Connecting Everything, Inside Volvo’s Massive IoT Strategy

Most industrial IoT deployments struggle with the same challenge: dozens of pilot projects that never reach production scale.

Volvo Group broke this cycle with a strategic transformation connecting hundreds of millions of assets across 140 factories worldwide. The secret wasn’t just technology — it was organizational change that unified scattered efforts into production-scale success.

Volvo Group IoT Expert Julien Bertolini joins the podcast to share insights from one of the world’s largest industrial IoT deployments, including:

1. How to escape pilot purgatory and scale from prototypes to production
2. Why multi-technology approaches are essential for industrial environments
3. Building IoT communities and citizen development strategies
4. Real-world use cases from AGV predictive maintenance to massive asset tracking
5. Why quality IoT data is the foundation that makes AI actually work
6. The convergence of IoT and AI for competitive advantage

Tune in to hear how strategic IoT transformation unlocks AI potential at an industrial scale.

Subscribe to IoT Leaders

Subscribe now

Subscribe now

Subscribe now

IoT Explained

The Rise of IoRT: How AGVs and AMRs Are Transforming Smart Logistics

Learn more

Whitepapers

How Can AI Improve My IoT Deployment?

Learn more

Blogs

10 Benefits of Using a Private 5G/LTE Network

Learn more

Case Studies

Fassi Gru

Learn more

Transcript

Intro:
You are listening to IoT Leaders, a podcast from Eseye that shares real IoT stories from the field about digital transformation, lessons learned, success stories, and innovation strategies that work.

Nick Earle: Hello. In this episode of the IoT Leaders podcast we have a very large industrial IoT use case. It’s Volvo and as you’ll hear at the beginning of the podcast most times we see Volvo, we think of their cars. That’s actually not part of the Volvo Group anymore. But it’s the trucks. It’s the construction equipment and it’s the marine engines.

It’s a very large company and they are embarking upon and have well down the road on one of the most ambitious IoT projects that we’ve ever had on IoT Leaders. Because essentially they’re not trying to connect thousands of things or even millions of things. They actually have a very clear vision, which you’ll hear to connect hundreds of millions of things. And one of the reasons they want to do that is they absolutely believe in the opportunity and the power and the potential of artificial intelligence. But my guest this week is Julien Bertolini, whose job title is IoT expert at Volvo.

My guest talks and this is at the end of the recording, talks very clearly about the fact that AI does have huge potential, but it is all about how much data you can feed it. And in the IoT space, that means you have to connect everything. You literally have to connect everything using a multi-RAT approach and get as much data as possible.

And those companies that do that will get the biggest return from AI going forward. So it’s a very interesting case study from a huge multi-billion-dollar company. And it starts off with his journey of what happened seven years ago when he joined the company and what they weren’t doing to where they are now and where they’re trying to get to over the next few years. Very interesting story and a very good industrial use case of IoT on a global scale across 140 factories in a company with a hundred thousand employees. So, without further explanation, I’ll hand you over now to my podcast with Julien the IoT expert at Volvo.

Enjoy.

Nick Earle: Julien, welcome to the IoT Leaders Podcast. It’s good to have you here. Could we start off? I know you’ve got a very good story to tell about your journey of IoT in Volvo, which is a very large company, and you just work for part of the company.

But before we get into all of that, maybe a little bit of background on yourself and your background and what your role is at Volvo.

Julien Bertolini: Hello, Nick. Sure. I’ve been involved in the IoT market since its early days. So starting in 2008 when I began working with machine-to-machine technology. And I have a strong technical background as a solution architect and a full stack developer. So, I’ve developed for mobile, for embedded devices and also on the cloud.

And I’ve collaborated with various industries to develop customized IoT solutions. And currently I lead the Volvo Group strategy for deploying technologies in IoT and edge computing.

Nick Earle: And remind me, how long have you been at Volvo? ’cause I think you were in the telecom business before then,

Julien Bertolini: Yeah, seven years ago I joined.

Nick Earle: Okay. And working for Orange before that? Yeah. Yeah, absolutely. Okay. And you’re in the south of France as we speak. Great. Now Volvo’s a big company and most people when you think of Volvo, I guess they think initially of cars, but they also think of trucks. But Volvo does an awful lot of things as well, Marine and whatever.

So which bit are you in?

Julien Bertolini: Yeah, the Volvo Group is not a car manufacturer anymore. So, our main business is around trucks. We’re one of the leaders in truck manufacturing but we’re also doing construction equipment. We’re doing buses and some engines for boats. So that’s our main activity.

[00:05:54] Nick Earle: Yeah. So that puts a box around what we’re talking about because this is a very large industrial IoT story. 140 factories and millions of pieces of equipment. We’ll get into all of that. So, you said he joined seven years ago. Here we are in 2025, so that makes it 2018.

What was Volvo like when you joined in from an IoT perspective, at least in 2018?

Julien Bertolini: Yeah, so the first thing that I’ve seen is one team was working on what we call a connected product. So basically, the trucks that are on the road. And it was a really strong and major team because we have more than 1 million trucks connected on the road.

So we’re gathering data from our product and it’s working really well. On the other hand, I’ve seen for the IoT, so for the industrial IoT that there was no formal organization in place. So like in areas like smart factories, smart logistics there was no, no formal organization. And I went all over the factories all over the world, and I’ve seen more than 60 IoT initiatives at this time. Six zero.

Yeah, it was quite big. We’ve seen really a big interest around IoT. However, there was no solution in production. So zero solution in production. So the challenge from the top management was to say, how do we escape POC hell. Everything was tech in this POC stage.

And so they asked me to investigate on the block hills. And I was able to see three blockers. So, the first one was technical. So multiple platforms were in use and when you want to put a new platform in production you have to go through a big process with security review and all that stuff.

Fully complete this process. The second challenge was around the organization OT people, so people from the shop floor and IT people were working in silos. And for OT solutions, you need a strong collaboration to build something that is really strong. We need cross functional collaboration. And the third blocker was their competence development. So, there was no dedicated team for the IoT to support the initiative. And so, it was super complicated for people to go from the POC to an industrial grade solution.

Nick Earle: And I was thinking certainly as somebody who okay, I’m running an IoT company now, clearly aside but in my career, I twice worked for PE companies, not with a hundred thousand employees, but two companies that both had 70,000 employees. In my case it was HP and Cisco.

But the three things that you described were exactly the same. Yeah, it’s pretty common.

Julien Bertolini: Exactly.

Nick Earle: Which is lots of people doing a lot of doing a lot of things apparently. But nobody had really implemented secondly, they were all working in silos. In fact, most of them were not even aware of the other people trying to work on one thing.

And then thirdly, they’re working independently. And they weren’t sharing any skills. They were learning the same lessons over and over again. I think what you’re describing is a very common characteristic. Frankly, it happens in small companies, but when it happens in big companies it’s everyone’s trying to do their best but therefore each other are not achieving anything.

So that was your starter, and you did your tour and yeah.

Julien Bertolini: So that was the three main blockers. And so, what did I do in 2019? First thing I have appointed an IoT platform for the group. So, it was at that time ThingWorx from PTC. So, it meant that everybody was working off the same IoT platform.

The second thing around the organization was to build the Volvo IoT community.

Anyone can be a member. There’s no structure or hierarchy, only members and community. So, the first year there were more than 200 members, so it worked really well. And it was people of course from the shop floor technical people from the IT developers also.

But we got people, department, people from the legal department, people from marketing.

So, it worked really well also. And the third thing about the competence development was to build an IoT team dedicated. And at Volvo there are around 20 or 30 people, you can think it’s big, but believe me, it’s really small compared to the IoT needs of a group like Volvo. So really our strategy for the IoT team is to develop the major IoT solutions but also to support the local team across the organization so they can develop their own IoT solutions, so we call that citizen development. And it’s really key for us in our strategy to go at scale, otherwise we know we’ll be too slow, and a bottleneck for the group.

Nick Earle: If I’m understanding it correctly that there are various different management approaches, we’ve seen fully decentralized, fully centralized.

It sounds like yours is a blend because in a fully centralized model, you take all the budget off everybody else and say you have no budget. You have to get my permission. I have all the money, and you have to get my permission. And that’s often not very popular. But it is a good way of making sure that people don’t waste money.

Fully decentralized. Sounds like where you started from but although you are spending money on the central components, like the common platform, no one can afford one platform. So, I imagine you’ve centrally funded PTC, but then most of your work is on advisory, best practice, sharing collaboration, communication center of excellence, if you like, for all the different people working on IoT.

So, as many initiatives can start off as possible, but at least they’re all pointing in a similar direction.

Julien Bertolini: Absolutely.

Nick Earle: Yeah. So that’s the basics. And maybe we can talk about what type of technologies you used because there’s an awful lot of equipment in those 140 factories. So, is a large part of that equipment connected via a wired connection?

Julien Bertolini: Yes, absolutely. So the majority of our IoT initiative is around existing data that are in the factory. All the PLCs and industrial equipment are producing a lot of data.

It’s not obvious to collect all these data in a unified way and to have quality data. So, most of our work is either around collecting existing data and so it’s wired and it’s also with various industrial protocols. We have a really old PLC which is 30 years old or something like that.

We also have brand-new robots in our factories. All these animals are communicating in different ways and so we’re talking with all of these devices in a unified way and collecting the data.

One of the big challenges is to have quality data. So quality data is to have an accurate value, but also in real time and also contextualize because of our project. And for that we’re using an industrial gateway to communicate in various protocols. And we also have some other projects where we’re using LoRa. We’re using LoRa a lot for smart monitoring and also for tracking especially to track our tracks around the factory yard.

But yeah, we have a lot of different projects. We’re doing also some projects for logistics when we’re tracking our packaging. We’re doing also some projects around computer vision for quality control along the production lines.

Nick Earle: Yeah. So we’ll get into some case studies in a minute, but I’m just trying to paint a mental picture of the scope of what you are trying to do.

You’ve got, first of all, you have 140 factories. They won’t all be the same, I’m sure, because you’re in different businesses, Marine trucks and different areas of business. So, the factories will be construction, so the factories will be different. Secondly, the factories have hundreds, if not thousands of pieces of equipment each.

Yes. They can be anywhere from 30 plus years old to install an autonomous robot that’s been installed this week. I’m almost thinking of it like this huge Rubik’s cube, three-dimensional Rubik’s cube where every single one of them potentially. You must have hundreds of different protocols connecting with devices where the connection to the device was only there, so you could get limited data out of them for maintenance, I guess maintenance purposes, but certainly not IoT. Then at the same time you are using different networks. Yeah. So, you’re using wired through some sort of controller aggregator, but you’re also using LoRa for wireless.

And you didn’t mention WiFi, so you, I know the factories will have a lot of WiFi. So why do you not people might be interested. Why do you not use WiFi?

Julien Bertolini: Yeah, WiFi is quite challenging in factories because of if you use it too much. So, if we have a lot of equipment that are using it, there’s a big risk of interference and saturation of this band. So, we try to avoid using WiFi.

Nick Earle: Okay. All right. Let’s get into some examples. So, we got the size of the Rubik’s cube and where you started from.

Yeah. So, let’s maybe we can go through two or three examples. I know when we spoke previously you were talking about AGVs which most people are not very familiar with, but maybe automated guided vehicles. Maybe you can talk about that first. Yeah.

Julien Bertolini: Yeah that was my first my first IoT project.

And I was in a factory in Leon.

Talking with maintenance. And I ask him, what is the most recurrent incident that has the worst impact on your business? And he was saying that more than once a week, he had the battery failure on the AGVs.

It’s a fleet of 85 AGVs in this factory. And for different reasons you could have an issue with the battery failure. And when it happens, it’s 1.7 ton one AGV. So you need two guys to push the AGVs out of the line and then to charge it or to replace the battery and then put it back.

So that’s really a bad impact for the production because several parts are not produced and it costs a lot of money. So for me it was like the low hanging fruits because it was super easy to monitor the voltage of these batteries. And we build a solution to collect the data. And then we’ve seen that the nominal or the normal voltage of these AGVs is 24 volts. And when it goes to 22, it stops. But then we analyze the data and when we’ve seen that when it goes below 23 volts it means there’s an issue somewhere, but there’s an issue.

So we have built a system that goes to the maintenance team each time there’s a case when it’s below 23. And then the maintenance team has something like 45 minutes before the failure happens. So it’s a super easy predictive maintenance system. And so now they can act before the failure happens and it’s saved a lot of money and work.

Nick Earle: And just for our listeners, again, this is a very visual case study for me.

An AGV is 1.7 tons and takes two men to push. What goes on top of an AGV? What would you use an AGV for?

Julien Bertolini: This is for an engine plant. It transports the engine during the assembly of the engine in the production line.

Nick Earle: Wow. 1.7 million tons and then it breaks down and you have to push it.

Julien Bertolini: It’s big.

Nick Earle: And then what about the trucks? You have a good case study that you talked about, that one being your first one, the AGV. But this idea of just finding trucks, it’s always amazing that companies like yours can lose something so big.

Julien Bertolini: Yeah, it sounds crazy, but yeah sometimes we cannot find our trucks.

At the end of the assembly line the truck is not fully finished. So usually you take the truck, you go to a factory yard to park it, and then an operator will take it, do some operations and park it again. On average this happens 6x per truck.

So now you have to imagine that you are producing more than 100 trucks per day. You have a huge yard, and most of the trucks are white. And now if there’s a human error when writing down the position then it takes two hours for two people to find the truck.

Nick Earle: So we’ve seen case studies like that all over the place.

But rental car companies have exactly this problem, by the way.

But these two people take the two hours it takes. Is that literally a couple of guys walking around this huge yard?

Julien Bertolini: No, they’re taking a car and driving all around the factory yard. They’re driving around.

Nick Earle: And they don’t have registration plates on them, I assume?

Are they like pressing something that makes the lights go?

Julien Bertolini: No because the keys are in the truck.

Nick Earle: So how do they know it’s the right truck when they find it?

Julien Bertolini: There’s a barcode. There was a big unique number on the windshield.

Nick Earle: So they have to scan the barcode of every truck.

Julien Bertolini: Oh, no. They can read it.

Nick Earle: So these guys are in the field looking for trucks, which you lose. Yeah. So that was prior to an IoT solution. So how are you solving that problem now?

Julien Bertolini: Yeah. So the first pilot was for one factory years ago. It was working with GPS and communicating with the technology. And at the end of the assembly line, an operator is scanning the tracker on the truck. And then we can follow the trucks in real time in the factory yard. And when it’s done, when the truck is fully assembled, we take back the track and link it. Basically, there’s a pairing operation and unpairing operation at the end. And it has worked really well.

So now, all of the truck factories are using this solution and also construction equipment too. It’s deployed all over the world for thousands and thousands of products.  

Nick Earle: Let’s go from thousands and thousands of products to potentially millions. We’ve talked about your AGVs. We talked about finding lost white trucks in the middle of very large fields or whatever. Yeah. But what about the assets themselves? There’s a lot of things that move within these factories. I guess there’s a lot of assets that move, you have to move parts from one place to another. Yeah. So, what do you do to track those as well? Do you have a similar solution?

Julien Bertolini: Tracking technologies are a super interesting domain for Volvo. We know that we have a lot of work to do in our internal logistics, so inside the factories but also for external logistics. For the goods coming from our suppliers and also for the logistics between our sites. The logistics are something huge at Volvo. Also, our process is not perfect in logistics. So, we can see that the stock numbers are not accurate. And it costs a lot of money when there are issues.

So, we’re building a tracking platform and the first use case we’re working on is around packaging and more specifically around what we call special packaging. So just to give you some piece of the context.

So, when you want to transport an engine from one plant to another, you cannot put it on a pallet because it does not fit. And that is the same thing for a truck cabin. Same thing for an axle, same thing for all these parts. So, when you want to transport this part, you need special packaging. So usually, it’s big metal racks. That’s really specific, depending on the type of part you want to transport.

And if you don’t have this certain piece of packaging at the right place, at the right time, you can stop the production. So, the risk is really big and because this impact is so huge, sometimes we’re doing express transport of this special packaging and it’s really tight. So, what we want to do is to track all this packaging and to do automatic inventories at different points of interest.

The big challenge here is that we have something like 200 or 300 points of interest, but we have 160K assets to track. So that’s a big deployment. And that’s just the beginning because at the end we want to track 30 million packaging.

And this is just for the packaging. We know that we have a lot of other stuff to track.

Nick Earle: Wow. So, hold on. I have to stop you. So, I just, I’m trying to do the maths. Okay. Let’s say, so 5% of 30 billion is 150K, you said 160K. So just on the packaging, you’re at 5%. Because there’s going to be 30 million is what you want to do, but there’s a lot of things you want to track ultimately,

Julien Bertolini: No, it’s even worse than that.

60K is for the special packaging. 30 million is for the standout packaging. But after that, we want also to track the port, to track the tools, to track the forklift. Name it that we’ll track it.

Nick Earle: If it exists, you’ll track it. So, it’s massive IoT.

All right, so this is a big, this is a big, long range, multi-year project and problem.

Julien Bertolini: Yeah, absolutely.

Nick Earle: And you, and how are you doing it? How are you starting off solving that problem today?

Julien Bertolini: Basically, we start with a POC, or I should say an MVP. So minimum viable product.

Yeah. And I’m saying MVP instead of POC because to start this kind of initiative I’m not just checking that the technology is working. That’s only one detail in the project. What I’m doing is I’m taking the purchasing department from the beginning, I’m taking the legal department from the beginning.

And also I’m working directly with the business to build a first version that is valuable and scalable. So, I want to be sure from the beginning that I can go at scale. So yeah, we have a different strategy for that. And one of the strategies is to build a big tracking platform that is vendor agnostic and technology agnostic.

So the idea is we can plug any kind of trackers from any vendor and so we can solve a lot of use cases.

Nick Earle: And we did talk a little bit about this previously. I think in this use case we talked about Wired, we talk about LoRa.

But I think I’m right in saying Julien that in this case there is some you use BLE so Bluetooth tags to cellular gateway and then backhaul the data from the cellular gateway.

Julien Bertolini: Absolutely.

Nick Earle: Yeah. So, you’re really using a little bit of everything and you’ve got. So much more in front of you to do.

So that’s probably a good opportunity to pivot from what you’re working on. I’m sure there’s a lot of other projects, but what you’re working on to the future because maybe we could talk a little bit about what you are doing because, and let’s start off with LoRa because I believe that on the metering side. Smart metering is something that’s energy management; smart metering is something that’s of huge interest to Volvo.

Julien Bertolini: Yeah. And we’re just at the beginning.

Also, in this domain of LoRa devices devices do metering. So, for water, electricity, for gas. But we have so much more to do. The energy management system is for the moment we don’t have any centralized solution. So, it’s all decentralized at Volvo.

And we would like all the plants to have the same solution, so really to mutualise the efforts and to be better in this domain. So, we’re really LoRa experts now. It’s this network that’s deployed in the main factories at Volvo. And it’s working really well. Tools to do a good job, promote the solution and to deploy it at scale.

Nick Earle: Why don’t we finish on a big subject, which a lot of people are talking about, and I would say a lot of people are frankly a little confused about, but everyone’s talking about it which is artificial intelligence.

And AI at the edge. Now that has got to be a big topic. You must be asked about that by management. And can we not use AI? Can we use AI to improve that?

Julien Bertolini: Yeah. I’m convinced that AI inference at the edge will definitely revolutionize several domains and it’ll help a lot.

However often the management is saying, oh, we can solve everything with AI, and they don’t really understand how it works. So I’m used to saying, okay, you can use AI, but only if you have good IoT solutions. And why I’m saying that is because the goal of IoT is to get quality data, and that’s the foundation to build an AI model.

So if you don’t have quality data, you’ll not get anything from AI. Crap in, crap out as they say. You should focus on quality data in your IoT initiative, and then you can build some good AI models and have great values with that. But you have to do that by stage and for example. For example, in logistics, we don’t have good numbers for our stack, and they have tried to put some AI on top of that. We didn’t get any results.

As expected from me, the result was not good. And I know if we can put a good tracking technology which can provide accurate data then we’ll have a better result.

That’s the lesson now – good use of AI.

Nick Earle: And I want to come back to that. When we talk about the edge, which let’s assume is the assembly line. I assume the vision is not just AI, it’s the human interpreting the recommendation of AI so that they can make quicker decisions locally rather than based on what’s actually happening from the data being collected from the assembly line.

Julien Bertolini: Yeah, definitely. That’s one thing also that we’re deploying all around the world. It’s quality control along the production line using computer vision. It’s super important to be at the edge. Why? Because when you are doing quality control if you see that something is wrong, e.g. your AI model is saying that the quality is not good enough then you stop the production line. And you definitely need an operator to understand why the AI is saying it’s wrong.

It’s not only good technology, but it’s also skilled people. And that’s why AI at the edge could make a big difference because you can be robust but also with a low latency and with a direct interaction with the people that are working on the production line.

Nick Earle: So I think that what you’ve said there as we finish, I think what you’ve said there is that conventional wisdom, or at least, there’s so many podcasts around about AI and you read about AI and you said two things, which I think everybody would agree with. But I think you’ve said one thing which for most people the penny hasn’t yet dropped.

So let me attempt to summarize that. I think everybody talks about the fact AI is not going to automate everything. It’s going to be AI and a human. There’s a lot of debate about whether human’s jobs go away, and what you’re actually saying is no, you need to take real time decisions on the data, especially at the edge, especially if you’re going to stop the line. So you can’t just rely on AI. You need AI and a human who has a lot more knowledge and a lot more capability to say, can I trust the data?

Should I stop the line? Or have I seen this before? Am I okay to let it go through? Most people would say. Yeah, it’s always AI and the human, AI and the human. And the second thing that you’ve said is that ‘crap in, crap out’ which is true, and it’s only as good as the data you put into it.

And of course AI is well known for hallucinating. And it hallucinates really well. It’s so convincing, even in the research paper field. It will invent research papers from professors that don’t exist. And so, it can certainly invent a situation on an assembly line.

But the thing that, and most people say yes, it’s only as good as the data, but I think the point that you’ve made, Julien probably as clear, and we’ve talked a lot about AI on this podcast, but as clear, if not clearer than anybody else on the podcast, is that IoT is essential for AI, and I don’t think that the penny has dropped because what you’re saying is you’ve got to collect as much data from as many things as accurately as possible with the highest level trust that it’s the real data to feed the AI model.

So the more data from real life things and events that you collect and feed into your AI system, the more you can trust the output of the AI system. And IoT is my words not yours, but IoT is essential.

IoT deployment and connectivity of as many things as possible is essential to get the maximum benefit out of AI in the future. Would that be a correct statement?

Julien Bertolini: Yeah,

Nick Earle: Yeah.

Julien Bertolini: Great job. Great summary. 

Nick Earle: And I think that’s really important because we see a lot of niches.

We have about 800 customers, mostly industrial customers at Eseye but we see a lot of niche use cases where people say, oh, I’m going to do this project. I’m going to collect data from that group of things. What you’ve talked about is wired. You’ve talked about LoRa, you’ve talked about sensors, wireless, and now massive IoT, the 1.6 million, the 30 million, and the however many hundred million.

The idea of connecting everything. Which we’ve talked about for a long time. Everything that has power will be connected. We’ve talked about that for a long time, and what you’re saying is, and the reason why you need to connect everything by whatever technology or protocol is because to get the maximum benefit and the productivity and the efficiency out of AI, you need to feed it with all of this data because you can’t scrape that data from databases on the internet. It’s not academic content. It’s not a book. You can scrape the internet and get every book in the world. And so I do agree with you. One of the reasons why people will start to accelerate the connectivity of everything is not because they want everything to be connected, but because they’re under pressure to actually get a better AI model.

And I think that’s something which people are only just beginning to start talking about. So Volvo is a great example in my view and what you are doing of a company that gets that and is embarking upon a very ambitious project to literally connect everything in Volvo, which is a multi-year, multi-year journey.

Which is your job. And you’re still smiling. Okay. Listen, we are just about the exact time that we use for these podcasts, so I’m going to leave it there. So, I wanted to thank you. For describing it so well and agreeing to come on the IoT Leaders Podcast.

For those of you who perhaps want to reach out to Julien on LinkedIn or whatever it is, Julien Bertolini at Volvo. He’s based in France and has a very interesting story around a very large company who were pretty advanced in this space, but also at the same time very honest in opening up, in describing what you have done, what you haven’t yet done as opposed to saying that you’ve solved everything, which is very refreshing.

So, I will leave it there. Julien, thank you very much for thanks a lot being a guest on our podcast. It’s been a pleasure to talk to you and good luck with connecting hundreds of millions of things going forward.

Julien Bertolini: Thanks a lot. Bye-bye.

Nick Earle: Bye-bye. And good to talk to you. Thanks, Julien.

Outro:
You’ve been listening to IoT Leaders, featuring top digitization leadership on the frontlines of IoT. We hope today’s episode has sparked new ideas and inspired your IoT and digital transformation plans. Thanks for listening. Until next time!