The Manufacturing Executive
The Manufacturing Executive

Episode · 11 months ago

IoT Data: An Untapped Goldmine of Marketing Insights w/ Jan Pingel

ABOUT THIS EPISODE

Traditionally, marketing teams in manufacturing have depended on CRM supply data to get a picture of their customers.

However, there’s an untapped well of customer insights that many overlook: IoT data from manufacturing facilities. 

Using this data, marketers can target specific campaigns to specific customer bases and create messaging that truly resonates with customers.

In this episode, I sit down with Jan Pingel , Product Leader of Digital Solutions at Ingersoll Rand , to talk about how IoT is opening up new opportunities to get a deeper view of customers and learn valuable things you don’t normally learn about them.

Join us as we discuss:

-Industry 4.0 Club 

-Using IoT data in marketing

-Data privacy and data security concerns

Resources we mentioned during the podcast:

- Industry 4.0 Club 

- SME.org 

- Arcweb.com 

- Isa.org 

- IEEE.org 

Subscribe to The Manufacturing Executive on Apple Podcasts, Spotify, or our website.

Listening on a desktop & can’t see the links? Just search for The Manufacturing Executive in your favorite podcast player.

But once you are connected, you are continuously getting information that I can tell you something about how this industry is performing, how these customers are using the equipment to see one of the new things they've seen out of the data that could be used in a marketing perspective. Right. Welcome to the manufacturing executive podcast, where we explore the strategies and experiences that are driving midsize manufacturers forward. Here you'll discover new insights from passionate manufacturing leaders who have compelling stories to share about their successes and struggles, and you'll learn from B tob sales and marketing experts about how to apply actionable business development strategies inside your business. Let's get into the show. Welcome to another episode of the Manufacturing Executive podcast. I'm Joe Sullivan, your host and a CO founder of the Industrial Marketing Agency guerrilla seventy six. When I think about market research, some things that come to mind are collecting and sorting through demographic data, studying industry reports, conducting customer interviews and Voice of Customer Work, evaluating potential audiences for viability and paid media platforms, and maybe looking at what search engine data can tell us. Here's one more for Om's and particularly for machine builders, and that's machine data. What can you gather from an Iot enabled machine that can help you not only service your customers better, but also identified trends in the market and buying behaviors among subsets of your audience? Most of the manufacturing sectors just starting to skim the surface in this area, but my guest today will dive into the topic and hopefully spur some ideas that might help you get out ahead of the pack. So, on that note, let me introduce him. yenpingle joined Ingersol rand in late two thousand and eighteen as the digital solutions leader focused on its industrial and compressed air systems business. He's responsible for connectivity to the entire portfolio by delivering digitally enabled solutions and services for customers. Product Strategy, innovation, design, IOT edge to cloud and machine learning, digital twin or a combination of passion and job responsibility. For more than fifteen years, Mr Pingle has been in the technology segment of the industrial manufacturing space, working on making manufacturing software enable optimization, integration and insights into the production and manufacturing supply chain. He has specifically been involved in product design for data collection, data visualization, data analysis for process and event data for manufacturing and, over the last couple of years, specialized in new technologies for data management, such as Iot, Iot, cloud, machine learning, digital twin and big data, and has held positions in global portfolio leadership, Global Products Strategy and global business management...

...over different periods of his career. He graduated from the Technical University of Denmark with a master's degree and electrical engineering and computer science and is a certified scrum product owner CESPO, as well as scaled agile framework, certified agilest and product manager, product owner and experienced in the user experience, in user experience, design, design, thinking and innovation. He is and has been an active member of I triple e Issa, is spe Aee, ashrae m yes a and other industrial organizations. Yeah, and welcome to the show. Thank you very much. I'm glad to be here. I'm thinking it's going to be a fun little session on some new topics. I'm hoping. Yeah, I think I think it will be, for sure. And and you know, I pulled you in here because you and I first met when I came on as a guest in Industry for point no club, which is something you and a few other really smart folks are doing and have been doing for a while now on clubhouse. So the few months back and somebody on from your crew asked me to come in and talk kind of about, you know, transforming the marketing mindset in this digital era. But I would I'd love for you to start up by doing here, is is talk a little bit about what you and some of the other co founders are doing with Industry for point no club especially knowing a probably a lot of our audiences not super familiar with clubhouse, and really it's less probably about clubhouse than just what you're doing in the industry for Point No. Worlds all in the over you. Oh, thanks for so. It is the form of Club I've been part of it for probably for months now. It's started, I think eight months ago. It's really google professionals in the manufacturing industry with the goal of engaging diverse worldwide talent to accelerate what we call the global of loostion industry. For I know, and so with any group, we have professionals in this area and then to clubhouse. We host rooms every week where we have very different topics and segments within industry for point o realm, so speak, if we invite key guests that can contribute with a specific aspect of something, and you are part of that. When we had, I believe, the what we call the collaborative for God what, this is about people and process etc. Right. But we have technical rooms. We're talking about the future of industry, folk know. We have a fireside chat we took, we take an executive or or key influencer and and have a have a good talk there about what they're doing when we every things going. And then we also have a Monday room which is women in manufacturing, which is a very well visited group for a lot of discussions around women's oppositions and and their way to kind of get forward and in the manufacturing industry. So right now is everything we do is on Clubhouse, which is the audio only avenue for having conversations around different topics and ours...

...club there is industry, for we know, and probably one of the ten most visited clubs on a manufacturing industry on club house right now. Yeah, I think it's really fantastic what you guys have been able to do on that platform. You know, I kind of dabbled or in clubhouse a little bit when it was getting really hot, you know, back around the beginning of this year, late last year, and I remember exactly when I was popping in there, but it seems like there was this influx of people into clubhouse and everybody's starting clubs and a lot of them just kind of dissipated or died out. But you guys have kept a really great community alive and I think it's sort of demonstrates the passion around these topics that you're talking about and the, frankly, the brains behind the people that are joining these conversations. So at highly recommend anybody who's listening here, if you haven't, go see what these guys are doing on industry, for point, on Club House. It just really great, smart conversations on the topic with a lot of thought leaders in the space and you can join right in and be a part of these conversations. So pretty cool stuff. Yeah, you can find us, of course, on club house when you can find something in you can find us on twitter and we also have a web page industry for CIAL Clubcom when you can see our schedule and not be able to join any room that you wouldn't be interested in. So yeah, a lot of ways to contact us. I brought this up at the beginning rather than the end, when I usually kind of give people a next place to go, because I think it's a good lead into with the discussion I wanted to have with you today. When I came on to your clubhouse session and talk to kind of about shifting the marketing mindset in the manufacturing sector. You said something that really caught my attention and you kind of talked a little bit about this idea of we have so much data that we can gather from machines at this point in time in manufacturing facilities, and you know, probably a lot of those things started just for operational purposes and to understand how the machines working. But as all this data builds, this also creates a marketing opportunity to you know, how do we harness all this data and then start using it to target audiences accordingly based on what the data is telling us and influence other parts of your marketing strategy? So you're going to do a way better job explaining this than I can, but it was something that I hadn't really thought about, even as a marketing guy in this era and in the manufacturing sector, and that's why I really wanted to have you come in and break some of that down. Tell us, tell us what you're talking about when when you mentioned that? It's also been interesting to me when we started. So my background is and data process data, manufacturing data. Their normal place for that is called a historian, which is a engine that collects data from all manufacturing equipment and it's very different than the typical database because it's really like a lot of scenariss like every minute I'll get a new temperature, for example, or I get a new pressure or get a new flow or something like that, and then then you look at trends and see what's going on with that. So in Asian when this whole big data discussing him up, which just like started probably five ten years ago in the General Manu fact general industry around marketing, consumer analytics, etc.

Toll a big data. Now there's so much data now. Well, my asthlee is that a lot of data is not necessarily big data because if you look at manufacturing, there is probably not an area where it's more data than in manufacturing. You look at history, is that collecting data from all these different machines on an entire plant. There is tons and tons and times of data, but you can't just apply the traditional big data analytics to it the way you've been used to in like a consumer marketing aspect. So there I see. Typically you have demographics and then you look at behaviors and so so you can, for Exama, go on Google and say I want to get a subset of your users that are twenty five, two, fifty years old, male and they buy reusable shaving blades some like that. So that's behavior demographics and now you can look at that data set and see what you can you can do with that and then you can start on marketing campaign. And so when you initially had these data scientists comming into the manufacturing industry said, well, gave us all your data and we'll go find some correlations in that information and be able to tell you something that you probably didn't know. Now. The problem here is that understanding traditional buying behavior us, the demographics, is fairly easy. I mean more or less everybody can understand that concept around that, when you go into a machine and you got to understand behaviors around the machine. When? When does it have a temperature spike or a pressure drop or whatever. Right it's very different. And so what I've seen a lot with these big and a LID, these companies coming into a manufacturing and come and trying to come up with something that that can help them understand something with new insights. What they really come out with this insights that is already known on the plant floor. It's collations that already in your data. It could be thermodynamics, it can be physics and be all kinds of stuff that everybody really knows that works on the machine. They know these kind of things because they worked on the machine so many years. And so what you're trying to do is to kind of look at it in a different respective of trying to go back to this idea demographics and behavior and instead of just looking at the data, you're looking at trying to identify some behaviors that you might be able to see him the data. So so, for kind of temperature spike is a behavior or pleasure drop is a behavior, and so so you might then be able to identify some collations between things you didn't know. And there are tools today in the industry that are looking at still kind of visual inspection. You have an an event, something happened and then you look at that information to see, Wall what can I see from the data kind of a minute before or five minutes before, having before the something there, and then you can say that that's kind of like a pattern and can then that the system that can that can go through the data and find similar patterns that...

...are like ninety percent close to what this is and then you can try to use that. But from when you talk about really non visual inspect and type of analytics, you got to think a little bit differently, and so data scientists today kind of have to involve those Assamese to make sure that they take away that already known correlations and then get into kind of valuating what's in the data. And so what really is interesting for a marketing perspective is that a traditional marketing process and campaign is you would look at the traditional data that you would have, which would be supply chain crm data. So you can look at, I know customers, they bought this machine is now ten years old. Based on our assessment, we should probably offer them x y and see right, but you only know when you sold them. You don't you might not know what it's when using that machine for has been standing still for ten years. So, but if you start looking at Iot and start pointing data in our regular basis, you can now know much more about what that company or what that customer has done with that machine. And so you haven't both the way to say, well, there's no reason to send out a campaigns will place a motor on a machine if it's only been half utilize compared to other machines. Right. So that's one thing you could look at. But even going a little bit deeper, maybe when I was working at rock wall we had the downturn and the two thousand sixtyzero seven, tyzero eight. It was very interesting because, like just looking at the at the sales numbers, we could see when customers started shutting off service, when they started not buying parts for stock and then when they didn't buy equipment anymore. Right, we could kind of see that. But when not thinking about Iot, there's much, much more you can see. You might be able to see that as in our entire industry is slowing down, for example, or they're picking up, or you can now start if you if you still use the demographics around the customer base and you have as much information and possible. You know size, demographics, location, what's the weather pattern? You know it's other summer or winter there, whatever, all that kind of stuff, and they started looking at their behaviors, and that could be machine behaviors. Paper also be like this, looking at at like how loaded all the machines. You can now really target some more specific campaigns around that information to the right customer base and you can refer to something that they actually can resonate with. They can resonate with hey, we know that because of the the forest fires, for example, you're slowing down something and we can help you with X, Y and Z, you can. We can help you with like rental equipment or something like that, and we can validate that that's actually happening based on the data we can see. It's right now, in the OM space and manufacturing of an untapped area of information, because what we use the out days today is to better service our customers and we also use it for onded to understand...

...if if there's some patterns around the motor that we started using three years ago is failing a lot more than the one we use before, etcetera. So we use that for sure right away. That's the obvious thing to use the data force. But from a marketing perspective, there there're weally some opportunities there that could be very, very different, where in the past we have mainly used like crn supply data to understand, you know, how old is the equipment that the customer has, what is the next time they should maybe upgrade or whatever, but now we can actually see some of the usage data that we can use and then target some more specific campaigns and especially also maybe a void campaign. So I there's no reason to have a campaign into an industry that's currently slowing down, where you would want to target the industry that's actually picking up. Right. Yeah, I think it's really smart. I'm curious, do you think this is this is a trend that's we're kind of at the very beginning of this or you know, I'm not up a manufacturing operations guy, right, I'm a marketing guy who works with manufacturers, and I'm curious to think that, like, are we just starting to skim the surface of this, or are the bigger companies like an ingersoll round where you are like for further down that path already? I think everybody is at this skimming the surface right now. It's not a traditional marketing area. I think even most Commons of work for what marketing is still looking at. You know, yes, you you have leads that will drive some campaigns and we have traditionally, like most others, use the supply team data and use the age of the equipment. We might have some usage data either by when we go out and serviously equipment, we can record that the run hours, for example, and they we can have a good idea of how well that machine has been been going and use. But really looking at the Iot data from marketing is something new. We are looking at it from what we call inside perspective, but it's still very, very customer focused. So, for example, like if we can tell a customer of that their behavior right now is not good, they might be like short cycling machine or something like that and we would want to tell them that. You they would want to either change some settings or behaviors or something like that. That's something we are doing right now, but those same types of insights could be used from marketing perspective as one. That's where I think some people are starting to think about. You kind of mentioned this a few minutes ago. You said something along the lines of you. We're interested in the things that you wouldn't necessarily know, like what are the other examples you can give of that, like what things may you not realize are going on that you could gather through some of this data? If it's still a traditional big data analytics, it's about finding things you don't know. It is taking a big data set, understanding all the demographics and look at the behaviors and then you can see some correlations around. Like we're thinking it's thirty five from fifty year old that are buying these specific five blade razors.

Right, but we're finding out that is really the thirty five to forty five that's doing or whatever. Right. So seems like that they're looking college or they're finding something could be different, that they're starting to buy less of them or something like that. Right. That that's just the two traditional thinking. They're right. When you go into the manufacturing and process data, you got to be careful not having that same concert because again, as I said before, there are a lot of correlations that are very well known that you don't want to have. Example that from one of my previous jobs, I have or we were working with one of gaps which was the liqual financial gas producer in Australia, and they have one of those big companies come in the said Hey, give us all your data and we will just go through it and look at it and then come back with some really great insights and you can use for your business, right. And then they got the data for like three months and then a game banks this. We found some very interesting collations in the data. If you increase x, you will get more. Why? And so they look, but what is x and what's why? And they said, well, X is what they didn't really know because they didn't have really the men data. They just looked at the data data, and so when they, when the content customer look looked at the data, they can see that that what they found out is that if you put more natural gas and you can get more liquefied natural gas out, which, of course, like you know, Duh, right. And so you really need to involve your sames to emanate all those known collations first, because then everybody will be interested in the things that they don't know. Right. So if you can have enough demographics and behavior data on your machines and on your customer behaviors around how they used machines. You might find some interesting aspects that you didn't know, and I'm not just talking now about the Simbold Anamy, that the physics about it, but really about the behavior side, that there might be something that customers are doing that you didn't understand or really knew that they were doing in that particular way. And so that's what you would will want to find out, is those things that you can either use to improve your equipment or maybe you have the equipment change or better operates under those behaviors that you see the customers doing. Or those behaviors are this bad. I mean it could also be that right. So if you can kind of ties behaviors to a lot of trips and warnings on the machine, you can tie those things together. That's that's a that's a bad thing the customers doing. So what we will be looking it is, you know, set point changes, when as the customers actually making set point changes and what is the effect of those? But that's just for many process in your perspective, looking at the actual data. But if you put in big data analytics, the idea would be that anomalies can be found more automatically as opposed to somebody have to say. I'm thinking this is the case, let me do with the data. But instead now you can see, hey, here's some new anomalies that we didn't know about before, and now it's true reverse. I found a problem them and now I can look at the solution, where as the normally is, I think I have a solution that we see if the problem is there. So yeah, and I'm going to go home tonight and most likely my Amazon Echo is going to be flashing yellow at me and my kitchen and Alexa's...

...going to tell me that I need to order more diapers for my newborn or more cakeups for my cur Eg and it's pretty convenient. But I also, you know, I can't help at the cringe a little bit when I think about how much Amazon and Google another big, you know, tech companies know about me. And I'm just wondering, do you see hesitancy from customers about giving back, you know, data to the OEM, or do you think that'll be an increasing sort of worry for one reason or another? Do you think that the positives that come from that outweigh any sort of privacy concerns? Well, data privacy and day securities definitely very important topics always consider both as a customer and as a vendor. I won't say that customers are necessarily hesitant, except for some specific sectors like the power industry life science. But custom every they're keenly interested in how Om's and vendors can help them at the same time protect the data and their privacy. It's interesting, though, that customers state generally would rather not share any date at all, and I see the problems. Has To say the less you share the better, right, but the same time they would still want to know one of the best practice in the industry and what can we see out of our data? So it's kind of like a two phase scenario. What they want to share for the part able, but want to get as much out of it as possible as well. And then, of course it's important that they also want the customers or the Olym to be able to help them as best as possible. And again, the more data they share there the more you get out of it. But it is definitely a specific area of concern. We've had various discussions with customers around specifically data privacy and security. So it's about for anybody that has solutions in the Iot and I would space. They got a document their whole security paradigm. What are they doing? And it also even document, you know, have your environment being tested by any official test companies, etcetera like that. There's tone of the mode that that will test your system make sure it's secure. You got to make sure that day data is encrypted all the way through from when it was generated at the edge all the way into the cloud, and then I need a privacy side. They're definitely differences. So from our perspective, our equipment is typically consider as a as a resource like electricity. It's compressed air or is electricity. So that data that we would collect can tell very little about what the customers actually doing with the equipment or with it, what they're doing in their plant. Right, we can get an idea of if they might be doing more or less. We could get an idea what their shifts would be, but then you could just park outside the factor and see when the lights on and you would know the same thing. Right. So there's not a lot of concern for our customers of what data we are collecting, whereas if you are an om for like manufacturing equipment like bottle lines, mixing tanks. Then it becomes a bit different because now you will potentially also have you you have the recipe of what's going on in...

...the manufacturing within your system, and so you got to be very careful about the data you're collecting or what you're collecting it for. So when I was working at some of the like rock woman, honeywall, and we were talking about historians, right, the historian would typically be collecting the process data, but not necessarily like the recipe data or the production formation or the product information. That would typically be in an EMA system or something like that. Right. So you could take the process data from my story and and say you can kind of if you're really smart, you can probably identify a little bit of what's going on, but it's still going to be hard. And so that's the same level, I think in o m that are pulling data off the machines. They're not so much interested in like the recipe and the actual products that are being made and how many and all that kind of stuff. They're interested in the machine, interested in whendom machine breaks or when it has a warning and what was the temperature, the time was the pressure flows and what was it right before. And so I know we have has to be a little bit concerned about at least communicating what they're collecting, what data they're collecting and, of course, what they're collecting the data for. And it's typically very obvious when you start having a connection with the customer, because you are you're going to be calling up the customer say hey, I can see your machine is down and I can see it has this particular air code and I will be coming out, you know, later today with this part and installed and get the machine back up and running again. And customers are very happy about that, right, whereas if you went ahead and says well, you are producing this particular product, then if you choose to do this other product, you would probably make more money. That's a different discussions and that's typically something that's could be left within the company, right within in the customers, and that's what they're hiring these big firms to do for them instead, right, and the data stage within the company. So you got to be very careful about what data collecting and what you're collecting it for. And then when you start thinking about because some customers would want to say, well, what can you tell us? What's Best Practices of this equipment? How do you best utilize that? How your best structure the set points or the control of this equipment? In my industry, for example, there you got to really be careful about what, if I'm going to be doing someone that you have to anomalize, anonymize the data. You got to look at. Here is five hundred pieces of equipment in this particular industry and here is some general trends of how these five hundred piece of equipment is being controlled or being one or being utilized right. So you have to look at that perspective as well and be very, very certain Bart because, for example, like we have customers connecting into our system to see the data, but they can only see their own. They cannot see anybody else this information. That can go into a photo. They can see exactly what they're machines are doing right now, but they cannot see anybody else's. And so if you're going to start looking at that level, you got to be very careful about how you process that data and you got to think about anonymizing that information to start seeing what you what you can see, because you can see a lot and you got to...

...be very conscious about what you use using that data for that you can see and, for example, like when you looking at the specific customer data, it's really about you servicing that equipment for that customer, and so you're looking at them, at the actual machine performance data and alarms and trips and trying to figure out how what's going on in the machine and how can you you know, what are you going to do to fix it? Think did and it's broken up, of course, like or it's meant before, like short sighting. If you can see some behaviors that it's not necessarily about what products being manufactured on the machine, but really how they use using it. If there's something that you can see that's not right, then you can use that like but you got to be very, very careful about the data you're pulling in and typically, I would say most, most are just pulling in that machine performance data the end. Is there anything I did not ask you about that you'd like to touch on today, or is there any you know, anything you'd like to say the manufacturing leaders out there who are, you know, it's kind of intimidated by all the new technology that's emerging and the data that's available to them and aren't sure where to get started. Yeah, I would would definitely say start engaging their marketing side of your company with the digital side. Those weak so there's there's digital transformation and did it all whatever, right, and it's from marketing perspective it's really about the website and getting leads and unstaying something from these. But it's on a dish side. There's another digital side. They're right. If you're starting to get data from your equipment on a real time basis, the initial project will typically start from a service respective like being able to better service your customer, be able to be put the more proactive and also be able to like in our case, for example, it's a lot of it is about reducing troubles. So if I can know as much about the machine before I come on site, I might have a very good chance of bringing the right part and fix it right away. Right. So that's the first initiative around that whole thing. But if you start engaging marketing and say okay, now we've gotten this data for let's say a couple years, right, well, what can it's tell us? If I have a marketing campaign around the age of equipment, it's really about how much has it been used? Can the Iot Day to help me give some more aspects or information around that that I can better utilize for a marketing campaign? And then once you get marketing who been involved there, they're going to have their own kind of aspects of looking at that information. I said, well, I can see this or this it is and I never even knew that we had that data because it's I mean, that's it's a it can be a gold mine of information for marketing, as long as you make sure you keep it anonymized as much as possible from a marketing campaign perspective least, so it's in a specific tick behavior type specific. But it's not. You're not going to go to a marketing campaign specific to one customer because you can see something. You got to be very careful about that, of course. Right, it has to be trend type data that you're looking at. But there is, I mean a lot of cases if you are connected to your...

...customer with Diote, it is very, very different than the only day that you had was when you sold the equipalment to them, to the customer, right, that's your endpoint of a lot of your customer connections without Iot and did your connection to it. But once you are connected, you are continuously getting information that I can tell you something about how this industry is performing, how these customers are using the equipment, to see what of the new things they've seen out of the data that could be used in a marketing perspective. Right, we're seeing customers with this particular component doing something much better than once without the component, and I can go to market compaign, to the customer spot, to component and say here's a case story. If you add a DIS component to your machine, we can show you a ten percent increase of whatever, right, or decrease of energy utilizations. So something that data can tell you much more now than before. I love that. It's a commit's like a completely new will, not new, but just additional way to do market research within a very, you know, specific subset. Yeah, absolutely, really smart. We end. This is really good conversation today. Appreciate you coming on and doing this and wondering if you can tell our audience how they can get in touch with you and how they can learn more about both industry for point o club and also what you doing it. And you're Sol rant. Sure, so I'm a Linkin. I'm on twitter link receieve my last name, pingle, so you can find me easily there and from there is reference to every where else. INDUSTRY FOR MY NO CLUB IS INDUSTRY FOR CEA clubcom. And then the other thing that I think would be interesting too is that this is new right and not just a marketing aspect but the whole industry for my no. So I definitely encourage everybody to not just go on Industry Fau no club, but other resources there are. You can search industry for when no technologies IOT smart manufacturing. You can check out the standards and and Industry Ong stations like EU. On Industry for my no SIME DOT Org says me, a arc is say I tripli and I wot wild. They have tons of resources. Well, that could get you into this space here. And then the next step, of course, system start using it for my for marketing perspective. But that's this temically not the first level there, but that would be and again also definitely encourage anybody to go on industry for my no club and join some of our rooms and clubhouse. I learned something. Every single day I'm in a room, even if I'm on a host I there's guest speakers with new aspects, new ideas and new information that I didn't didn't know about. I learned something every day I'm on these room. So definitely encourage everybody to go on. It's a great way to get an hour, two hours every week and get some more information about industry for my no, and different aspects sort it. And it's not just the technology is is also the people, of the culture, etc. said a lot of a lot of different topics that could be very interesting to learn about. Beautiful. I can speak from my own experience about the quality of some of those conversations, so I would second everything you said there. Welly and once again,...

...thank you. Really appreciate you taking some time out of your day to do this. I think this is going to be really interesting, you know, the episode for some people to listen back to and open their eyes to some things I haven't been thinking about. Glad to be here. Thanks to you. Thanks for invitation. It's been it's been great time. It's been fun. You know, everything you do here is all good information to to the to more ass that the same audience. Why it as a screen industry for Pint O club is trying to reach and trying to evangelize on this Cup of information, so great time join it. Likewise, thanks you on and that's for the rest of you. I hope to catch you on the next episode of the Manufacturing Executive. You've been listening to the manufacturing executive podcast. To ensure that you never miss an episode, subscribe to the show in your favorite podcast player. If you'd like to learn more about industrial marketing and sales strategy, you'll find an ever expanding collection of articles, videos, guides and tools specifically for bedb manufacturers at Gorilla Seventy sixcom learn thank you so much for listening. Until next time.

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