Connecting People, Parts and Processes with Tego’s Tim Butler

David (00:00)
Welcome, you’re listening to the IT/OT insider podcast. I’m your host David. Subscribe to get the latest insights shaping the world of industry for the zero and smart manufacturing. Today I’m joined by Timothy Butler. Timothy is the CEO and founder at Tego. Since 20 years, they specialize in visibility, traceability and real-time analytics in the aerospace, pharmaceuticals and energy. They designed the world’s first racked high memory and passive.

UHF chip, enabled companies like Airbus and Boeing to digitize their lifecycle maintenance on their planes. And that is exactly the world we’ll be diving into today. So Tim, thank you so much for joining me.

Timothy Butler (00:43)
Thanks for having me. It’s a pleasure to meet you and have this conversation.

David (00:48)
We’re gonna kick things off. I’d love to learn to know you a bit better and also for our audience. So who’s Tim?

Timothy Butler (00:59)
Well, thanks. I’ll keep it short. But I’m a serial entrepreneur. I’ve worked in technology pretty much my whole career. regret, as we have talked about in the past, sometimes right at the beginning of this with the internet and the other activities. So I got involved working with both Boeing and Airbus and all their major suppliers 15, 20 years ago when they had this major need to be able to tag

assets on planes to be able to track the information because they needed to go with the asset because hey guess what their assets fly all over the world every day.

David (01:36)
Yeah, and so if we go back to the would say the early days, so 20 years ago, what triggered you, I would say what was the challenge, what triggered you to found Tego?

Timothy Butler (01:50)
Yeah, thanks. So as a serial entrepreneur, I had just moved on from the previous company that I had. I was looking for what’s the next opportunity. And I was looking for an opportunity where you could identify a very large market opportunity, identify a very clear competitive advantage within that opportunity, and a very clear opportunity to grow that competitive advantage rapidly in this marketplace. At the time,

No one believed that you could actually design and build a fully passive UHF RFID chip that could hold thousands times more memory. At the time it was pretty much all, you know, sort of for lack of a better term, Walmart based. It was 96-bit chip and that was about it, maybe 128. know, Tego came along and we looked at it and I’m not an electrical engineer but I was lucky enough to hire some really, really smart ones and capable ones to do this but we were able to, you know, design and build

A chip that could hold literally instead of 128 bits, it could hold 256,000 bits, which is exponentially larger. And that opened the door to a whole new world of thinking about passive UHF RFID that’s kind of expanded into who we are today. That chip and that technology led us in the aerospace industry to actually working with them very early on to actually help design and then write and support and write

the actual standards, the ATA spec 2000 chapter 9-5 and then the ongoing updates to those standards over last 20 years to meet the requirements for the industry.

David (03:26)
So that means that you started off as a hardware company? Is that correct?

Timothy Butler (03:31)
Yeah, we started off as a chip company. We designed a chip, and we still design a chip today, and we still sell that chip as part of our product solutions. But what we’ve learned along the way and for the, I’m sorry, I’m based out of the US, so I’m going to use a US reference here.

David (03:33)
as a chip company.

Timothy Butler (03:53)
a field of dreams movie from Kevin Costner said, if you build it, they will come sort of thing. Well, if you make this great chip and do it, then suddenly all the rest of the people in the industry will figure it out. Doesn’t happen that way. So what we learned along the way was, in order to use that high memory within a chip, you then had to work with the printer companies, you had to work with the reader companies, both fixed and handheld, to actually enable the software to be able to read and write to that information.

be able to work with those areas. So we got involved in actually creating the software and the other capabilities for that. So we designed and built the first software platform for aerospace industry to actually read and write these tags, both on the plane and as they get manufactured and implemented across the organization. And we then helped them to design for them the actual schema and structure for how this is done. And it’s the one that is out there today.

The critical elements there for that chip had to be, it had to hold thousands of times more information, had to exist and had to survive for very long periods of time, 30 years, had to survive under very difficult conditions, meaning extremes of temperature and extremes of radiation in this process. And it had to be able to have security because you don’t want people walking on planes and doing things where you’re suddenly clicking into tags on planes because today there’s literally thousands of them.

flying along every day as you go.

David (05:24)
Okay, you made it a bit more concrete. where can I find, well, maybe not a physical location, but where can I find that type of tags?

Timothy Butler (05:29)
No.

You can find them on our website. No, I’m just kidding. You can find them. it’s just logically what the original purpose of the airlines do this, is what they call line replaceable units, LRUs. So any product or any asset on the planes that need regular maintenance, they wanted to be able to maintain that information or connectivity that information on the plane. So you’re talking about

David (05:36)
Yeah.

Timothy Butler (05:59)
seats and oxygen generators and life vests and galley equipment and cockpit control equipment and brake equipment. Literally it’s slowly but surely expanding across all elements of the plane and it comes in various different forms. All those little screens in the back of your things, they’re all tagged. And so you don’t see them or touch them, but the suppliers of those things.

mandated to put those on there so that the airlines and other players can use them as they see fit.

David (06:33)
I see what a mandate now.

Timothy Butler (06:35)
Well, was a mandate, essentially was a mandate from the beginning because it was, you the way the airline industry works. The reason Airbus and Boeing actually worked together early on with this was because they were being asked by the airlines to provide this capability to the major components on the planes because it’s extremely difficult to be able to connect all those backend systems worldwide in a real time fashion.

to do that. The concept was, if I can have an asset with a tag on it that has enough information to give me the information I need anywhere in the world to do the work I need to do in terms of maintenance or repair, and then have connectivity to the correct information automated there in a safe, secure way, that tremendously improves their ability to do things versus

tracking these items. In addition to that, it’s for inventory control and inventory awareness. Something breaks halfway around the world. They have no idea at this point what’s going on. we hear examples of an airline. The airline door broke in Sydney for a major airline. And so what did do? They didn’t have other things in Sydney. So what they did is they borrowed an airline door from one of their other, quote, competitors who own the same plane.

And which is very gracious of them to do, but they borrowed the door and put it on the plane. then suddenly, I think it was like 18 months later, they got a bill from that competitor saying, hey, you owe me a lot of money because you took that door and you never gave it back. And you don’t even know where it is at the moment. And they’re like, what? We didn’t know that. then, it’s just, there was no, so.

The point of that story is there was no tracking of the actual asset. It could be a simple thing like a seat or an oxygen generator or a life vest, or it can be as complex as a door to a plane. And so now the ability to actually start to connect that information into your systems in a real world way is part of the standard operating procedures.

David (08:44)
So in my mind, I now see these chips being attached on all these equipments and revolve-flown planer, or least we’ve seen a plane. But who sees the data? Who works with the data? Who, I would say, the consumers on the other side?

Timothy Butler (09:00)
So great question. the data itself, that was part of the functionality of the chip. So you need to have that chip that enables encryption and authentication, because you don’t want anybody and everybody necessarily seeing it. There’s nothing magical about the data on there. You’re going to have a serial number. You’re going to have some basic information. But you don’t want people changing those things. You need to have it authenticated and verifiable.

But at the same time, you want to be able to, when that plane lands in some facility and they’re doing maintenance on it, that that maintenance person can go click with a RFID gun and look at that thing and say, here’s the information. And then they can actually reference that information associated to their back end systems to get the further information they need. If they need to be repaired or updated or configured, they can do all that information. And then,

they can go click again and now all that information is stored, the critical information is stored back on the chip on the asset as it goes in flies again and the information is also stored in their backend systems. So the first time they have a verifiable way in which to do it. Because the reality was if you just had barcodes or some standard system, you like to think that everybody is going through the process flows that they should be in a regular basis but it doesn’t really happen. And when you start to have failure rates of the process of 10, 20, 30 percent,

the whole system pretty much breaks down because you can’t trust the data and the information. This way, it enables that capability because it’s now part of the SOP of how you actually do the maintenance process.

David (10:35)
I think this is also the perfect time to start talking about IT versus OT. Obviously our main thing in this podcast. Let’s go there. how can I, say from the picture in my mind, what is IT in an aerospace company and what is OT? What is their responsibilities? Where do they sit? Are they in silos, et cetera?

Timothy Butler (10:39)
Mm-hmm.

Let’s go there.

okay, this is my outsider view of having worked with those folks, know, with those folks and enjoying it for last 20 years. I think it’s their phenomenal group of people who do their job as, you know, like anyone else, is try to do it every single day to make sure people’s lives are safe. That being said, you you still have the, you you have this idea of, I think in the past, IT is sort of supposed to be.

Information technology, meaning I’m going to build pieces of technology, silos of technology that people need. You have a problem that you need to be solved today, David. Here’s a piece of technology to solve that problem. Okay. And we take care of it. And that’s all well and good. And I think over the last 25, 30 years, I sort of refer to this as 20th century IT, it’s really from the 1980s, 1990s on.

David (11:39)
Yeah. Yeah.

Timothy Butler (11:55)
where you’re building these silos of information. And now what happened is that that capability was interesting and useful because you needed it for just one thing and there was a simple ROI for that. But what’s happened, and this is kind of gets to the IT OT convergence, is that in order to operate going forward with the speed of information and technology growing,

you need to have data across multiple IT environments at the same point in time. And that convergence of the two, of IT and OT in that space, is a big challenge. that IT is simply saying, hey, look, I’m giving you the information. You need to what you need do with it. And the OT people are going, yeah, but you’re flooding me with it. I mean, we use this expression. You’re drowning in data, but you’re starving for insight.

You know, it’s true for most of all of our customers. You know, they’re sitting there from an OT perspective. The difference is OT get person on the, whether it’s in the plant floor or it’s on the maintenance area, they’ll have six, eight, 10 screens up on their computer trying to collect and manage the information. They’re trying to, they’re pulling information down from SAP every night into spreadsheets because they have to manually do the processes. And it just creates a huge, you know,

David (12:49)
Yeah.

Timothy Butler (13:14)
they’re always being reactive to the information rather than being able to look forward to their daily business activities.

David (13:23)
This really boils down to, I would say, the need for data convergence. But then the difficult second, well, it may be easy to say, like, yeah, we need to converge our data somewhere. But then, of course, the problem we immediately faced is, but in what systems? And I would say that the difference in, I can assume that data, traceability data has its own, well,

I don’t assume I’ve seen traceability data, I’ve seen geospatial data, it has its own intricacies. Where do you start for such an exercise?

Timothy Butler (14:02)
Yeah,

yeah. Again, a great question. think one of the challenges that I continued to see in the last decade, believe me, from a company perspective, we were on the bleeding edge for a long time with a lot of this. It’s like all they wanted to do was this one thing from a data perspective, and that’s it. And so getting people to understand that this data bleeds into every aspect of every person within the organization, and then being able to

tie those pieces together on a daily basis, being able to connect this backend digital data to the movement of assets across around the organization in real time and give them the tools to be able to do that is absolutely critical going forward. And that’s in my mind when we think about this, the IT OT convergence. It’s the IT from the backend systems and the tools that are being used to the OT of the actual data that they need to have to run their business.

And now, know, the number of people who five years ago, six years ago, well, how do you solve this problem, John? You know, well, I call Tracy and I call Abigail over here and they know how to, they’ve done this before. So I just listen what they have to do. And that now we solve the problem versus, you know, I’ve got a platform and a solution that enables me to capture the knowledge and the capability of both the backend system of IT and the data requirements of OT.

I think a large part of the challenge for most organizations that you see today is, and this isn’t unique to aerospace to be honest with you, there’s a generational and a cultural challenge to how we think about things. I’m still of a generation of people still use paper and a pencil or a pen stuff versus if you’re 30 years old and you go through a warehouse and walking around, you’re wondering.

David (15:38)
Nah.

Timothy Butler (15:57)
What are these people doing? Why don’t I have a phone? Why don’t I have an iPad? Why am I not using a laptop to do these things? Why isn’t technology working on these things? And you’d be surprised how often technology is still very, very dated. I think there’s a big issue for lot of organizations is IT has been so powerful for so long and been driving a number of these things.

they’re dominating the conversation in a lot of these organizations. was just saying and pushing, I’m giving you more technology. I’m giving you more information. I’m again, flooding you with data, but you’re not flooding me with data that I can use in real time to do my job. And so how do you bring that all together?

David (16:38)
So

that also immediately goes into the direction of contextualized data. It’s having the right data for the job at hand. That’s also tightly connected to the overall data management problem or problem question, whatever. When I look at data management, when I look to IT, for example, I see that

Timothy Butler (16:46)
Mm-hmm.

David (17:07)
Well, you can’t imagine an ERP system without decent data management layer. That’s almost, I would say, impossible to imagine. If it would exist, it would also be impossible to maintain. Having said that, data management is still a difficult topic for the OT sites. For many reasons, I think it’s also to do with knowledge and the fact that…

Timothy Butler (17:19)
Yeah.

David (17:34)
engineers are mostly just working on their own problem, on their own plane. What have you seen, I would say, when it comes down to data management and how that converges?

Timothy Butler (17:44)
I think that’s, you sort of just described where we’re at today. The challenge that a lot of organizations have is exactly that issue is how do we start to address that? And I think what we’ve seen is this sort of portfolio of landscape of two standard deviations from the mean in each direction. And I think the critical factor seeing the difference between those two has to do with the

how serious the problem is that they’re having to address. What we see all the time is tens of millions of dollars of inventory, they don’t know where it is. They can’t physically track and manage it. The critical players for their organization are spending anywhere from 60 to 80 % of their time just pulling spreadsheets down from SAP to get the information that they need to do to be able to give them the information. And by the time they…

David (18:14)
Yeah, yeah, of course.

Timothy Butler (18:41)
do the put the information together and hand it to their superiors or try to make a decision that’s already out of date and now they have to do it all over again. And you know on top of that then their ability to then connect that information in any sort of process for the organization is critical. It’s not something that is allowing them to be thinking forward for their organization.

When we look at it, what we try to do is get people to focus on a unified view that’s connecting the people, the parts, and the processes to their business outcomes. If you can really think about the way we think the ITOT convergence will continue to occur is to get people to actually stop thinking so much about technology and start thinking about their business.

surprised when I talk to folks here about how much of companies that are in aerospace or in chemicals or in automotive who want to have teams of 50, 70, 80 people who write code. Why? Why are you writing code? that critical to your business operations? I think it’s critical to their IT survival to a certain extent. I mean, because they like doing those sort of things.

David (19:53)
Mm-hmm. Mm-hmm.

Timothy Butler (20:00)
But it’s not connected to your business strategy. It’s not connected to your data. And what we’ve seen along in this space, particularly in aerospace, is the critical need for data versus technology. I I think if we all had to step back eight to 10 years and pick up a laptop that we had eight to 10 years ago, could we kind of do the job that we have today with that same technology? Probably pretty close, actually. Could we?

do the data that we had 10 years ago and use that that we have today, probably not. So it’s about thinking about how is the data and the information is going to apply to what we’re doing today.

David (20:44)
Given the fact that I would say there is more data available, hopefully it’s being integrated in a more accessible way. Hopefully with a decent good use case in mind. I’m sure it has to unlock new potential. So maybe a bit of a dangerous question. This is the crystal ball.

Timothy Butler (20:49)
Mm-hmm.

David (21:13)
A crystal ball moment, I would say where we are at right now and with the technology available right now. What could happen?

Timothy Butler (21:26)
Well, I think there’s two elements here. One is, and again, I’m coming from my perspective, bias view from the RFID and technological perspective. But that being said, I think one of the critical pieces that was missing prior to this, when you think about all the ITOT aspects in the last 30 years, you’re tracking digital data, you’re tracking machine data, and you’re optimizing machine data and other activities. But what’s been missing

David (21:36)
Yeah, of course. Yeah.

Timothy Butler (21:56)
is that unified view of the people, parts, and processes, the movement of assets and going forward. So we have customers today that are tracking 2.5 million assets in a plant floor on any given day in real time in the course of the last year plus, creating well over 80 million inputs to those things. So now what you’re doing is you’re

The idea that you’re now actually capturing this movement of things and the processes and capturing that information, you’re now finally creating for the industrial sector the information you need, the vectors, the values you need to have to then begin to embed and learn these decisions and learn how to grow this, which then starts to fall into this whole issue of AI and all the other things that are starting to poke its head up in the economy today.

David (22:55)
You mentioned the buzzwords. No, no, no, no, it’s good. It’s good to. Yeah, it’s good. You know, we’re on a technology side. So we need to mention the buzzwords. But what do you see now, for example, regarding LLMs taking place? Maybe let me share my perspective first on this.

Timothy Butler (22:58)
Ha ha.

You can’t help, you can’t help.

Yeah, please. I was gonna ask you what

it was because you’re at the leading question. So you clearly have a perspective. So go for it.

David (23:21)
Yeah,

I’m also a bit, you know, when when chat GPT was was, I would say when LLM became, I would say available to the mass with chat GPT, I think first response by many people was a new era has started.

In just six months time, in a year time, you’ll see things you never imagined before. I think today we are at a point where most people understand that the thing is processing text, natural language input. And it’s very good at certain tasks. I use it, for example, on a daily basis to help me write and rewrite stuff. English is not my native language, so I can actually use…

a tool like Shatchipi2 to help me write in English. It’s also really good to help us code because that’s also in, say, it’s structured outputs. You can actually, you can kind of predict the output. And what I now see happening, especially in the industry, is these types of algorithms being used, for example, to help design SCADA screens or…

workflows or help in, for example, in data management to make data management more easy. Why? Because again, you can somehow, I would say it’s a predictable output. You can actually describe somehow the output you’re expecting, for example, some kind of adjacent forms. But what I don’t see happening too soon is using these LLMs to really, I would say, step into the actual

processes, understanding the chemicals or understanding the, yeah, in the case of a manufacturer, whatever they are producing, because from my perspective, that’s too far-fetched from what they are designed for.

Timothy Butler (25:34)
you’re already answering your own question there to a certain extent, meaning, the first part, the reason you’re able to do those, rewrite the memos and do those things is because you already have a sense of what the outcome should be or could be. And so you understand the value and what the vectors would be within that sort of structure. In terms of these processes and what’s going on, you don’t. And so the only way you’re going to get those going forward is to

pull together the data and then gather that experience to start to have some basic known understanding of what those outcomes will be. And I think you’re hitting on the challenge of the LLMs in the industrial sector. It’s not going to be enough just to pull a bunch of back-end data, ERM data or CRM data and pull it together and think that you’re going to suddenly come up with magical new ways in which to operate your company because you don’t

have those vectors and those outcomes. And from our perspective, that’s what makes what we’re doing, I think, so exciting and interesting is because we’re capturing those steps, those processes and stuff that are going to create those learning experiences. When we’re working with our customers today, we’re seeing sort of two things that create value for them right away in this. One of them is by capturing this information, we’re able to give them an ability to look into

their outcomes, not just today, but tomorrow into the significant future, which they’ve never had before, because they can now see those elements going forward. And two, they can break those elements down into what I call more levers to pull to make decisions in real time in their decisions. Because right now, when you’ve got a limited set of data and you have a limited time frame to look at it,

you only got a limited choices to make a decision. Whereas now you expand both those elements and you create those opportunities. Those opportunities then create outcomes that you can then build upon for the LLMs to go forward. mean, it’s a big statement, I don’t think the LLMs are going to have a lot of success in the industrial market until they’re able to capture those vectors and those outcomes in some fashion in their systems.

My guess is, although I have known nothing about it, but I would guess that Palantir, why they’ve been so successful in the DOD, Defense Department market of this in the US, is because they’re probably doing that for a lot of that. They’re understanding how those things are for their activities there. I don’t think they’re necessarily directly related to what we’re talking about. But they’re a form of that sort of thing. What is a company that’s kind of trying to do that?

versus like you said, I’m gonna write a better memo or a better design of something.

David (28:27)
I want to go back to the projects you’re doing. if you step into a project, a new project, what are, from your perspective, what are red flags when it comes to the project team or the company? When do you think like, this is… It’s going to be difficult.

Timothy Butler (28:56)
Great question and I, regrettably, I had that experience this morning already. no, So, and I mean this in a good way, I guess, but it’s the issue is if you’re end up.

David (29:00)
really? Telling me everything about it,

Timothy Butler (29:14)
We come in and our role where we best succeed is we act as both the architect and general contractor to solve these business problems for the organizations. If we get in there into an engagement and we can’t get them to actually explicate what their business problem is, and all they want to do is talk about technology, and or all they want to do is say, our IT team has this technology and we’re implementing it and it solves this one little problem and that’s where we’re going to go. We’re going to spend all the time there.

David (29:40)
Yeah.

Timothy Butler (29:43)
We’re not going to do anything else until we do all these other things. It’s like, you that’s a, that’s a big red flag because you know, what we’re seeing now, particularly in the airlines and the conversations we’re having is they’ve done that in the last decade with certain solutions. And now they’re finally coming back and, going back and going, you know, it’s limiting. We got some value upfront, but the value, the ability to scale and to expand across other things is really limited.

And again, it costs us too much money and time to be able to move these things forward because all these technologies have certain capabilities, but they also have certain limitations. the reality is, yeah, I implemented it. It’s great. But you know, Bobby, the guy who implemented it at the organs at the company there is left and gone to work somewhere else. And now no one knows how to do it. And now something breaks and what do we do and how does it go forward? It’s very limited. So the, ability.

to have a platform that’s a commercial off the shelf platform that takes those technological elements out of your worry and just focus on the data is where we come in. So the organizations that say, we’ve got these business problems. We’re spending way too much time gathering data instead of analyzing it. We’re spending way too much time looking for things rather than finding them and doing something about it. We’re spending way too much time just talking to people about

what’s happening in terms of our schedules versus having the schedule right in front of us and being able to make changes quickly and easily. Those are three simple things, but those three simple things have huge multiplier effects within the organization. And the second part of this whole thing is this is not an IT problem. It’s not even an OT problem. It’s an organizational problem because where we’ve been most successful

The users are on the plant floor. The users are in HR. The users are in finance. The users are in the buyers. They’re in the executive management. They’re all using the same data. But they’re using it in their own unique ways to do what they need to do. And so you need to have that single source of truth of information that gives you the… But it has to be consistent across all that because you don’t want different players using it at different levels.

And too often, people think that just because you pull information from SAP or any other things that it’s the same for everybody. Well, in my data lake or my system, am I pulling it every two hours? Am I pulling it every four hours, every eight hours? Because depending on when you’re pulling it and who’s pulling it when, you can have very different pieces of information, which causes confusion across the organization in whatever processes, whether it’s in manufacturing or maintenance and repair.

we see from a sales or user perspective, if the focus is on incremental specific technology, it’s a big red flag for organizations. You really need to be thinking about this from an organizational perspective. I use this metaphor not always very well, but it’s the notion of the elephant and the blind men sort of thing. If you don’t understand that you’re looking at an elephant and you think you’re just touching the tusk,

David (32:50)
Yeah.

Timothy Butler (32:57)
you know, you got a problem and you know, you’re not going to realize it till much further down the road and you can spend a lot of money, you know, fixing one thing and you’re going to find out that you’re going to, it’s going to be very difficult to move forward.

David (33:11)
everybody is looking at their piece of the elephant. And I think that’s a great way to wrap up this episode. Because I think what I like here, if I can summarize this, is the fact that obviously we talked about hardware and software and thus technology. But it’s the way technology is getting put to use.

Timothy Butler (33:16)
Mm-hmm.

Mm-hmm.

David (33:40)
across the entire organization. And that’s also, I would say, I believe that ITOT convergence should never be a decision to just collaborate, or it should never be a decision indeed on technology, but it should actually be use case driven, actually be value driven. This may be even a better way to put it.

Timothy Butler (34:07)
Yeah,

yeah, no, I totally agree. mean, you know, we kind of work by this motto of, we know we don’t know. I mean, we know that technology is going to continue to rapidly change. We know that, you know, that, you know, needs are going to continuously change. We know that technology is going to move faster and faster. So if that is the case, then, you know, trying to spend a long time with a specific technology to do something is by very definition going to fail over time.

And so you have to have something that’s flexible, it’s mobile, and is connected to these other elements, can converge as needed across the organization and across organizations. There’s nothing that limits any of this going forward. I’ll finish with, from a data perspective, the upside of all this is that, and the dirty little secret that we see all the time is,

David (34:37)
Yeah.

Timothy Butler (35:04)
The data is pretty straightforward. It’s not a question of you have to get 20 million data pieces from all over the place. There’s a set of data you need to do, but you need to understand what that is and then allow it to be applied across the organization wherever, whenever it’s needed in a functional way. And too often, that’s lost. Everyone’s focusing on all these other data elements or infrastructure technology elements versus saying, here’s our data.

David (35:07)
Yeah.

Timothy Butler (35:31)
Here’s how we want to present it to everyone and let’s go from there.

David (35:35)
So yeah, that’s a perfect way to end this episode of the IT/OT insider podcast. Tim, thank you so much for sharing your insights and to our listeners for tuning in.

If you enjoyed the conversation, don’t forget to subscribe at itotinsider.com and leave us a rating. And we see you next time for more insights on bridging IT and OT. Until then, take care. Bye bye.