs so that the outcomes are not skewed by just a, a simple lack of data. It it's an end-to-end approach.

Tom Raftery: 13:15

Okay. So I'm not sure which way to go on this. I dunno whether to ask you and I'm my brain's going two directions here. I want to know more about that, but I also want to know, because this is the Sustainable Supply Chain podcast, I wanna know about use cases for this data that help companies achieve their sustainability goals? So let, let's go with that one. Tell me, tell me some use cases where this new class of data can help your customers achieve their sustainability goals.

Bryan Merckling: 13:49

Sure. Yeah, I'd love to. So the first use case that, that makes sense to most people, when you talk about IoT or the intersection of AI and IoT, the first use case people recognize is generally locationing, realtime locationing of assets. So it it, the simplest, lowest hanging fruit is generally, if we know where our assets are, we can go find them faster, we can utilize them faster. It's a quick way to improve efficiency. We also then don't have to spend two weeks, or some of our customers previously spent a month or more than a month at the end of the year doing inventory, figuring out where everything was, what their shrinkage was. So there's a immediate recognition about real time asset tracking. We have taken that a, a step further. And, and the step further is these beacons, these sensors that people have historically used and that we have historically used for realtime asset tracking they get cheaper all the time. When we first started Thinaer, a, a asset tracking beacon was somewhere around $50 each. So you have to decide what things you're going to apply it to. They have to be expensive enough, and you have to lose them enough for it to make sense to put that, that beacon on it. What's happened is that the industry has continued to advance, and I'm proud to say that my team's helped some of the key players around the world redesign some of these things and make them cheaper. So now these can cost$2 or $2 and 50 cents. So a huge paradigm shift in the cost of these things, which allows us to track raw materials and assets now. So now you've gone from a thousand things to hundreds of thousands and millions of things that, that you're tracking. And so it's raw materials, it's assets. Now we can get into use cases that really start to impact waste. So now we know where raw materials go at all times. We know if they're in environmentally controlled areas. We know the temperature, the humidity of them. We know if they leave places they're not supposed to be. So we can start to impact efficiency and, and anytime you're impacting efficiency and yield, you're automatically impacting you're, you're starting to drive down waste. You're starting to drive down energy use. And so that's the fastest way in. But here's how it really starts to apply to your podcast and the efforts that you're driving around sustainability. When you start to track millions of things and those things have a battery life of a year, you're starting to fill up the landfills pretty quickly with coin cell batteries. So the, the two were a little bit at odds? Yes, we're, we're more efficient as a company. We are trying to drive down energy use and we're trying to be a more sustainable state-of-the-art manufacturing environment. But we're creating a whole lot of waste around coin cell batteries. We've created some inefficiency in the fact that we have to go replace those batteries. So maybe we're not doing inventory at the end of the year and shutting our business down. But we do have people running around replacing batteries in millions of little devices. So we have been working from a sustainability standpoint, we've been working with partners around the world for the last couple of years to first increase battery life. So battery life is now three plus years. Most of the beacons are five plus years. And there are new chip configurations that allow them to use less, less energy. And we're working with a couple of new vendors that are pretty exciting on this front. So we work with a company called Atmosic, and they've got a new way to harvest energy from these little coin cell and, and maybe AA batteries. We're working towards, we're not there yet, but we're working towards a design for a 10 year battery life beacon. So now we're 10 x, right? 10 x what we used to be. So now things are starting to better align from a sustainability standpoint. And then there's even a new sticker. There's a new Bluetooth low energy sticker called a Wiliot Pixel, and they're an important partner of ours. So we use the Wiliot pixel and we, we, and we partially, power the Wiliot pixel with ambient energy. So the energy from your cell phone, from your wifi, it partially enables those little stickers to wake up. Now we're into a situation where we have no batteries and we potentially have lifespan well beyond 10 years. So we've been attacking it from a sustainability and how it ties back to your efforts in this specific podcast. We did push up efficiency. We did, we did improve yield or we have been improving yield with our customers. We're trying to make sure the byproducts of how we're doing that don't have a negative impact on on the environment at this point.

Tom Raftery: 19:01

Sure, sure, sure. Is there a trade off in the batteries between functionality and battery life? If you are dialing down the use of the battery, are you necessarily reducing functionality or are you keeping the same functionality? And as kind of an addendum to that question, the Wiliot sensors that you spoke of that use ambient energy, are they functionally reduced or are they just as functional as any other sensor?

Bryan Merckling: 20:08

Yeah, so very astute question. It's, it's really the next, piece we had to solve as we were trying to dial back the energy usage. So we, we've got, and a lot of this is intellectual property within our company, but there are lots of use cases whether you're capturing vibration, or temperature, or humidity, or voltage, amperage, light. There are lots of different use cases. I go back to kind of how I, I said we start with our customers. We understand the business problem first that they're trying to solve. When we decide what the best sensor is, we are also deciding what the best, what the optimum configuration is. And so when we're solving for, for the configuration, we're solving for not only the battery life, but what data we need and how often we need to capture that data. So it does have an impact. In fact, the way we get three years, five years plus on, on what we call our workhorse beacon is we removed all of the metadata chips from it. So it. Only gives us location. Now if we do want to add vibration or temperature it does, it does impact the battery life. So from there, you're looking at, do we need to know three times every second? Is three every three seconds okay. Is every five seconds okay? Maybe every 10 seconds or a minute is okay. So it's, it's a balancing act between the data you're trying to get. The business problem you're trying to solve and trying to make sure we're doing this in a sustainable fashion.

Tom Raftery: 21:47

Sure, sure. Yeah. No, I, I've used the example before on this podcast of temperature sensors, you know, and if the temperature sensor is saying it's 24 degrees, it's 24 degrees, it's 24 degrees, it's 24 degrees, you know. That's not really particularly useful data. It's when it says it's 24 degrees, oh no, it's 30 degrees now it's 32 degrees. That's when you want to be alerted, right?

Bryan Merckling: 22:13

Yes, makes perfect sense. And the same thing we're working on a new, a new way to do locationing where same thing. We don't need the beacon to blast that. I'm in the same place. I'm in the same place. I'm in the same place. We need to know, we, we want the beacon to wake up and tell us when it's moving. And so there are some new options for that as well.

Tom Raftery: 22:36

Okay. And I mean, location is one use case that you mentioned, but I can imagine there are lots of other use cases. The other big one that comes to mind is the likes of maintenance. And so, you know, switching away from scheduled maintenance to do more maintenance, where the device itself says, excuse me, I need some maintenance now, please.

Bryan Merckling: 23:02

Yeah. Great. Great segue. That is our next, most most utilized use case. So when I talked earlier about these manufacturing environments that are 25% digital that's usually the machines. There are some machines that have some level of intelligence built into them. But most machines, most purpose-built machines today on the manufacturing floor are expensive, and they're old. In fact, we have one customer that installed machines in the late forties to make chocolate. And we asked them, do the machines still make the chocolate today? They do. Do, does it, do they work well, they make the chocolate fine. The problem is we can't tell when they're going to break down and stop the manufacturing line, you know, downstream. And that that impacts yield, that impacts efficiency. You're, you've got maintenance workers in there in the middle of a shift, so it impacts your resources. Has, has a huge impact. So the opportunity here is to make any machine you have smart and connected, and that has a couple of, of benefits for our customers. So the first one is naturally yield. If I know when a machine is, if I know when a machine has a problem with a ball bearing because I put a sensor on the motor, and I'm reading, I'm reading the vibration, 50,000 plus times per second, I'm doing predictive analytics. I can tell the ball bearing's going bad. I know that machine's going down soon. So now I can schedule that maintenance on the third shift or during a time when that machine doesn't need to be used and I don't impact yield. So that has a, that has a certainly a big impact on yield, on profitability and ultimately on, on the energy that that's used and waste, because sometimes they'll break down full of product and you've, you're wasting the products on it and you've gotta restart the whole manufacturing line once, once you get it back up and running. That's one place that it makes sense. The second place it makes sense is, like I said, these are expensive purpose-built machines. If we can extend the life of those machines by making them smart and intelligent. Now we've, we've had another impact on sustainability of that manufacturing environment. And we have customers that have put, we have one customer that's put 32 sensors on one machine. So a purpose-built, very expensive older machine is now one of the most intelligent machines on their manufacturing floor. By using these little sensors that we provide.

Tom Raftery: 25:50

Fascinating, fascinating. I am curious, and maybe, maybe this isn't the question you can answer, but I'm thinking in terms of the difference between, for example, and this is an analogy, the difference between the the carbon footprint of an electric vehicle versus an internal combustion engine vehicle, and when do you swap out an internal combustion engine vehicle for an ev. And if I'm thinking that I'm thinking about this manufacturing vehicle, sorry, this manufacturing machine for manufacturing chocolate that was bought in the 1940s and is working away fine, but were the manufacturer to buy a new machine that was made this year. You would have to think the energy requirements for it would likely be far less. And so I'm guessing, well, I'm not, I am, I'm, I'm trying to figure out overall lifecycle analysis. Would they be better staying with the 40-year-old machine or would they be better off swapping in a newer, smart, connected, but also machine that requires far less energy?

Bryan Merckling: 27:13

Yeah, well, I'm, I think the, the answer to that is the dreaded, it depends. So yeah. A, a machine that's been around that long, you know, certainly the electric motors that were in that machine when it was first built would not be efficient today, but, if there are components inside that machine that could have been retrofitted or had to be retrofitted, the motors that are being built today are far different than what was being built in the forties. And so most of the energy consuming components on those machines have been replaced many, many times. In fact, in fact, when we, when a, a ball bearing's starting to go bad, a lot of times if the motor's old enough, they're just replacing the entire motor with something that's newer, more efficient because it will pay for itself. Right. Great point. The energy that's required from a, from a electric motor built in 2021 is gonna be very different than one that was built in the, in the 1940s. Now, if the components cannot be recreated in a more efficient manner, there's certainly an an ROI argument for, why don't we go ahead and just replace it with a new machine and, and we would get the intelligence we need to feed AI today anyway. So there, there are, there are reasons to replace them. There are also reasons to extend the life of some of these older machines.

Tom Raftery: 28:34

Sure, sure, sure. It's like the story of the guy who had the same axe in his family for the last 500 years, it only had five new heads and 15 new handles.

Bryan Merckling: 28:46

Absolutely. Absolutely. That's exact the exact scenario for it.

Tom Raftery: 28:50

Okay. What about doing things like reduction of waste you know, monitoring manufacturing for quality control issues, those kind of things? Or are they being used as well?

Bryan Merckling: 29:05

They are. That's, if you're clicking down, if we're clicking down the most important use cases or the, the ones we hit first, it, it's certainly asset tracking what's become now asset and raw material tracking, digital twinning machines with new sensors like we just talked about. But the third one is quality. And quality is important for sustainability because rework is basically like remanufacturing the same thing over and over again. And the statistics, we actually have talked with a customer that has near 100% rework, so you're. For everything you are manufacturing, you're manufacturing it twice. All of the energy that's required, all of the waste that's required, you're doubling all of that through your manufacturing process and that has, obviously has a huge impact on, on yield and an impact on profitability. So they go hand in hand. And we, we have, we have a really, really intelligent, advanced customer who at, at some point. I'm gonna be a little vague here, but at some point in their manufacturing process, if the product has a sheen on it, they know there was a problem. So this might be step 10 in the manufacturing process, but if there's a sheen on it and it's not matte, once it's been painted, they know there's been a problem and they go from step 10 in the manufacturing process all the way back to step two in the manufacturing process, resand the item and start the process, including curing all over again. Well, there's a huge sustainable impact. If you can identify the fact that at step three, step four or step five, that at step 10 you're gonna, you're gonna have a quality issue and have to go all the way back to step two. So yes, quality is, is huge. And, and eliminating rework is one of the most, in our view, one of the most visible parts of creating a sustainable a sustainable manufacturing environment.

Tom Raftery: 31:14

Okay, so we've talked location, we've talked maintenance, we've talked quality. Any ones that we're missing?

Bryan Merckling: 31:24

Well, I think out of everything we do most things most things kind of funnel back into those, buckets. The last thing we're starting to see more and more of is environments. So air quality because you can start to infer some manufacturing issues, if you understand what air quality should be and what it turns into. And so we are starting to see a lot around environmentals. Companies are getting fairly sophisticated, not just air quality, but positive negative air pressure in, in certain locations. Perhaps a a paint room extra fumes and you can have an impact on air quality if you don't have the right positive, negative air pressure into a paint room or a a paint facility. We're starting to see them get much more sophisticated around temperature and humidity and starting to understand the impact of temperature, humidity. And humidity on raw materials through the manufacturing process. We have a customer that has raw materials that need to be kept at minus 30 degrees Celsius or below. And every moment that those raw materials are above minus 30 impacts shelf life. So now you're impacting waste and you're impacting quality at some point in the manufacturing process. So that's a pretty extreme view or a pretty extreme use case. But then we have customers that raw materials get too gummy or too soft, and they can, they can start to have a, an impact on machines and some of these older machines as they go through the manufacturing process.

Tom Raftery: 33:00

Fascinating. Cool, cool. Where to from here? I mean, we've seen to your point that the beacons or the sensors are now getting longer battery life, for example, in some cases with the Williot ones you mentioned zero battery. What else is happening? What else, where else do you see this space going in the next five, 10 years? I mean, sensors are becoming cheaper as well as, as well as longer battery life. So they're gonna become more ubiquitous. I've heard people talk about the internet of everything, and what kind of implications does that have when everything has a sensor on it?

Bryan Merckling: 33:42

Yeah, well, the internet of everything, I think is going to require and, and this folds into where we see it going over the next five years. Over the next five years, we see the internet of everything becoming far more ubiquitous. To do that, you're going to have to be leveraging ambient energy. So we do see the Williot Pixel as, as a a, a big player in that space. We also see things people like Atmosic who are creating ambient technologies or technologies that can power themselves by, by the sheer vibration, by movement. So you'll start to see beacons get better and better and smarter and smarter about how they acquire energy and how they harvest the energy. It's really the only way that the internet of everything's going to work for everybody. If it's going to be sustainable. We've gotta make sure we're not using coin cell batteries for everything we're doing. We're we also see a bigger push for some of the more advanced sensors that perhaps you're capturing vibration at 50,000 plus reads per second for predictive analytics. They'll be simply AC powered to make sure that you're not eating a battery if for that type of a sensor, that heavy of a load, you know, batteries are 30 days to 60 days and that's just not, that's not sustainable. And, and you can't, you can't grow your business that way.

Tom Raftery: 35:12

Interesting. Cool. We're coming towards the end of the podcast now Bryan, is there any question I did not ask that you wish I did, or any aspect of this we haven't touched on that you think it's important for people to be aware of?

Bryan Merckling: 35:26

for Well, I think you asked all the right questions. And I think the, the one thing I would, I would just wanna put a spotlight on is the data. The amount of data great. AWS commercial with Matthew McConaughey, where he is talking about AI is the future. So does that make data the new gold? You know, is there gonna be a gold rush around data? We think there should be. If you are feeding AI, if, if your AI is skewed and you're feeding AI with the wrong. data set, it's gonna be skewed, right? The, the decisions that the models are going to make are going to be skewed. You're not creating the, the impact that you need on your business. You're not getting the yield, you're not getting the profitability. And of course, if you've got a quality issue where you're reworking or re rebuilding everything twice, then you're not a sustainable company. So we think data's key. Making sure that you have no blind spots, no digital blind spots. It'll help. It'll help even the least sophisticated people make better decisions today, but it'll help AI make better decisions tomorrow as well.

Tom Raftery: 36:34

Sure. Sure. That makes sense. Bryan, if people would like to know more about yourself or any of the things we talked about in the podcast today, where would you have me direct them?

Bryan Merckling: 36:45

I would send them to our website. It and it, the spelling's a little different. We try to be a maverick in everything we do not only in how we try to push technology forward, but also in our branding. So we're Thinaer.io and it's spelled. Thin, like THIN, but a e r.io. Certainly go to our website. There's, there's some About Us pages and there's some good information on our website. We're also on LinkedIn, so just search Thinaer, T-H-I-N-A-E-R. We, we do have a, a really good CMO and PR team, so we're always putting new things out there. But website's probably the best place to find us.

Tom Raftery: 37:25

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

Bryan Merckling: 37:31

Thanks for having me. We appreciate it.

Tom Raftery: 37:34

Okay. Thank you all for tuning into 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.