Amir, welcome. Thanks. So, absolutely pleasure to have you here today. And I think for the people who don't know who NVIDIA is and what you guys do, can you maybe give a little bit of a glimpse on that? Well, let's do that. So I guess that NVIDIA is mostly known for GPUs and our stellar performance in the stock basically. But that's maybe not the full truth. So we are more of a software company these days. I guess that we are now closer to 40,000 people, employees, and 90% of them are engineers. So we are very engineer focused company. And 70% of those 90% are software engineers. And that might come as a surprise. And well, the task of those engineers and those developers is to solve use cases. And those use cases are sold in the lines of businesses, against the lines of businesses. So we find really, really tricky problems to solve. Usually manufacturing operations or any industry vertical. And we go about to build a stack to solve that problem. So that's what we do. And that's maybe just a great segue to the case today. Yes. So how are you guys working with Scheffler? What is basically that you're trying to do together? Yeah. So maybe we should set the stage by talking about Scheffler and what they do. So they've called themselves the motion company. So anything with motion, they're probably integrated in some way, shape or form. So it can be powered trains, powered like anything that powers engines, gearboxes. You have robotics, components in robotics that make arms move. So joints, that's their business basically. And yeah, so what you can see the core objectives behind. And that's what we are aiming to work against. So at the end of the day, it's about integrating half of their plants. So they have 100 plus plants. So into Omniverse. And that's basically digitalizing the whole thing. So any assets within that, the whole processing, new processes that you invent in that factory. All of that basically. Okay, but I understand that. But if we just take a step back and think there is a variety of things we can do out there. Right now, there's a lot of our clients that are super pressured on the margins, on cost. There's labor. There's a lot of, you know, things happening around the world. Why invest on this? Why do this? Yeah. So I would say that this actually is part of the solving the problem. So we talk about, for example, labor, labor costs. Well, if you can automate stuff, then probably you can digitalize, automate, and simulate and do all kinds of magic in that sense, right? You have, for example, supply chain problems all over, especially in this crazy days in the world. So you can go about and try to build stuff the old way. Or you can simulate all of those things and basically just see what goes, what cannot work, what will work. Let's say that you build a new factory and you cannot get new components into that. You cannot get the robots that you aim to have. You need to source them from somewhere else. Do you go and pilot that, like live and do that? Or do you simulate that first and see what works and what doesn't? So all these kinds of leverage you can do. So basically, we're talking about building a certain kind of infrastructure, right? Correct. But if we then approach that to a few concrete operational problems that they had, maybe you have an example or two to give to us, a factory, a warehouse, or what is that you're doing. So we have basically three very good ones. I would start with a Brownfield example. Yeah. So a new facility needs to be built in China. How do you go about doing that? Easy way, right? Well, you probably have a factory that works quite well. So if that is digitalized, you have all that like printed out in a blueprint. Why not take that blueprint, put it into where it's supposed to be, in this case, China. And then you can simulate and emulate all of the various factors that are different. And by that, let's say humidity, for example, it can be different. And these machines are very, very complex. Like they have many moving parts. So will that disrupt the process flow of manufacturing? Can you compensate somehow? If you can simulate all those things, you take away a lot of risk. And of course, that reduces downtime and, well, increases efficiency. So are they actually using to say we have a flow from point A to point B in assembly of motors or whatever it could be? Yeah. To say how this assembly should look like in China and do it in a different way because you have digital assets? Is that correctly understood? That is correctly understood. And also if you want to implement or improve like incrementally, you can also introduce those digital assets in that new factory and see how it would work out. So it's both and more. So, okay. Now I'm starting to get the idea of here. I'm a bit slow. But if we basically look into how they traditionally been doing that, we're going to touch that in a minute. But I think a lot of us are wondering, it must be quite an effort to set this up. So what are the impacts? What are they getting out of this? Well, if we go back and see at the core objectives. So basically that's the objective. And the idea is to, at the end of the day, provide a digital ecosystem for all of these factories. And, well, we talk about efficiency, of course, driving down downtime, introducing faster implementation of new products, new product line, new processes. All of that is very much better done. If simulated first, you take away all the errors. And you can do it also in scale, in parallel, basically. So you can try out many, many, many things. And then you choose on the things that you like. And you focus on those. And then you start to prototype those. So sorry to drill you down a bit on that same again. But I just, you know, we are from operations. So I'm really after the hardcore numbers here. Hardcore numbers. So if we're looking at, I don't know if it's lead time or process time, have you seen any results on doing this? So benchmarked up until now then. And this is a continuous effort. We have a couple of other use cases we can go into. But we see basically a operational OPEX goes down by 60% the cost of that. 60%? 60% up until now. So it's probably increasing. And then also introducing new assets. So completely new assets, be it whatever, goes up by 40%. So the speed of innovation or the speed of implementation. All from like processes, but also into like proper new warehouse. So if I want to basically look at my manufacturing footprint. And I'm looking at a new factory. You're saying that deployment time for this new factory is basically decreased by 40%. Up until now and increasing. And it's quite easy to understand why. So you can go about, for example, programming a robot in a new factory in the old-fashioned way. You run into those problems of environmental differences, let's say. That can happen. And then you have, and that process might include 300 assembly points. So how do you go about that if you want to automate that and get that increase in speed? Well, you simulate it. And you simulate it as much as you can. And you automate everything that you can simulate. Of course, you will run into that 1% that's really, really hard. But then you use reinforcement learning, for example, for those things. So you would have a human being doing that. You have AI looking at what's happening and how to do it correctly. And you feed that into that 1% that doesn't work. And you can automate that also. And this works especially well in prototyping. That is very interesting, both, I guess, from an R&D perspective, when you start to introduce a new product. Yes. But also from a running production. So now maybe thinking about a company that is, you know, very traditional. There's a lot of engineers that work in a certain way. And now introducing something that for some it might seem completely, you know, from another world. How do you basically go about that? Yeah. How do you do that? Yeah. So, I mean, there are, it's very, very easy. Not easy. It's never easy. But for Schaeffler, for example, there is a clear guidance from the leadership basically. Saying that we are going to introduce this to half of our plans. And the idea is to introduce a digital ecosystem. Now, remind me of that because we need to talk about that also. So, if there is a clear vision. And I thought that we heard the word vision. Yeah. That kind of takes away the complexity of people and process and organization. Because if you can automate ideas and production lines working against that idea. Then you will have the company behind you basically. So, you want, and that takes away another problem also. So, I think we heard the like perpetual pilot phase or something like that. That is a very, very big trap. So, you do this proof of concept and pilots. They solve a technical problem really, really well. Everybody high fives and are happy. And then what? So, where's the business outcome? And I think that that was also a theme. So, work against that business outcome. Automate as much as you can. And even start with the complex problems. That's quite okay. If you think that you can automate those, just go for it. But have that clear vision in mind. I think that's the key. Okay. But then you touch on something, right? Because we just discussed with Metas that one of the advantages that they had was the data quality. Yes. That they had from the start. Yeah. And now I'm thinking, okay. We know we've been to quite some factors that that might not be the case. So, if we touch on data sharing, data quality. How do we even go about that if our data is not fantastically perfect? And how do they handle it? Yeah. So, data quality in that case. So, data usually sits in silos, right? And those silos can have various quality of data. So, either you have good historical data you can lean on. So, you have sensors, you collect those things, and they are accurate. All good. And that's especially, especially important when we talk about the digital ecosystem. So, I'm taking a little bit of a sidestep here. No, that's right. So, you need to have that data quality in place because you would share digital assets with your ecosystem. So, let's say that I make these robotic parts. I feed them into my robotic maker, which sells them back to us. It would make so much more sense if we use the same common language and same common platform. And we can try out, and I can trust the simulation that's coming out of them to implement in my new processes, in my new factories. So, are they doing that? You mentioned a bit on the ecosystem. Yes. So, that is the whole foundation, basically. And that's the Omniverse. That's the whole idea with Digital Twins and Omniverse, in this case. So, we kind of, we give to our customers, and they give back to us in that sense. So, we leverage each other in that sense. So, maybe just now that I had the opportunity to ask a lot of questions, but the audience also wants to ask a few ones. So, I'm just going to read two of them for you, but just start with the first one. If an operational leader in this room wanted to start tomorrow, what are the main capabilities that matter more than buying the right software? That's, you can answer that in a two-way. Basically, I think the most important is still the vision, and you work against that vision. And then the other one is, of course, automation. Can it be automated? If it can be automated, then it absolutely makes sense to digitalize it and use AI as much as you can. AI is very, very good in automation in that sense. It's kind of the purpose of it also. Okay. Then for the second one, for operational leaders here who are curious, but somehow maybe cautious as well, because they have other things that are pressing in the budget right now. What is one smart and first move they should make and one common mistake they should actively avoid? Yeah. So, one smart move, first move, and then one mistake they should avoid. So, yeah. This goes a little bit back into the people and process side. So, again, working against the vision. Start small, but have that vision in mind. Scale out in waves, and then you walk from there to that ultimate goal, basically. Do not do the perpetual pilot POC mistake. So, have a buy-in on what you're trying to do, and when you do it, you see the clear business outcome that comes from that. I think that's a more important point. Okay. So, now forward looking, right? So, looking three to five years ahead, maybe let's skip to three. Which part of operations do you expect will look fundamentally different because of physical AI and other things that you're doing here? Well, I think for Schaeffler, but for the whole world, all of it. So, I think that the projection is $5 trillion in new investments upcoming three years when it comes to manufacturing and heavy manufacturing. So, that's new investments coming up. You can do it the old-fashioned way, and you can spend time like 80% in doing it, like losing 80% out of it. Or you can use it like in a new way, in a digital way, and using AI to do all of these magic things that you can see behind us. So, what is this $5 trillion investment on? Is that capacity? It's new investment in manufacturing. So, new plants, robotics, processes. That's the project that's seen within three years. So, if you're going to do that, I mean, that's why we like that industry quite well, right? It makes no sense to do it in an old way if there's a better or new way to do it and enforce it with AI. Again, let's take that prototyping example, for example. I think for Schaeffler, we went down from 600 hours into half a day. So, let's say 12 hours from classic prototyping into new ones when it comes to robotics and parts into robotics. Okay, so basically you're able to take one robot that you were supposed to integrate into the next factory and you're going from how many hours to how many hours? 600 to 12. 600 to 12. Yes. I guess that's a pretty good return on investment That is a pretty good. The cost of system integrators and other things in that. Absolutely, absolutely. And that's only for one thing. And then you can do that and scale that out also for other parts. Fantastic. Well, I think we're running out of time. So, thank you very much, Amir. And thank you so much for your time. Thank you. Yeah. Yeah.