A wonderful good morning from Seammens in hall 27. Hanover Messa is live and in action and we are coming to you live from our booth with engaged colleagues. We are full of energy on this day two at this world where the industry meets. There is digital twins, there is AI, there is industrial metaverse, and there is so much more to explore here at our fair booth, 2,000 square meters with roughly 55 exhibits, technology to touch. And Max is my co-pilot here. We are two of the moderators. We have a wonderful stage program. We are covering all the topics which are of essence for our customers to solve their challenges which are big and there is a lot of challenges they have to face. It's energy which needs to be cut. It's waste which shall be reduced. It's the resources which shall be considered as to be taken very very care and not to be used too much and not to used in the right way. Max is looking at me like what is she talking about? But we are talking all day from 9:15 until 6:00 in the afternoon and there is lots of customers coming here to our booth. There is lots of tour guides taking care of all those visitors. So if you have not decided yet whether you want to come to Hanover or not, that's definitely a go now to click that button, get your free ticket and come to Hanover and see for yourself. Brazil is this year's partner country. That's because Brazil is using a lot of new technology. They are using a lot of solutions from semens in regards of solving problems not just because of energy demand there is big as well but also solutions in process industry which need to be taken care of. We have Nura one of our customers who is here on site. We have Axia and AIA and we have lots of other topics and I'm handing over to Max now because he's the one to tell us more about what's going to happen on stage.
Thank you Christine and yes this time it's my turn to say you've done your homework. Very very well done. All the topics I think you mentioned them that we're covering. So yeah our stage program we have our main stage as always and we have our gallery studio. More about that in a second. And we have our colleagues Miki and Izzy covering everything here at the booth as our running reporters. So hopefully by the end of the week we'll have been everywhere here at the booth. We have on stage or during the entire program. We have customers, we have experts in panels, in talks, in presentations, in product launches, the full nine yards if you like. So you're not going to miss a single thing here at all. You mentioned one or two of our customers. I've uh cheated a bit. I've written a few of them down. Uh what you can expect today in our program. So we have PepsiCo, we have Tata Electronics, Natura who are handing out our lovely goodie bags to all of our speakers. We have Danfos, I think you mentioned Axia already, Canal, CASMT, Aramco, and of course Audi who are here with a with an actual car just outside our fair booth. So uh if you want to come to handover then make sure to check out the car as well. So um yeah many many more customers and many many more partners as well. Speaking of partners, it's our entire partner ecosystem. Yeah, we believe that this digital transformation journey is only possible with a collaborative approach. No solo efforts anymore. So we're partnering up with the likes of Accenture, Capgeemini, AWS, Nvidia, Deote and many many more. They're all around our booth. We have three dedicated areas for that. CPG, so consumer packaged goods. It's a digital enterprise showcase. We have our technology deep dive. That's where we're standing here with four dedicated customer islands to to basically show the entire value chain and we are showcasing the future in our innovation hub. So, a lot going on here at our booth. And um I think we're going to hand it over now to our wonderful colleague Militia who is on our gallery studio.
Yay. Good morning everybody. And good morning to day two. We are filling this day with realworld innovations and so many smart factory stories, sustainable infrastructure, industrial AI. And you know what counts most? We're telling real stories. We're telling stories about CPG. We're telling stories about our partner Brazil. So, a lot of love is going to be shared in the air today and I hope you're going to follow all the live streams, all the live sessions downstairs on the main stage with Christina and M. But I'm most excited to see how industrial AI is taking shape in so many different realworld places. like I wrote down from automation built from the bottom up to Brazil, cyber security on the shop floor everywhere of course and also physical AI moving from crowd cloud to the factory. So for me that's like the highlight of my day and we're mixing a little bit of real life science, manufacturing, people, culture. So we we're telling the whole story here today and I'm glad to be here and welcome you to the stage and the gallery and I'm looking forward to my sessions and I hope you're looking forward to it too. So see you around and with that said I'm handing over to M easy is downstairs on the floor. Hey, how's it going?
Hello. Hello and welcome back to the floor. We're at day two at Hanover Messa. I'm joined here by Sebastian Hull, a marketing manager with Seammens for direct, current, and data centers. So, Sebastian, are you excited for day two? How was day one for you?
Absolutely. Day one was amazing. We had lots of interesting conversations with customers. So, I'm excited for day two.
That's great. Okay, let's kick it off with we are hearing more and more of more and more often about direct current grids in modern AI data centers. Why is direct current distribution an issue at all? Well, the main driver is the enormous power densities of AI factories and the companies like Nvidia show uh rack designs that go even beyond 1 megawatt and that's where direct current has certain advantages. So you don't have any conversion steps from AC to DC where you lose power. So you have 8% more energy efficiency and you have uh no conversion losses, no conduction losses um and up to 15% more power and thereby also you can save up to 50% of copper and you can integrate uh renewable energies and battery storage seamlessly so directly to the the DC bus.
Great. So direct current sound promising but also challenging. What makes protection in direct current networks so challenging?
Well, there are three main challenges. First one is the very fast increase of short circuit current which is 10 times faster than an AC circuit. So you have to be very very quick when it comes to protection of the devices and the natural zero crossing what you have in AC circuits and use it to cut the arc that is not existing in direct current. So you need a special technology to avoid these arcs.
Great. And which protection devices can really switch DC safely? Which technologies come into question here?
Basically there's one certain device which is kind of the gold standard here. It's a semiconductor circuit breaker which we are launching here on handover fair right now and that's our central secb which is based on semiconductor technology and it's ultra fast up to a thousand times faster than common devices. So they can switch off arc free and within microseconds so there's no damage to electronic loads like the GPUs in data center which are very expensive. So, this is kind of a door opener for us here.
Wow. So, this is a really exciting product we've got here to show at handover. So, if this is interesting to you, please come down to hall 27 and you can speak to Sebastian yourself about the new Centron SECB. And with that, we're going to hand it back to Max on the main stage and Militia in the gallery studio.
And that's right, we're in the gallery. So, everyone here in front of the stage, good morning. How are you all doing? Ah, thank you. Yes, very good. So, we have a few empty seats here. So, make sure to fill them up. There's no reason for you to stand around when you can sit. So, they're all here for you. All these seats. Please make sure to take them. Okay, we're ready now for the first session of the day here on our stage. And we are talking about recipedriven control because manufacturers today they need a lot of things. Faster changeovers, more consistent quality and clearer batch execution even at the equipment level. So this presentation is going to show how we can bring standard recipe control to process machines and production cells as a modern web-based bottomup SCADA solution. So here to tell you how this works, please welcome on stage product manager from Seaman's Horge Mendoza Saledo and global marketing manager also at Seammen's Ramod Rao. Welcome gentlemen.
Really thank you much. Thank you people and welcome today. Thanks for being here in the morning. Um today we are going to talk about our latest innovations regarding recipe driven control.
Yes, I'm going to start off with introduction and then Jorge is going to come in with more of a deeper dive into the topic.
So let me start with an image. I'm sure most of you would like. Yes, it's a cake and it looks delicious, doesn't it? Now, what is the first thing that you would want if you want to recreate this cake? Any answers? No. Well, it's a recipe. Everyone has a recipe. The grandmother may have one for the cake. A chef may have one for his signature dish. The logic is pretty simple. You follow the steps. You take the right ingredients in the right order at the right temperature and then you get the same result every single time. Well, if you're into cooking, it's not every single time. It's most of the times. But then that's the magic of a recipe. It captures knowledge and it makes the complex repeatable. Now let's take this idea of a recipe into industry into manufacturing into production. What do we have? We have an industrial recipe. It follows the same principles. We have the materials and the quantity, the list of the machines and equipments to be used and the processing steps followed to make a product. Now this industrial recipe is used mainly in batch processes. What are batch processes? Where are these used? Well, the batch processes are used to make a lot of products in different industries such as you would have yogurt and cheese in the dairy industry. You have creams, soap, shampoos in the personal care manufacturing. Then you also have chocolates and ketchups, mayonnaise, paints, the list goes on. These processes are typically controlled by a batch control system which executes the recipe and then also controls the equipments used in production. When you talk about modern production, what do these industries want in a system? They're looking at a system that supports faster changeovers to give more product variety but still maintaining the same consistent quality every production cycle. To achieve this has its own challenges. The recipe is still there. The logic is still ready. But these challenges do not always start at the production line. They start much earlier right at the machine. Every batch, every sequence, every critical step begins right at the equipment. The machine level we're talking about can be a mixer, it can be a reactor, a blending unit, a dosing skill. Traditionally, this level has always relied upon custom PLC logic, manual operator steps without any structured recipe behavior. The result non-standard recipe procedures, limited recipe flexibility and of course difficulty in engineering creating a gap between what recipe defines and what the machine actually delivers. Now this brings us to the question what if machines could run standard recipes and not just PLC logic. So Jorge how are we answering this question as seammens?
Yeah very good point Paramut and to answer this question let's have a look deeper at the market. So we find every day there two main challenges. The first one we talk about product variability to really face every request from a customer to really be able to produce any variant of the product. This comes usually also very related with a second main challenge which is the engineering complexity. No. So less knowledge of the market with the deeper capabilities on the on the on the engineering. And all this we have a clear vision from CMS to cover these challenges with two main goals. Every production cell, every machine should be capable of produce with an adaptable process. Everyone should be capable without any re-engineering to really adapt the process to any request. Together with this to tackle the engineering complexity we have also a clear answer to this standardization at automation to tackle all this together that's what we built a new system I am very pleased to announce today here at harfare 2026 for the first time available worldwide winc unified sequence a new modern recipe-driven control system for batch processes. It is specifically designed for those machine builders and production cells which requires the maximum flexibility at the production but having the having it with the minimum engineering efforts with sequence. We start at the very bottom at the PLC with our S7500 family. We are able to run the batch directly at the PLC to really reach the maximum robustness and reliability. On top of it, we are based on our most innovative visualization system, WCC Unified. Here you will be able to customize your screens and customize your your workflows so that you can really operate, execute, monitor your batches as you wish. And at the very top of course sequence brings you all tools needed to really produce your batches starting from the creation of recipes, maintenance of recipes, monitoring, operation and execution of batches and at the end for full transparency and optimization of the process batch reporting. But the key innovation here at sequence comes when you look at our goals. The first one, flexible production. With sequence, flexible production is built natively on the system. With the web recipe editor, you are able to really customize and adapt the process of your machine or your production cell. Very easy. No engineering and no coding. You don't need to believe me. We can see it. Here we are at the recipe editor of sequence. An example of a mixer. Just a standalone machine with a mixer where you can produce really tons of products with different variants. Here you are capable of create your recipe not just parameters but also the behavior of the process and the machine with a simple drag and drop easy. Of course once you have your products and your recipes already there you can adapt them very easily again create variations. And if we go one level further then we have a production cell. Here you are able also with the same easiness without engineering and without coding you can also adapt the behavior of every unit independently and at the very end dependently on on the flexibility you may need orchestrate all units at once. But this easiness usually comes with a with a higher price in in in in terms of engineering efforts. In this case, how do we really tackle it with lower engineering efforts? Because this is all on the productive system and you don't need to go to any coding, any engineering. Back to our second goal, standardization on automation. sequence is based on the SID8 standard very well established in the market for batch processing. With the idea standard, the engineering is able to really model every machine, every unit to their own needs, still following clear rules and allowing this flexibility at the end. We reach it thanks to our S7500 PLC family and with the library concept of TA portal. Here all customers will have already a base following the standard but being able to customize every of the machines to the custom needs. So it's the perfect balance between flexibility and customization. And all this flexible production and standardized automation with WCC unified sequence. Now Premote please help us a little bit how to fit how to position sequence in the current market in the current portfolio.
Sure Jorge. So we have seen what Jorge told about machine level automation. Now if you see traditionally we have systems like Seaar and Brahmart which have delivered proven top-down SCADA based recipe control for complete production lines and plants fully aligned with ISA 888. The capability is mature. It's essential. It's proven. But we have seen now that manufacturers also require a bottom-up clarity to bring structure to where the variability is the greatest right at the equipment level. That's what sequence helps deliver. A modern recipe-driven way to run machines and production cells with consistency and transparency. Remember that recipe we saw from beginning? The one that captured knowledge and made uh complex things repeatable. That's what sequence brings to the machines from recipe to product every single time. So hey, if our customers want to know about binsc unified sequence, what do they do? Where do they approach?
Yeah, also a good question. Sequence is not yet released. is being released at the end of the month. Therefore, we only have it today here and this week at our booth. You can go over there and to check it. We are together also with our customer process showing an example how a machine builder can give the maximum flexibility to their machine and to their end customers. Also, please stay tuned at the end of the month. or social media, our websites, everything, it will be released. Thank you very much.
Thank you very much both of you. Amazing stuff. We have a little bit of time left. Question to to both of you. You've been here yesterday as well. Yeah. All day. Um maybe Premwood, start with you. What have you been talking about with people here at the booth so far?
I mean we are located in in the CPG area where we were showcasing sequence as uh H or told earlier. So customers are coming to understand how is it easy how it makes their life easier at the machine level. They want to come they want to experiment you know try the drag and drop feature that is there that we are showcasing it live. Uh and they're pretty interested and they're looking forward to like when can we have this that's why I asked the question again today you know when can the customers experience it.
Yeah. Okay, thank you. And what about you, H? What have you been talking uh about with the with the visitors here?
Well, it was uh very interesting already yesterday because um a sequence is very expected at the the market currently looking for for a recipe and batch system based on a SCADA and on a on a PLC. Yeah. So many years already, let's say, waiting for this and then the customers were already a little bit excited about this. Yeah.
Cool. Pretty good. Well, I hope you have much more of these conversations throughout the week. So, yeah, thanks again. Great presentation. Can we have another round of applause, please, for Thank you. Enjoy the rest of the fair. Bye-bye.
So, up next, it's time for our first talk of the day here on stage. And um we're going to talk a little bit with our partners from TCS because manufacturing has reached an inflection point and AI AI is no longer just an add-on to automation. It's becoming the automation layer itself. And up next, we are going to be talking about what it takes to embed AI into robots, AGVs, drives, PLC's, sensors, and more to ensure that factories can sense, think, act, and learn. And we've also published a white paper on this in collaboration with TCS where we translate this vision into architecture. So, please welcome on stage our guests. We have Shini Vaza, Naresh, and Mark. Thank you. Round of applause.
It doesn't matter who comes first because we can have you sitting underneath your profile. You see, we've made it easy for you. Hey, that makes sense. We'll make it logic. We'll make it work. Awesome. Gentlemen, it's great to have you all here. Um I want to start this talk a bit differently from the other ones. Mhm. Oneliners you you and then you what's the one misconception that leaders have about physical AI? Shinaza would you like to go?
I I I'll say it two ways. Yeah. The positive and the negative. Right. Positive is you know you don't really have to do anything. Everything is autonomous. It works beautifully. Nothing to worry. The negative is you think it's a magic wand and you just wave like we see in all the pictures around. And it works. Right. So that's where the misconception. The reality lies somewhere in between.
I'll just add on to what Shini said. The misconception is, you know, often times people think physical AI is digital AI or Gen AI plus robots. It's not. Yeah, it's more to do with latency, physics, safety, irreversibility, a lot of things. It's the full stack and AI that acts is tougher than AI that talks and gives insights.
One liner Max I'll say as many technologies we overestimated in the short term and underestimate it in the long term. And and the misconception u you know to tease you a little bit is you said AI is becoming the automation layer. I think that's a misconception and and we'll unpack that.
We will we will unpack that. So yeah that's a great way to get us going and uh and with that welcome again. So Mark you are head of operations software here at Seammens. Um Naresh you are global chief technology officer for manufacturing at TCS and Shini Vaza you are vice president and global head for industrial autonomy and engineering also at TCS and with that thank you once again for making the way here to handover and Mark for bringing the guests along.
So Shinaza maybe we can continue with you. Um, let's say you had half a minute or so with with a manufacturing CEO. You you can pick who that CEO is. I don't mind. But what would you talk about with that CEO when it comes to, you know, changing when AI moves into the physical layer, you know, when we get beyond pilots and dashboards and everything, what would you be discussing there? What's important?
I think whatever happens, manufacturing will remain manufacturing. So the most important aspect is what is it that makes a difference for that particular CEO. It could be the yield, it could be the throughput, it could be the quality. How do the AI elements, the physical or the digital AI, whichever way you want to call it, add up to that goal? So I think the most important discussion still remains what is the business outcome that that particular enterprise is looking for and how do you work backwards from that goal to get to that objective. It is unfortunately most often discussed from left to right. Right. And therefore you end up discussing a lot more about aspects such as maintenance, downtime and all which is good but it adds up only to a delta. You have to work backwards from the outcomes that you're looking for and where does your competitive differentiation lie. So that would be the topic of conversation.
Okay. Now Mark, our colleagues here have put together another amazing booth as always. Um, a new home by the way here in hall 27 for the first time in case you didn't know. We have our CPG showcase, we have our technology deep dive and over here we have the innovation hub where we're talking about the future and something that that our experts are talking with with all the visitors here is of course the the autonomous factory. what are the enablers of such an autonomous factory for us at Seammens?
So I think um there are four five maybe major elements that I think about at least in terms of of of enabling it at at the you know lowest level if you want there's softwaredefined automation next generation of automation. So I said cheekily that AI is probably not going to be the automation layer and I'll give you an analogy. If we drive a car today, even a self-driving car, the ABS braking system is not an artificial intelligence, that's a very deterministic program that knows braking coefficients, how much time to pulse, get sensor feedback. It might be an AI that looks through the cameras and decides there's an object running in front of me and now I need to break. And then when it decides to break, it hits a deterministic system that then goes ahead and breaks. And the same thing on the automation layer. I think that will be highly deterministic. The way a conveyor belt runs just to be pedestrian or the way a chemical factory has to be opened or closed and mixing and so on. Very deterministic, but it has to be more adaptable. It can't be automation setting concrete. It has to be automation that can be interchangeable. So that's software defined automation at the bottom. Then we have physical AI obviously coming in more and more autonomous robotics thinking. We're going from programming a robot to giving it a goal and it figuring out how to do things. That's a big component of us transforming into an autonomous factory. We have the digital twin of the factory because I think we increasingly need to treat the factory as a product. So when you design this car again, you run hundreds of thousands of virtual crash tests of different configurations of a car to find the optimal car. I think we need to treat the factory the same time. As it gets more adaptable, there are more and more permutations of the factory and you need to figure out what's the optimal configuration for your given thing. That requires a digital twin of the factory. And then there's the factory AI brain or whatever we want to call it, AI operational layer that will run and orchestrate these things depending on what we want. All of it coupled together with a fabric. So that's I think the things we think about. But then I'll go back to then there's what is the business outcome. It it's not just about all right these are the technology building blocks but how you deploy them where you deploy it where you deployed it first would be based on like what is your biggest business imperative and then you work yourself backwards and that is also I think where the partnership we have come into play.
Yeah and it's exactly what you said there the three core building blocks comprehensive digital twin softwaredefined systems and industrial-grade AI brought together with this unifying data fabric for cross-dommain data intelligence for exchange. And all of that then made scalable with our seams accelerator which you can find throughout the booth. Um Nesh now these factory or AI factories AI infactory concepts they all sound very nice and I'm pretty sure the seaman's ones will all work fine but not all of them will. Some of them will fail. So where where will they fail? Where could they fail? and what do you see as like minimum technical requirements that have to be in place before you even go on this journey?
Yeah, I think I think it's a very relevant question in today's times. If you look at last 24 months of all AI initiatives and I think it's a learning also what we have seen is categorically if you try to boil the ocean and start with the P or a pilot first mindset technically you will not answer some of the core complex questions like you will not answer data integrity availability data quality CISO concerns because you are only dealing with a happy path. The moment you switch your mindset to a production first aspect, then you will ask all these tough questions on day zero. Yeah, I think going with a PC or a pilot first mindset will have some programs succeed but largely what we have seen is you know they will not scale beyond MVP1 but programs that will succeed are those programs which will where you will answer all these tough questions on day zero. The second part of your question was you know what level of technical governance is required I think for AI today technology is there the promise of the technology you know the in all the AI initiatives which we have seen it's worked well I think what's required is the awareness of uncertainty is the human in the loop guardrails what level of overrides you need to make when to make and of course failsafe plans I think the governance aspects if largely these three controls are applied. I think we have a method to the madness in making sure that the programs succeed more so importantly in physical AI.
Yeah. Yeah. Now I think all three of you have a use case with you that you'd like to share. Um maybe we can start with you. Um I think you have something about embedding AI into the physical layer and how it changes the operating model and not just the KPI underlying it. So what do you have for us?
I would um you know to just take on where he left because that adds on to the operating model that you are talking about. Uh I think the one of the big constraining factor on any shop floor is when we start with an assumption that it is a is a smooth flow and everything is streamlined. But the fact is that there are so many different equipment protocols that need to be harmonized with each one having its own heartbeat. That is where scale gets impacted. Therefore the operating model when you're talking of when you're saying you need to change the operating model where the business value will come the real one will comes when you can do let's say an engineer to order or maybe even a configure to order at scale and at the same pace at which you do mass production. When you are able to get to that level of sophistication, that's when your operating model changes. That's the real value of investing big time into physical AI. And it is a little different from the MVP approach where you're actually checking the technical feasibility of what a particular solution stack can do. You actually have to move to an MAP, right? what is the minimum acceptable product for the shop floor supervisor to actually deploy it at scale that will include all the way from software controlled OT up to the harmonization between the human and the robotic systems. So that's where I think the scaled deployment of ETO or CTO will become really the benchmark of success.
Okay. I think you mentioned scale at least three times there. This is obviously important. Mark, when we look at scaling all of this at scale, what would you say is the most underappreciated quality to unlock this transformation?
Well, I I will actually just take a little bit of a step back and and and pick up with with what Nares said because I'm actually a big fan of the P, but I'm a big fan of the PC, I think, was what what you were saying as well in the context of do you have a reference architecture of where you want to go to? Um because PC's can be like your random walk or drunken walk, right? In mathematics, if you're just taking random steps in random directions, you end up walking the drunken walk. But if you have a place that you want to go to that's well defined, then your PC's become your step. They might not be on a straight line there. They might be, you know, but in average, you're going in the right direction. And that's the approach that we're taking for instance with our own factories and some of the things you see on stage. So we are now building autonomous factories in our own factories that need to perform at scale and at cost. So it's not a demo factory, right? Real factory produces our products, but there is a reference architecture. There's an idea of where the factory should evolve to. what are then the proof of concepts that we choose that also like Shavas said has clear business value but then also point in the direction of where we want to go. So that's what I would say as we scale sometimes if you do try to boil the ocean at once it ends up being these five-year projects that where the definition has changed by the time you get in goal. So start small do the PC's make sure they have clear business value but make sure that you have a reference architecture where you're going to so that it all adds up.
Yeah. Okay. Now we're almost out of time but Naresh I'd like to maybe um ask the final question to you and it's about machine learning. So of course machine learning is a big big part of this um and it's going to become an even bigger part when this whole AI thing in factories really takes um takes shape. But where would you say machine learning really shows value already on the shop floor?
I think uh if you ask me the three to five years that we have last three to five years a lot of machine learning work that we did with structured data and now the whole unstructured data with the right level of reasoning being put to use is a right combo to address. But if you talk about just the aspect of machine learning and the successes that we have had predictive maintenance supply chain risk mitigations with the existing data you know talk about shop floor OEE based analytics in terms of improving the OE and the throughput on the shop floor these are all the places where actually machine learning has proved its merit and work quality transformations which are largely not rule based but aspects of datadriven things but I feel in today's world given the maturity of the technology if you have that level of datadriven insights coming in from machine learning and then you back it up with the unstructured reasoning where you have variability uncertainty coupled both of them is the best business outcome that you could get which is end to end not just FYI but also FYIA which is actionable insight at the point of consumption so that your data doesn't become stale by the time somebody acts.
Okay. Now, we have a slide here showing where visitors here to the fair can find us. And also for everyone joining online, if you can make it to Hanover tomorrow, Thursday or Friday, then these are the places to go. Um, so Mark, from a seaman's perspective, we are all around. Uh, obviously it's our booth. TCS, I don't know, do you want to just explain very briefly what we can see here at the booth with you guys?
Yeah. So we have multiple points of presence. Of course we have the TCS pavilion where we are showing the full factory of the future with all the elements coming together. We have a little presence in your pavilion as well where we are showing some of those facets especially in the context of your CPG environment and the bill of material management and the whole idea of industrial autonomy. So we have and we have of course presence with a couple of other our joint partners including Microsoft. So we have the presence there as well and you're going to be on stage a few more times as well your colleagues as well.
So um we're looking forward to that. And um with that we are unfortunately out of time. We have one more thing that we'd like to give you though. I think we have two ah there she is. Sorry. Thank you very much. Always from the right. I know. Thank you. This is something from our customers. Natura. So Vaza and one for you. Thank you. You don't get one, unfortunately. This is um yeah, they have an exhibit here. They're in our CBG showcase, Brazilian cosmetics company. And um yeah, they're very happy to hand this out to our partners here. So thanks again for being here with us. We have a big round of applause for our speakers. Thank you so much. Thank you. Enjoy the rest of the fair. See you soon. Thank you. And with that, I'm going to hand it back over to Christine.
We survived. Thanks, Max. Wonderful. Wonderful. Good morning to you here in Hall 27. How are you doing? Yes, I see smiling faces. I see nodding. You're doing good. You had a great day yesterday at Hanover Messi already. You're excited to see what's going to happen here at our stage in Hall 27. It's the living room of innovation and industries. We do have a lot of customers here on site who tell their success stories. One of them is Natura. What Max mentioned just before they sponsored little treats for our speakers. It's cosmetics which are produced with Seammen's technology coming straight from the Amazon region. why Brazil is the partner country of this year's Hanova Mesa and not for a single reason for many reasons they have been 45 years ago the first ever partner country which is a great statement to come back and showcase what has changed since then and that's a lot of digitalization a lot of energy topics but let's take some insights into what's happening in Brazil and Some facts in the video.
Welcome to Brazilian business. That's what we're going to talk about now. And I don't know how you feel, but I could have one of those coconuts right now and book my flight to Brazil and have a great time. But before I do that, we're going to have four more days here at Hanova Mesa. Customers sharing their success stories. And one of them is Axia and they are Latin America's largest renewable energy generator. And you've just have seen some pictures of their country. It's massive. It's a lot of transmission lines they have to cover and they are committed to sustainability with 100% clean energy. If you've ever been to Brazil, you definitely know that there is a lot of wind. So wind turbines is one of the topics but transmission lines and using that and making it really sustainable that's a whole different story where you have to cater for because modernization efforts have to be taken care of and Brazilian society shall be ensured that they have interrupted services and Axia and Aria is taking care of that. We got two representatives of Axia and Aria here on site and they will tell us more now. Please welcome them with a big round of applause. The vice president of operations and security and COO at Antonia Vareo de Godoy and the CEO for electrification and automation at Seammens in Brazil, Fabio Koga. Big round of applause to the two of them.
Hello. Good morning. Thank you everybody. It's a pleasure being here and talking a little bit about the Brazilian power electric sector. In this sense, I would like to invite Mr. Varjon from Axia. Good morning. Please tell us a little bit about the power electric sector in Brazil and mainly about Axia one of the biggest players in this sector in Brazil.
Okay Fabio but first I would like to thanks Simons for this invitation to present our company and our digitalization program. It is very important for us to present this moment of Axel. Axel has three big business transmission generation and trade energy. It is the largest southern part of the world company in renewable and clean energy. Our energy 100% of them is renewable and it is very important for us and we are the second company renewable and clean energy in the world. In the last year, Koga, we have 6.5 billions of highs in net profit and we distributed the biggest dividends in our since electro was created. Amazing. in the 60 years and now AXIA is a private company and in the past its name is electro now AXA is a corporation company where 60% of our share is from private sector and 40% is for public sector. One thing that it's more important for us is the size of AXA. AXA is in all the states of the country and we have 75,000 kilometers of transmission line. It represents 37 38% of the national grid in Brazil. And we have 32 33% of the hydropower plant generation. It is very important because we are in all the states. We are in 23 of the main seats in the states and we are responsible for attend 80% of the main charge of the company. One point that is very important Koga is in the last year in 2024 we got the best aviability of our history with the aviability of 9.9% in transmission line aviability and in the last year we got 99.96% of the aviability of the transformer capacity it's very important because are the biggest and the best indicators of aviability of the company. Another point that it's very important is almost of our energy is clean renewable but it is sustainable and it is established. It is very important for the system because our energy is hydropower plant and in Brazil in the last 50 five in the five six years we improved very much the distribute generation but in the moment of the day the distribution generation disappear and the gen hydropower plants need to attend the demand in a high velocity. cost and with a sustainable and established generation. It is very important for the country and for our customers. It's really impressive.
Yeah. Uh let's talk about reliability and let's talk about the AXIA challenge. As you said, we know that AXIA has some huge systems, some of them from years ago. How do you see the partnership between Axe and Simmons in sense of digitalization, artificial intelligence, and how can we cope in the future? How tell us a little bit about the partnership and how can you use the Simmons expertise to support AXIA in the upcoming steps.
One point Koga that is very important for our company is we believe in the asset management. We think that this is the way to improve our reliability, our aviability. And for this, it's interesting that we are open to new ideas. And in the last year, we organized two monitor centers in Rio Janeiro, one of asset manage of the equipments, the main equipments and another about the weather. And it is very important to do our target and to develop this step by step. Yesterday we talked about a male project to study the diagnosis of some kind of equipment maybe circuit breakers together ze and ax to detect and to define when can I do the next maintenance when I will have problem with the equipment and to discover to answer this question it is very important that we can use together the AI. interesting you the meeting we have with Mr. K where we can apply exactly the artificial intelligence and digitalizations. Right in this slide we can see how is our asset management tasks. We develop a matrix of criting that we can define the performance of the assets and the risk to the system. With the combination of these variable, we can define how or when will I have to invest in the new equipment, when I need to invest in the maintenance, when I need to invest in monitoring. And with these answers, we can use the equipment as much as possible. And it is possible to define the time the exactly time when I need to do the maintenance. Not a pling maintenance but a condition maintenance.
Perfect. Now let's talk about digitalization process in AXAP. Do you have any aspects to share with us?
Yes, Koga. What happened when we study our system? We detected that 60% of our protect assets are older are in the end of regulatory lifetime and we need to change this. But there are other points. The other point that it is important that we have a change in the regulatory constraints and we need to improve the requirements of our protection. We needed to make headundance in our protect. And another point is as the equipment are old, we need to improve the maintenance and to have more equipment, spare parts of the equipment. With these three points, we decided to develop a new concept of program, a digitalization program of our
substation. And this is the name of PDIG and it is important because it is the beginning in this subject between the partnership in Axia and Zemen.
Zimmens. Perfect. And now maybe you can share a little about the future aspects how Axia is foreseeing the challenge that you have and how Simmons can cope with you to overcome these problems.
The first point is we are trying to attend the new regulatory requirements because we have redundancy in our protect system with the new program. This is the first point. The second point, you are substituting the old copper cables for fiber optics. We are developing which is quite sustainable, right? Yes. More sustainable. And we are developing a program that it's not only a protect, but it's a refurbishment of the protective. It's a new philosophy. And we can see there how was and how will be. And now we have almost 13 contracts in development.
This is the product to select our partnership. It's not only by price but by price, by technical and by experience to do the better for the customer. And this program we can see that we have to invest in 127 new substations with new pro control and protective system for substations for old ones and we think that we planned to invest 22.1 billions of reais not dollars in the next 15 years since 2024 to 2039. This is the idea of the digitalization program that we call PDIG.
Well, amazing. So, there is a lot of opportunities for Simmons and for AXIA to cooperate and I hope so.
Yeah, that's great. Well, we still have one minute. So is there any other aspects you want to share with us about Axia? I think that it's very important that the responsibility that we have with the system because if you have a failure in one of the major cities of the country it will be a problem of access and we are concerned that we have to improve to invest to do more and more better and better our target to availability our assets. And we believe in the asset management. We believe in the digitalization. We believe in the renewable generation.
Okay. So name of Simmons. I would like to thank you very much sharing this huge experience with us. Thank you so much.
A big thanks also from our side here. Great presentation. Thanks so much. And for the case that you want to learn more about Axia and AIA and those solutions, it's right at the corner here on the booth on the left side. This is where you can find the Brazilian corner where there is more than Axia and a lots of customer showcases represented there. Passionate colleagues informing and I see passionate colleagues here with Brazilian flags. Bombia everybody. Always great to have partner countries who are passionate about their topics coming here to our stage. And now ladies and gentlemen, get ready for the power of Latin America. We have representatives coming on stage now talking not just about Brazil. We're going to cover the whole continent. Now in regards of the journey to industrial leadership, they got talent, they got technology and they got AI powering a sustainable industry. Please help me in welcoming the four musketeers representing this topic now and welcome them with a big round of applause. Judith Va, Pablo Faba, Edoardo Gorks and Alejandro Brainval. That's the powerhouse of Latin America journey to digitalization. Thank you, Judy. Hello. Wonderful. Good morning, everybody. The stage is yours, Judies. You're going to host the show.
Well, thank you everybody for joining us today. I mean, who better to talk to about LATAM than these three gentlemen who all represent seammens across that geography that is five times the size of Europe, two times the size of China, and a region that has rich resources, an incredibly talented and well educated population that has an up and coming industry. And anything really that also unites us with values, language here in Europe and soon also by the Merosour agreement which is going to come into interim effect on the 1st of May. Now, Zemens has been in this geography for a long long time, well over 150 years, building the first telegraph line from Rio to Monte Vido. And so we are long-standing partners for the region. And Zemens is really part of the local furniture as much as we are a global enterprise. And I want to be talking to these three gentlemen about what makes this region so unique now. And Eduardo or maybe we start with you.
I would like to say that we share a lot but we also have a lot of differences. But what we do share is that we've been going through changes and volatility for ever since Latin America exists. At the inauguration it was said that the German immigration to Latin America is about 200 years old already, although there were Germans before that. And even is not an exception. It's there and we've learned as a region but also as a company to cope with that changes and volatility to develop a huge resilience and to be able to supply the world with energy, with food, with minerals, and with knowledge the people that are there in a very fast and adaptive way I would say.
Alejandro, what about you?
Well, I'm convinced that Latin America is an awesome region. I would like to ask anybody in the audience from Latin America. Okay. Anybody who wish to be in Latin America. Okay. So, we have 100%. Good. And why is that so? I think Latin America is awesome for many many reasons. One is markets. We have incredible markets, incredible customers in the region starting in the south in South America with mining, with food tech in Brazil, process industries, oil and gas, food tech as well, manufacturing, machine building, and then going up to Mexico with automotive, with the CPG and many others. So it's a very high variety of interesting markets and a high variety of talent looking at people very complimentary talent development in the region. So we as seams do share a lot to develop and serve the customers in a best way. The other thing is openness free trade agreements. So it's a very open region for trade. We have for instance in Mexico more free trade agreements and access to global markets for instance with Europe. We have an agreement with the European Union to Asia to the US proximity to the US. We have in Mexico more than 3,000 kilometers as a border to the US. And that's why we have what we called gloalization which is a key driver for growth. And gloalization is not a mistake. It's global companies operating in the local markets and that's what makes us different.
Pablo, do you want to tell us the Brazilian perspective as an Argentinian?
As an Argentinian. Yes. So I would like to mention about energy transition which is one of the biggest challenges that we have at the moment. Right. And you mentioned about the size of Latin America and can you imagine this size with a big isolation sun very strong sun all over the region. So solar is very productive in our regions but also in terms of biomass we can produce a lot. So and also for minerals, lithium, the reserves of lithium in Bolivia, Chile, I think it's the big part of the reserves from the complete world. So Latin America is an answer for this energy transition and also for bringing this for other countries. Yeah.
Now I want to explore three things with you in the coming few minutes. We'll come back to clean energy because that is obviously a big one that this continent and Brazil in particular stands for well sought after in the rest of the world from a decarbonization perspective as well. I want to talk about the region who's very used to change and volatility and what that does potentially from an innovation perspective. But I'd like to start with you again, Alejandro, and talk about gloalization in the context of resilient supply chains because you know the current conflict in Iran and with Iran and the straight of Formulus is just another example of many in the last few years about the fragility and the vulnerability of supply chains. So tell us about the benefits of gloalization in that context and maybe beyond that.
The key driver to explode gloalization for growth is involving the small and medium-sized enterprises in the supply chains in the region. In Latin America, 99% of the number of companies is small and medium-size enterprises. So as long as we get them up to speed in terms of digitalization adoption, in terms of AI adoption, that's the driver for success. In Latin America, we have 18% of AI adoption in manufacturing industries. And that means one out of five has already AI in an industrial environment. But only 5% of the industrial SMEs have adopted AI. So we have still a big gap, an opportunity for growth all together. And that's what we're showing here in our booth at semens in the Hanover Messe how to leverage them and join them in the digital journey and transformation to adopting AI to make the supply chains stronger.
Thank you Alejandro and Eduardo. I'd like to come to you when it comes to dealing with constant change, dealing with volatility. There are some sectors who've been more prone to that than others and sometimes that actually has a positive impact in terms of innovation. Can you maybe take this one?
You know that there is no market that is as volatile today as the natural gas and oil market. And we were speaking about Latin America supplying energy to the world, which is the case. And oil and gas needs to adapt itself very fast, quickly, and in a sustainable manner too together with the environment, but also the people that work there. So we've been working together with the customers developing digital twin technologies to help them be faster in production. So always the main goal is to get to the first barrel or the first cubic meter as fast as possible to supply the world. But also being better in developing the people, training the people with our technology tools, make them learn about safety also and bringing that in a more sustainable manner using less energy and less resources to produce the same amount of energy. So, Simmons is helping a lot in innovating and co-creating with the customer which is scalable and we can bring that to the rest of the region and the world of course.
So, a heavy industry that is also working on very innovative ways of reinventing itself and that is going to be needed for the energy transition for quite a few more years. But of course the world is talking about green energy first and foremost these days and this is where Brazil is the poster child with more than 90% of renewable energy on the grid. And a lot of sectors now growing as a result of the opportunity that comes with clean energy. Pablo, tell us a little bit more about that and what can the world learn from Brazil?
From Brazil. So from our history, it's a long history already. Brazil started with hydro power plants because it was cheaper for Brazil. Brazil is so competent on that that it was cheaper. But Brazil was very clever in establishing the infrastructure for that and Sims has been a strong part of that all the grids because you can produce energy big power plants in some place but the consumer is somewhere else. And out of this already established infrastructure all over the country allowed later on other energies like wind in the north of Brazil or solar all over different areas to be connected interconnected into this big grid in Brazil. And everybody of you know already that the biomass in Brazil and flex cars. Do you know the flex cars in Brazil? Our cars, my car for instance, the car of my wife, you can put gasoline or ethanol in the same tank in whatever proportion the car is just running. So it's helping a lot in order to go for this transition and semens is helping in all these verticals with our automation systems, with our electrification and also with our digitalization.
Yeah, and I find this fascinating because your electricity grid is basically continental size, right? So for a continent like Europe, there's much to learn from how that is actually done and how it is done with energy supplies that are in fact renewable. Now, I wouldn't be sitting here with my Latam hat, but also my people hat if it wasn't about people as well. So let's talk about the brilliant people of Latin America, highly educated, many young people entering into the labor market. A lot of very well-trained STEM/STEAM people and professionals that are highly needed in the region but maybe are also needed elsewhere with the demographic changes that we have. And at Zimmens, we often talk about resilient supply chains, but we also talk about resilient and relevant people because with the constant change of the world, I think this is a really important part of it. Now, Alejandra, I'd like to give it to you first. Tell us about the role people play for you.
Well, I strongly believe that people is the one most important differentiator of why Latin America has a very strong potential. And why is that so? I would say the Latinos we have a strong sense of resiliency. I mean almost every country has been through several crisis of different areas. So we have been trained already to be resilient and that helps a lot in driving business forward. Agility mindset, growth mindset, a very highly curious population and very young population. And if we take the average population in Latin America is in the low 30s. And if we compare that to developed countries like Germany, Japan or others which is in the very high 40s, that means a very solid foundation for the future. You were saying education. We as Semens are investing heavily in education. We invest in low-income areas in elementary schools in which we have trained more than 1 million kids throughout Latin America through the Seammens Foundation and we continue to do that in high school and universities. We have as a matter of fact in Mexico more STEM graduates every year than Germany for instance which is a very solid foundation for the future. So I strongly believe that the people topic is the single most important aspect why Latin America is unique in the world.
Thank you Alejandro and Eduardo. I'd like to turn it to you next. I often say learning is like going to the gym. You lose muscle tissue pretty quickly if you don't train. I think the same is true for learning. Tell us how you think about that.
And it is very true especially for us. I was talking at the beginning that we are resilient and adapted to changes region and with a young population as you say but still we have the challenge of designing and developing a lifelong learning attitude to stay relevant to develop a sustainable employability along our life. We've been very active in the whole of Latin America in promoting that our people does that. We have more than 1,100 people already learning about AI and data and half of our staff is already active on Semen GPD for example. Trying if I measure myself to that I would say I am not satisfied and I think it's a good thing to keep on thinking what should I do more how should I implement that? Roland said last year that we will be the last generation to have a lead only human teams. And I need we need to develop some agents today. We are also the first generation to lead mixed teams, hybrid teams, which is good. I really think that we still need to do a lot to continue with that. We have achieved a lot also in learning and we have wonderful tools to do that.
Yeah. And I think it's really important to understand that for the very first time in humanity, technology develops faster than typically we learn. So it really is on all of us to make sure that we keep up and that we keep learning. I think it's more important and more critical than ever for people's own sake in terms of remaining relevant but also for companies like us to stay competitive. Now people is maybe about our people at Semens but really success lies in collaboration. That's it. Lies in ecosystems like lies in partnerships. Pablo tell us more about that.
That's a very important point because no one is as good as a team can be and that happens also with partnership. I mean business partnerships of course with our partners that are here but also partnerships with associations, with institutions, with education institution. In Brazil we have a strong partnership with SAI, SAI is the biggest technical school all over Brazil bringing a lot of new talent not only for us but for the industries in Brazil. So that's a very important topic. There's one case here that is partnership together with social engagement. And I challenge all of you or invite all of you to visit us in the Brazilian booth or also in the Natura here in the CPG part. You're going to see how we semens with partners we are engaging in order to improving lives of people that have disadvantages living in the rivers of the Amazon forest extracting good stuffs from the Amazon and how we engage our technology in doing that. So both topics I think in partnership and this social engagement is something that in Latin America I can imagine in all the regions is very strong. We mentioned Alejandra you mentioned already the Simmons Foundation. The Sims foundation is a big way to do that for us to engage particularly in education on young age.
Yeah. So I have visited that booth. You get some nice goggles. You even get some nice smell in terms of the oils that we're extracting or that Lura is extracting in the Amazon in a way that is really very regenerative. Helps the local community, brings skills to the local community as well. So it's one of the cases that I can only double down on. Please please go and visit our booth there. But I would like to leave you with a first impression of Latam as we look at it a very diverse region a region full of opportunities and I think like teams have complimentary skills. I think we also see that in the world and therefore as Zemens we're really all about from a values perspective an open world order where every geography also brings in their strength where we as zemens can bring our technology and partner with the sectors that make up Latin America the well-established ones as well as the new ones that are up and coming and how we want to support that. Now you mentioned the Brazilian booth. Let me also say if you are excited about our technology, please look around. If you are excited about Zmens, we also have a team that is happy to talk to you about all the wonderful opportunities of working at Semens. So any final words of wisdom from you?
Let's avoid football, right?
Yeah. Yeah. Yeah. That's a big one. Soccer.
In a couple of months, we will not be working together. will be working against each other.
Against each other? Yeah.
No, Brazil, Mexico, Argentina. We have a meeting in the US and Mexico.
So, World Cup, we would want to welcome everyone to the World Cup in coming up and looking forward for those interesting games.
Yeah, I wish you all the best. I'm sorry. I feel sorry already for Argentina, Brazil, but um yeah, you're anyway.
So we would welcome anybody. So we're going to have a big big party coming up. Very important event. So happy to see you all there.
And you know that the technology that we are implementing with Pepsico that is also here on the booth can help them bring to the stadium whatever is suitable for the two countries that are playing on a very dynamic and fast way. So yeah, we know what kind of snacks we are going to get at the final. No.
So this is about adaptive just in time flexible. And if you're interested to learn more about that, how it works if you have a temporary factory stood up. That is adaptable to the needs of whoever comes to see the World Cup, that's your booth, right?
Exactly. Good. So with that, thank you. And please go off and explore if you haven't done already. Thank you.
Thanks to the four of you. Yeah, great picture. Then I have one question after you finish with the smiles. Now this is the powerhouse of Latin America. This is Seammens representing Brazil, Argentina, Mexico. That really is amazing in regards of the technology we are catering for. And I have a question to the four of you as a matter of fact as you come frequently to Hanover Messi, especially you with large delegations. What's the thing you're looking most forward to every year when Hanover Messa is on the schedule? Now don't say curry worst honestly innovation technology but what drives me the most is to see a large number of very very curious customers and colleagues here discussing about innovation and driving technology further that's what makes this event very very special and every year there is even more innovation what we are showcasing here so that's always great to come what's your perspective. Eduardo,
Building on Alejandro's comment, conversations. We need a lot more conversations to bring all this wonderful technology to the world and also I'm always looking to what is going to surprise me and I get surprised every year. Mhm.
Yeah. For me, it's the same technology, everything that we are learning here every year, but connecting with people is the main point because can you imagine how many customers from Brazil do we have here? How long would it take for me to visit all of them in Brazil? So, it's there's no other way. So, it's a good moment to be here.
That's a smart idea as well to book a plane, invite all the customers to join you and then you have a long distance flight. So, they can't go anywhere. They are there with you to go in sessions. That's true, Judith. I mean, you're here every year as well.
So, for me personally, it's always a moment of pride to stand here and to look at all of this. It's just an enormous moment of pride. But I believe that energy is a good indicator for what is possible. And I mean, look at the buzz here. I think this is where people exchange. This is where ideas are born. This is where possibility basically takes shape. So, that's what handover for me.
Thank you so much. Big round of applause once more to the four of you. Great session, great insights, and all the best for your future days here. Thank you so much.
And we'll have more customer success stories here on stage. As a matter of fact, you will see Judith one more time at 3:20 on that stage talking with Jariman Love from Natura about how Semens is supporting with sustainability solutions for their productions. At a quarter 12, we have Agaria here on stage and at 1:35 there is CMP with solutions here on stage talking about how Seammen solutions help them. And with that, ladies and gentlemen, are you ready for another session where AI, the digital twins, autonomous production and the solutions which Seammens provides together with a partner and bringing real difference to the shop floor. We're talking about partnerships and ecosystems all the time and in this session it's Nvidia who is supporting us with solutions but not just supporting we are working together we are developing stories and solutions for our customers and sometimes it's even us ourself who is the customer. So from vision to factory floor, this is the title of our next presentation. And I think we're all clear. AI is the buzzword at this fair. And industrial AI is entering the real world. Now we've been talking about virtual worlds, about digital twins. But now AI makes the difference. It's transforming how industries design, produce, and operate. But I'm not the expert. But I'm glad I'm having them coming on stage now. Please help me in welcoming Rhina Brim and Ref Liberadian.
Hi Ref. Hello. Great having you here again. Rhina, a wonderful good morning to the two of you. Wow. Day two at Hanover Mesa, huh? Are you excited or exhausted?
Excited, of course. I mean, look at this.
Yeah. I mean, this is a big bust. I have to introduce for the ones who don't know him yet, Ref Liberian. He is a frequent speaker at our stages. Two years ago you were at Hanover Messi the first time with Seammens and since then you did not miss a chance to present on our stages and talk about the great joint collaborations we have. You're the vice president omniverse and simulation at Nvidia. And we have Reb here with us. He definitely is the one who made digital twins learn how to dance. He knows what factory automation is about and he's the COO of Seammen's Digital Industries and is responsible for our automation business within Seammens CTO as a matter of fact. Excuse me Rhina, but the OO is in there as well. So let me dive in here. Rhina, we have already seen AI applications for quite a while in industry. What fundamentally is different now that makes automotive autonomous production real?
Well, we are handling with AI since decades but in the past AI was very much on the analytic side. So we analyze things or optimize things like quality inspection like that but now we're moving from the analytic phase into the assistant phase. Yesterday launched our engineering agent. Yeah. which is assisting or already acting now and I think now the acting piece comes into it and when we talk about autonomous production AI powered autonomous production the acting piece is a very very important one but acting means really acting in the real environment and that I think is a big game changer which we have working now and will become more and more real.
Mhm. Um ref when we say AI changes things when AI leaves the digital world and it enters the factories with physical AI. What actually do we have different from what it was before?
And essentially what Reiner was saying how we've been using AI up till now is to analyze to help us get insight into information that's naturally digital. The AI that we've had and what we're mostly using today is about the knowledge realm which is encoded in the digital realm. What we're in the process of doing now is taking this capability, taking this intelligence out of the digital realm and giving it a physical form. Giving the AI a body so it can act out in the physical world. This is a really monumental shift because it allows us to apply this computing technology that's on this amazing exponential curve of growth not just to information but to all the things that we do in the physical world. If you look at the global markets about 5 trillion dollars of the global markets are information technology. This is largely where the computing world companies like Nvidia have played in. But as we apply computing to the physical world, the hundred trillion dollars of physical industries can now benefit from the exponential growth of computing, which is a big number. That's for sure.
And when you say it's so easy, we're talking about real big numbers here. So talking about what was possible on the shop floor and what's possible now with all this new technology what wasn't possible before right now
I mean what ref just said there's a 100 trillion market which is somehow in the industry and the question is how you make this 100 trillion market more productive and I think with the way how we and automation was always one main lever to get productivity into factories into industry but the way how you automate comes to a certain limit because currently automation is very much writing your code for the automation controller the PLC where semens is market leader and it's acting but if something comes up which is unknown the machine stops so you cannot automate it anymore or if you have smaller lot sizes or need flexible tasks automation is not good enough from a technology because it's made for mass production so you don't have a good return of invest if you want to automate a lot size of 10 you better do it manual because you know writing a program for a lot size of 10 doesn't really make sense you could do it but bad return of invest so you don't do it but now the question is if we are not having a rule-based automation where I need to tell the system exactly what to do and how to do it we go to a goal-based automation system where I only tell do this and it can be done for a lot size of 10 and this can be done for lot of thousand or lot size of one that makes a big difference. But how you get there that an automation system is smart enough to act flexible on a situation which was not preconsidered when it was engineered up front and now AI comes into play. So if we bring AI out of the digital world in the physical world and we embed it into our machines into robots into automation system then the system gets smart enough. So you don't need to tell the system how to do things. You only tell the system what needs to be done and that what needs to be done is executed by the system autonomously and that's the big shift and if you do this then I strongly believe we're on the next S-curve of productivity and you know productivity on a 100 trillion market that's a big big thing and now we as partner Nvidia and seammens we want to bring this productivity to our customer that they benefit out of that.
Mhm. So when you say tasks which can be done is it like packaging or loading or you have some examples.
If you go to a factory today there are many factory workers and look what they do and exactly they're moving pieces from A to B. They handpack things they pack things and offline. So those aspects or they're doing maybe engineering tasks in the factory a scheduling of what you produce next but those tasks are still be done and the question is can we automate more of those tasks?
Mhm. And it's not just physical tasks also like you mentioned data centers topics like you think of huge number of wires to be handled in data centers. a data center, you know, Nvidia calls it and I like that a data center is an AI factory. So now if you consider data center as a factory, yes, also in a data center absolutely what I was meaning was more kind of I have a I need to produce a batch and now I do a production planning and that production planning currently is also a manual work but that can also be automated in the future by agents for example.
Mhm. Okay. Now next question. What else does it take to move towards an AI powered autonomous production?
Well in order to build these machines that can see that can understand their environment that can operate and act within these environments and do this well and do it safely. We have to create a brain for the robots themselves. The brain has to be capable of making the understanding of the world and making a plan and actually controlling the systems to do that. How we build these brains is actually very similar to how we train humans and animals. When a baby is born into this world, it doesn't know how to see yet. It has to learn. It learns depth perception. It learns color. When the baby doesn't know how to operate its body, it learns how to do that by doing random things, doing some science experiments, breaking things, throwing things until it learns how to pick up a cup and drink properly without breaking things. We train our humans on operating complex machinery like airplanes by first putting them in a simulator. We have them fly for hundreds and thousands of hours in simulation doing situations that are unlikely to happen in the real world but if they do happen are very dangerous situations. So we want them to have experience doing that. So the way we have to train these AIs is essentially by reconstructing the physical world in a simulation where they can go learn how to behave, how to operate, how to understand things in the physical world in a safe place before we deploy them. Now the problem that we have is although we have all of the technologies to simulate many aspects of the physical world, building these simulations, building the worlds that we simulate and making them accurate is actually very human intensive itself, similar to the task we're talking about doing in a factory. And so that's always been prohibitive in terms of the amount of time and cost for us to build a simulation to actually do this. But fortunately, this new technology that we're building, these AIs that can understand the physical world are the same AIs we can use to help us build the simulated worlds. That we then use to go train the AIs themselves to operate out here. So, this is the big big change.
Mhm. I like the comparison to learning how humans learn things, but some learn fast and some never learn at all. So I think the AI definitely is the easier to handle part here, right? In comparison,
There are things that humans are always going to be better at. As far as making value judgments, deciding what is worth doing with our limited resources, what is going to give us the most value. That is something only a human can do. And so we will always be better at it. But there are things that machines, they've been superhuman at doing many things for a long time. We have industrial robots that can lift whole cars and move them. No human could do that. So there's no problem between us working with machines that can do some things better than us.
Mhm. It's still the guts feeling which is something which we as humans have but it's definitely the faster learning aspect with AI. Now in regards of this moving towards an AI powered autonomous production right now what's the seaman's scope in here
I mean building on what ref you know he talked about the brain and the brain need to be trained so I think training the brain we use very much also omniverse from Nvidia to train it but we have as semens comprehensive digital twins so having this kind of information of a machine and the behavior of the machine using this kind of physical data which we already design and using that and using them with omniverse also creating then photorealistic training data sets which you need to train the brain. This is one topic where seamless Nvidia is working very close together. So how we get a very intelligent brain number one. Number two then you have the brain and need to deploy it. It need to run in a real environment because this is now where the brain meets the physics and in the physics semens is providing a lot of automation equipment like our controllers like our PLC. So all the connected automation, the embedded compute, the connectivity to all the different sensors, but also maybe data which are coming out of the process. So our goal is then to take this brain and embed it to make really real world actions. That's the second path we are working together.
Mhm. And where are we at Seammens at this journey looking into our sites and production facilities right now?
Well, I mean that is on the CES in January, we announced we working especially at that area. I mean we're working in a lot of areas together. We're just focusing now on that area. how we really moved to autonomous AI-driven production and we said let's act as a customer zero at semens and we have our factory in Alangan and we want to make them the first AI powered autonomous production and here we're working together to really enable manual to move manual work into automation and this is a highly automated factory which is a world economic digital lighthouse but Still we have a lot of manual work and one topic is and this is very representative for a lot of manufacturing end of line packaging which is normally manual work. So we do it as a customer in this case maybe we have a small video where we show how this is done. So this plastic bag currently a human takes and put in the box and in the future this should be done by a machine. In this case, it are two robot arms. The brain to do this, we train together with Nvidia also using universe and we embed it into the automation system that it can be done. And it's an interesting use case because it's not only doing a one task. You see here the robot arm is correcting something. Something is not in the box. So it's correcting it. So really it understands the situation and is acting accordingly. And that is one of the use cases. And what we do together now we say what are the use cases with the best return of invest and we work on those use cases as customers hero but with a clear goal to scale it globally to our customers.
Mhm. Okay. So what's Nvidia's role in here when we see all that you said you've been working on that together
And Nvidia's position in general in the world is to build computers. We build the most advanced computers to solve the hardest problems. Problems that are practically impossible. We make possible for physical AI and for what we're talking about here. You need three kinds of computers. You need the computer that's actually driving the robot. That's the brain the software that is the brain of the robot runs on. We build those computers and the software stack for that. But before you can use that computer, you need the AI factory that produces the brain. So we also produce that and all the software stacks there. But to use that AI factory, you have to feed it with the raw material into the AI factory, which is the data about the physical world. And for that, we use a simulation computer, the omniverse computer, to generate the data that feeds into the AI factory. We build all three of those. But by itself, this is just raw technology. We can't actually take this to market and make solutions and applications for others. And so our position here with Seammens is to couple our technologies to infuse our technologies into Seammen's offerings to actually make solutions and applications that solve real problems in industry. We do the hard computer science stuff and Seammens maps our computer science and our computers to the physical world solving industries problems together. It's a perfect match.
Perfect match. And it seems you have it under control. But what's the toughest problem you haven't solved yet?
I mean industry shop floor means high reliability. And we don't talk about 80%. We need a close to 100% reliability. It need to be secure. It need to be easy to use also for the operator when it's really running. I think that's a very big element. At the end, we don't do this as a proof of concept. We want to have a return of invest. So, we also need to reach a cost position where it makes sense to put it into place. I think these are the four areas we're working on.
Yeah. And I think at the core of solving all those problems, we need to have more simulation. We need to make it easy to create simulations of all of the things that we're going to deploy to ensure that we build really good AI, physical AI, really good robot brains that actually are robust, that work well in every situation, that are safe and efficient. But to build those simulations has always been too hard. It requires experts that we have very few of in the world to even set up the simulations to begin with. For the first time, we now have a technology that actually removes that bottleneck for us. these AI agents, the coding agents that have arrived that are basically allowing all of us to have these superhuman abilities to develop software are the exact same. It's the same technology we need to create these simulation experts that will help us build the simulations and move forward. So this has been the biggest challenge, but we now have the solution to it and it's time for us to apply it.
Perfect. And since time's almost up, I have one more question to you. Since we're talking about the future and innovation, when we talk about AI, I think thinking in a future of 5 to 10 years and a factory automation, can you even imagine what that's going to look like? We talked about it yesterday with AI things will change in six months tremendously. So what's your envision? I mean ref just said it. I think the possibility of agents and always on agents acting agents is not at all yet arrived here. So if we move that further we really move step to step to AI powered autonomous production and the good thing is every step creating customer value. Not like self-driving cars you either drive it autonomously or not. Every step creates customer value. Agents going to be and the workflow of agents will be the next big step. and your point of view refer.
Yeah, I our factories, our warehouses, the 10 million plus factories in the world are all going to have autonomous systems. We're going to have the factory and the warehouse itself be a robot that's watching all of the robots that are operating inside. We're going to have AMRs moving material around in these factories, arms, humanoids, every form factor of robot you can imagine in 10 years is going to be there. We're going to have AIs dispatching work to other AIs and all of them can embody themselves into physical form at will. It's going to be pretty amazing.
Pretty amazing. and we just are the ones to give commands and book a flight to Brazil and enjoy our time there as we don't have really physical work anymore ourselves to which we have to do we just have to give commands thank you so much the topic of what you have been presenting is at several stations here at the booth but there is one station which you definitely should take a look at because this is an example of where we our customer zero it's in the what we call innovation hub. That's a large area towards your right side. Don't miss the chance to go there. A big thanks to the two of you, Rana and Ref. Always great getting your insights. And we have one gift, but maybe even technology from Nvidia helped us in regards of simulating. Thank you, Cena. Wow. That's from Natura, one of our customers from Brazil. They produce their products with Semen's technology. And this is what we like to give to you. And maybe we get some well machines from Nvidia for simulation maybe next time which we hand out. I don't know but your processing computer processes definitely are hard to hand out. Thank you.
Big round of applause to the two of you. Great session. And with this I'm handing it over to Ramin, my colleague who is moderating our next panel on stage.
So, good morning everyone and a very warm welcome here on our session on industrial AI in the real world. So AI is really changing how we produce, design, commission and servitize our products. So AI has a huge impact on R&D, on production, quality assurance and also on servitization of our products. So you can really say that AI is the enabler for making operations smarter, smoother and much more sustainable. So my name is Ramy Ganipana. I'm hosting here the session today and I'm hosting a session for the set VI. So the set VI if anyone is not aware of what's the set VI it's the German electro and digital industry association. So we are part of that as seammens and with me today I have also three guests with me who are also part of the setvi and who are also part of the working group of services. So therefore warm welcome. So let's have a quick introduction and Michael let's start with you.
Yes. So Dr. Michael Sharp part of Semen's director for goto market for data and AI
Dr. Jawatub u responsible for proprietary AI models for Andreser and driving the strategy in the company
and I'm Lucas Mosko I'm heading the strategy and portfolio management of Bosch connected industry.
Thank you very much. Thanks for the introduction and let's drive right into the topic. So we would like to say or see which kind of impact AI has and we are working along all the different phases from planning and design over engineering commissioning operation optimization up to the serviceization and the modernization. So we will have a look at each of the phases and we want to have very concrete and real use cases how you adopt AI in these kind of phases and what you do there and especially what are the customer benefits in that one. So let's dive right into that. Let's start with planning and design and I'm handing over to my colleague here from Semens. Michael.
Yes, Ramen. Thanks a lot. And as Ramen said, so let's start with the design and planning phase. And um what
We are integrating generative AI into our NX design tool. It functions as a co-pilot, a digital assistant that helps with guidance, instructions, and industry standards. It can generate CAD designs and verify them against guidelines. It's already useful and will evolve further.
Thanks. That was the planning and design phase. Now we're coming to engineering. Michael, once again, how is AI helping in the engineering phase and what customer benefits have you seen?
And also to configure products before commissioning. So Michael, what have you in engineering and how is AI helping there?
Yes. Let's move to automation engineering. We launched our engineering agent yesterday at the Hannover Fair. It moves from co-pilot to digital colleague. You describe what you want, and the agent writes code, documents, verifies, and adapts if needed. It helps customers save time and gives them a return on investment within one to two months.
Thanks. After engineering comes installation and commissioning. So Chawat, which use case have you brought?
Our use case is about compensating pressure sensors. Sensors go through pressure and temperature chambers. We use a neural network to determine parameters from a smaller set of points, solving an underdetermined system. The network predicts parameters and we calculate loss. After training, we can use just one temperature to get high-quality parameters. Also, we have a test later.
Maybe a short one afterwards.
Okay, a short one after that. Listen closely. We'll have a test.
Thank you, Chad.
Now the operation and optimization phase is serious. Lucas, you brought two use cases. What role does AI play there?
First, our shop floor AI agent deals with unexpected equipment downtime. It helps employees report issues in any language, creates high-quality tickets, and provides autonomous solutions. It continuously learns. We estimate cost savings of €85 million per average plant, providing ROI within the first year.
Got it. And the second use case?
This is the smart maintenance agent. It analyzes sensor data, historical performance, and production planning to predict failures and optimize maintenance planning. It allocates resources and initiates work orders, covering the whole plant. Benefits include minimized downtime and maximized asset longevity. Both agents work independently but synergistically, communicating with each other.
So they work complementarily.
Thanks. We have one more case from Michael.
Yes. Our Sensei predictive maintenance platform now includes a co-pilot that gives factory-wide asset transparency. It helps different personas get the right insights in time, reducing unplanned downtime.
That might extend product lifetime, but modernization may be needed. Chavat, what have you done for modernization?
We developed an in-house AI model to predict flooding. Sensors measure level, soil moisture, rain, etc. across a large region. Data is collected in the cloud, and the AI model analyzes signals to predict whether a flood is likely. This is critical for safety and human lives.
Thanks. We went through all phases. Scan the QR code to get the brochure with all use cases. Now, I'd like to ask my panelists: how did you build trust and drive adoption of industrial AI?
Michael?
We ensure users benefit. For example, the co-pilot is used more during night shifts because no one is available. With agents, we free up time from routine tasks so engineers can focus on conceptual work. Acceptance comes when the tool helps.
We focus on use cases with high robustness, like non-language AI models where no double-checking is needed. For example, the compensation use case passes the same tests as the alternative system, so workers trust the numbers.
I agree. We have senior experts now, but many will retire soon. Creating reliable solutions allows us to capture expert knowledge and make it available globally. Also, user experience is crucial for daily use.
Thanks. If you want more information, ask the experts here. Also Max from set VI is available. Visit the Seimens booth, set VI, and Bosch booth. Thanks to my colleagues and participants. Enjoy the fair.
Thanks to Ramen, my host, and our external speakers. Lucas, thank you. Here are gifts from our customer Natura, made with Seimens technology.
And Lucas, thank you for presenting.
Michael, stay with me. A round of applause for our speakers.
Michael, you stay here. This gentleman is so eager he will do two sessions. We'll talk about AI moving from digital to physical. I'm not the expert, but I have Michael Shrup, and now Michael Lou and Kevin Feruzan are joining.
Michael, thank you.
Hello. Take a seat.
Let's rearrange. So, we have Kevin and Michael. Perfect.
Quick introduction: Michael Shrup is Director Go-to-Market Data and AI at Seimens. Michael Lou is Head of Engineering Processes at Andred Metals. Kevin Feruzan is Head of Global Strategy and Partnerships at CSMT. Thank you for being here.
Michael from Seimens, what big changes are happening in AI?
We see two fundamental shifts: from digital to physical world (physical AI), and from co-pilots to agents. Yesterday we announced the IEN engineering agent, which moves us into the agent era. You simply describe what you want, and the agent does the work. It was released after testing with over 100 customers.
When we release to market, we test hardcore. We have two pilot customers here. Michael from Andred, why is the IEN engineering agent important?
We are a world-leading supplier in several industries, performing millions of hours of engineering each year. The scale is huge. We were one of the first pilot customers. We see the transformation from co-pilot to agentic AI. The agent understands our projects contextually. That's a big shift.
Kevin, what have you used the IEN engineering agent for?
CSMT builds production lines for automotive, EV, and new energy. The agent is purpose-built for industrial automation. It understands PLC logic, HMI design, and robotic programming. It saves time in development and supports fault checking during commissioning and after-sales. The proof of concept is our electromechanical braking demo at the booth, co-developed with Seimens. The first version was built in less than two months and shown at CIF in Shanghai. After that, we adapted it for Hannover with added functionality.
Great. Michael, are there any limitations? It seems everyone is happy.
We tested a lot of customers. This is just the beginning of the agent phase. We will continue adding features at a high pace, addressing all aspects our customers face. It starts with contextual understanding, then agentic capabilities, then moving into new verticals. It's exciting.
Michael from Andred, what is your experience?
We've been testing for half a year. We can now use agentic AI to convert functional descriptions into real software code. That's a big shift—before we programmed manually. Other applications include HMI creation and troubleshooting. In the future, deep domain knowledge will be embedded, allowing AI to automatically generate plants based on past projects.
Michael, what differentiates this product from other AI assistants?
It links to our TIA portal, understanding the context of automation engineering. It can incorporate customer and general standards. It's industrial grade, deeply integrated, and includes our automation knowledge guide. That makes it reliable.
Kevin, where do you see the biggest benefits?
First, I want to thank Vasi for highlighting CSMT in the keynote. The biggest benefit is that we can simplify complex production lines. With easy setup without deep expertise in specific domains, we save time and focus on important engineering work. The agent acts as a force multiplier, not a replacement.
Great. Michael, what is the future perspective?
We are at an inflection point. The key is to transfer deep domain knowledge with AI. We are shifting from AI as an advisor to AI doing the work, freeing engineers for high-level thinking. Also, open systems are necessary to interact with different suppliers. Natural language generation of code is a major enabler.
So, exciting times. Michael from Seimens, what is your vision?
We will continue working on the product. The field is evolving fast. We want to extend seamless collaboration between suppliers and customers. We aim to cover the entire value chain—design, simulation, operations—with agents. That will fundamentally change how we design and operate products.
I feel your passion. Is there a QR code for more info?
No, but you can see the black machine—the CSMT demo. Kevin is there. I'm around the booth and at the engineering agent station.
We are also moving into humanoid and embodied robotics. The next six months will see productization and market entry. Come see the big black box.
CSMT commercial break. Thanks, Kevin. Good luck. We have gifts from Natura. Handing over to Max.
Thank you, Christine. Time for a breather. Next, we'll hear how Seimens and Alfen help Dutch grid operator Nexus strengthen the power grid through standardized pre-assembled medium voltage installations. That approach enables scaling from 10 to 100 stations per year. After this video.
Our power grid is under pressure. Consumption rises, but expansion lags. The power grid needs to be rapidly strengthened. Previously, each substation was individually designed. That limited Nexus to 10 per year. Now they need 120 per year. With partners like Seimens, they are developing standardized and prefab building concepts to accelerate grid extension.
Ladies and gentlemen, please welcome Michael and Joanne.
Joan, welcome. Michael, nice to have you. Take a seat. Michael, you are CEO of Alfen. Joanne, you are Managing Director of Smart Infrastructure at Seimens Netherlands. Thank you for coming from the Netherlands.
Joanne, what challenge do you have in the Netherlands?
Our grid used to be yellow, now it's completely red. No capacity left. Many people want battery systems, solar panels, wind farms. Industry cannot expand, houses cannot be built. It's a huge problem.
Michael, how does your solution help?
We cooperate with Seimens and Nexus to standardize installations. We have a prefab concrete base and standardized components. It's easier to install and maintain. Previously 10 stations a year; now we can do about 130. As EPC contractor, we provide turnkey delivery.
What does prefabrication mean for organizations?
You don't have to do work on site, not dependent on weather. It speeds up. It's like Lego blocks.
We used to engineer 50 different stations. That took a lot of time and engineers are scarce. Now we only need five types. Also, commissioning engineers can work in our factory in Zutmir instead of traveling, so they can be home at night.
Don't try to solve grid congestion alone. Cooperation is key. Even with competitors, we share the concrete base design. Letting go of the fear of competition is important. Also, structural issues like groundwaterproofing and cable throughputs are worked out in detail.
Insulated switch gear that we have. So we call it the NXC plus 24 blue gas insulated switch gear and it's very important that that all fitted together. So on one hand you had your European tender with us with the guys like ABB and Schneider and on the other hand you had the European tender with the people like Alvin and that had to be fitted together and that's a challenge of course.
But because we are in close collaboration also for all kinds of other solutions. This was something that we could actually suggest to inexis. Well, look at it from a different way and we can solve your problem. To increase a tenfold, it's incredible. It sounds easy, but just think about it. 10 times as many substations from 10 to 100 a year.
And just coming back to you mentioned blue GIS or blue gist g. It depends on where you are I assume how you say it. We have that here at the booth as well. Could you just very briefly Why is it called that? Because it uses clean air, right?
It's Well, it uses gas. So, it's not a clean air solution because that's an air solution and an air solution has a disadvantage that you still need to maintain it. This one was will be closed in the factory and that's it and it will run for 30 years and you do not need people and you do not need it for the station either that come and have to maintain it. That saves so much again personnel which we do not have in the Netherlands. So that's a very big advantage and it's completely safe. No flu or gas anymore in there.
Yeah. Did you just say 30 years? So that's what we can call maintenance-free. 30 years I assume. Yeah. Okay. Okay.
About 30 years more but the tests do not go further than that.
And finally then uh again for both of you may maybe Joan you can go first. So we've spoken or we talked at the beginning about this being a very Netherlands specific issue. Yeah. All the all the yellow and red everywhere. Um so is this solution now only something for the Netherlands or can it be used wherever?
Yeah, I think it could be used everywhere. But I think what I want to emphasize is that in the Netherlands we used to have far more an ecosystem. So all on our own and we are the best and we have the best products and we're now after an ecosystem. Okay. completely the opposite where we work together. So I think it could be used everywhere, doesn't it, Michael?
Well, absolutely. So that you know from Alpha point of view, we're very proud that it's made in Europe by Europe and well for Europe, but even for global customers because it's a solution that can be copied and pasted everywhere. Mhm. Mhm. So, anybody that's interested out there, we're willing to help you wherever you are in Europe.
Well, that that is great to hear. Um, and then as we approach the end, um, maybe Michael, quick question to you. Are you going to be around a bit longer for the audience to talk to you in case they have some more questions?
Absolutely. I'll be here for another couple of hours.
Okay. And anywhere at the booth or a particular place you're going to be?
Particularly close to the coffee. But ah yes, I I thought you would say that. Yeah, but the coming hour we have to go somewhere else. So just after 1:00 we will be here on the booth.
Okay. So So you as well. Yeah. You'll be you'll be in and around. Okay. And um with that matching the color of your top um but it's not for you unfortunately. Thank you very much. This is a a special gift from Natura, our customer in our CPG showcase. They are handing this out to all of our external speakers. So you have a um a cosmetics kit in there from the tool. Thank you very much. And no, not cosmetics. Um and it's basically demonstrating our partner ecosystem, our customers all working together. So thank you so much for joining us here today, Joan, and for bringing along Michael. Thank you so much. It's been a pleasure. Can we have a big round of applause, please, for our guest? Thank you so much. Enjoy the rest of the fair.
And now we are moving on to the next presentation of today. And um we talked a bit about electrical infrastructure there and we're going to keep that conversation going because um yeah the future of industry is of course not going to be built entirely from scratch. It's going to be upgraded as we go. And up next, we're going to discover how AI is um transforming existing power and industrial infrastructure into predictive high performance systems. And for this, please welcome on stage our global service manager for electrification and automation, Amadep Singh Rana. Welcome.
Hello everybody. Before I start talking about AI, I want to show you all a very simple thing. I have a pen in my hand and if I drop it, it goes down. It's not any AI model that does it. It's the gravity at work. So there are fundamental laws that we have to deal with in the physical world. This is also true for our electrification infrastructure. AI has to operate within the guardrails of this system. AI cannot hallucinate. We all know AI is moving at an extraordinary speed, but in the industry, customers ask when does it become real? In our world, you will not get the answer on how to improve electrification by a chatbot. It is found where the power must flow reliably and safely. AI cannot invent a condition or recommendation not grounded in system reality. Our customers face pressures: distribution utilities need transparency and capacity at scale, data centers have cost of downtime, EVs create congestion, heavy industries need to decarbonize with aging infrastructure. We cannot solve by adding new copper and steel alone; we need to get more performance from existing systems. AI is enabled by electrification, but AI can also help electrification become smarter. For industrial electrical grids, AI is grounded in three things: physical assets, contextual operating data, and laws of physics. AI must know real equipment. Data should be seen in context. Domain knowhow is the real differentiator. The breakthrough will come in five layers: sensing, interpreting, predicting, optimizing, and augmenting. From visibility to insight to foresight to optimization to augmentation. AI will scale in layers. Examples: grid operations for anomaly detection and switching recommendations, and asset management for offline assets using multimodal analysis. AI will unlock megawatts of existing infrastructure. I invite you to innovation hub at booth 67 for a live demonstration of a domain-tuned model on asset management. My colleague Nishe will be there. Thank you.
Thank you so much, Aman. I have something for you from Natura. Look at that. A bit heavy. I'll take that off you. Thank you. Well deserved. So, we're almost ready for our next session. Um, just quickly though, in case you've just joined us, you've taken all the seats. That's good. Come closer. We don't bite. So, Ammon was just speaking about the innovation hub where you can find his solutions. And by the way, I hope you remembered his quote. Maybe one day we'll be able to quote him on that. He was speaking about innovation hub. That's just one of the three big sections that we have here at the booth. So on the other side we have our digital enterprise showcase for the consumer packaged goods industry. CPG end to end from data collection all the way through to when the product hits the supermarket shelf. And in the center you have our technology deep dive area with our digital thread approach. This is how we are approaching customer challenges. We are helping them along the entire digital transformation journey in electrification and buildings, smart manufacturing, advanced machine engineering and accelerated product development. So gather around, listen to our presentations, listen to our panels, make yourselves at home and find out how we are realizing tomorrow today. And with that, I'm just checking. Militia, are you ready?
Awesome. Then I'm going to hand it over to you. All the best.
Hey everyone, how are you doing? How are you doing? Are we doing good? Can we say you? That's cool. Well, you know what? I'm going to say you because it's all about partnerships here. And right now we're talking about our partner Agraria and that's what Zemens does. We collaborate. And I'm very excited specifically for this session because our partner country is Brazil. And the fact remains we're at the world's largest stage of industrial automation and transformation. But today instead of talking about technology in an abstract way we want to talk about people about production but also what that really means for us as we move on and we also want to welcome our speakers Philillip head of sales electrical products at Zemens, manager automation manager at Agraria, and then we have Ricardo. Welcome. Great to have you guys. So, let's not dance. Let's get comfy. Please take your seats. Wonderful. So, ah, that's comfy. It's my first seat today on a proper sofa. How's the lounge? You like it? Lounge is good. Yeah, it's comfy, huh? It's great to have it. Yes. Yeah. Mik, you got to go a little back. I can't see you, Ricardo. Yes. There we go. Ah, yeah. Now I'm liking it. Okay. So, how can we build automation expertise from the bottom up? Do you think it's possible? Well, yeah. I think it's possible. Ricardo, what do you say?
I think it's possible. We made it possible. Yeah.
I think Philip is going to explain to us the concept. Yeah, I mean at the end the answer is simple. Yeah, that transparency at every level is key. Yeah. And before we start uh I'm happy that uh Mikail made it because you had a bit of a trouble with the airplane yesterday. So what a bit of a smoke. So great to have you here. And to talk about the great um use case we have developed together. At the end of the day, it was all about uh datadriven uh modernization uh and what we experienced. And you said that you've been here a year or you couldn't make it last year because we were just starting to exchange on what Agraria's ideas are and to build a bit of uh collaboration uh a way forward. So it was not just about the technology and as you mentioned already it's also about the people the philosophy to understand the different pain points. Yeah. What we have it was also for Igraria a new way forward for the transformation. Yeah. So it was a key as you said. Yeah. It's all about the people to talk to exchange to learn from each other you know and then um at the end it's a step-by-step approach. It's about the people who also change a bit of the mindset what we're going to do with all the data and it was at the end of the day a foundation for AI to predict and prevent problems before they even occur. And this is where we are happy to have that case now because as uh Ricardo will also say later, it also helps us not only in Brazil but everywhere else in the world to develop on this based on what we've learned on this case.
Yeah. Yeah. So um we're talking a lot about collaboration here but let's talk about foundation and scale right. So maybe about you tomorrow is a very special day. I just before I dig into this here in Germany we drink a lot of beer. Do you know what day tomorrow is? Yeah, I think I heard about that tomorrow is the beer beer day in. Yeah. It's a coincidence. And you know what? We're all going to drink a beer then later. I think it's a nice idea. But anyway, you really have a deep connection to Germany. Am I right? So um and when we look at the beer production, can you briefly tell us about the foundation of Agaria and the scale of your operation today?
Okay. Uh first of all, thank you Micia (Militia). Uh it's a great honor for me to be here representing Agaria, represent Brazil and our community, Andrew Hills. And I will talk about uh our foundation. Agara was founded in 1951 by 500 families that come from the south of Europe. Uh and have we have the Nibio the Nubio rivers and they call the Nuben Swabian and they have German ancestry. Then with their hard work they created a larger turnhouse 8.1 billion Brazilian reals. It's approximately 1.38 billion euros. And today we have 2,000 employees. It's the biggest agro industry in our city and we have some silos with beautiful paintings. We work with seeds and animal food and we have also an oil industry. Our first industry with automation started 30 years ago using S5. We have a large-scale production of malt and 30% of the market. There is a joke in Brazil: if you drink a beer, one in three chance that the malt comes from Agaria.
I think the... And you're in Bavaria. So it's not a niche operation. It's industrial scale but with responsibility. Mika and maybe you can just add on as well Ricardo a success at scale brings complexity. Now let's talk about the plant floor challenges.
We have a lot of challenge and the first one is the space and our cabinet is really old. Then we have to reach at the limit and we have a problem with safety. In the image we have old technology with no transparency information. Every time we need to go to MCC's to understand a fault. We need to find a solution that gives us space. We use other technology but we think about more steps with data. Then we called Siemens to help us understand the best solution.
The pain was clear when we got to Agaria. They were running critical loads with obsolete hardware, no data visibility, manual resets. They wanted a solution. We worked together and our solution came with a new product, an intelligent link module you can see at the booth. At the time it was a very new product with no references. It was important to have Agaria as the first big project reference. About 300 starters. Everything is digital with all information in the control room. It opened doors for us in Brazil, United States, everywhere. That's why we are here and proud.
And I love that you brought it. We can touch it. This is the real stuff. It's not abstract. This is a success story. Now, I'm going to hope I get this right. 3RC7. Yes, wow. How did it help Siemens and Agaria break through the physical and digital ceiling?
With this solution, it helped Agraria first with their needs. They needed remote from the control room, not having someone at the panels in the danger zone doing remote resets. We solved with this product, but we brought much more information. We brought transparency about motor data, currents, overloads, motor condition that they did not have. This opened new possibilities for Agraria to integrate this information and have better understanding of their manufacturing.
Today with the solution of 3RC7 we can understand more about our process than we could with the old factory. With this new technology we can see the process in a different way. I can cross data and understand how the flow rate of the current of the motors are running. It's a change of mindset and really interesting to use this technology.
Yeah, this is the main point. There's so much data out there and having that transparency in this little compact thing is proving what's going on. That almost deserves a round of applause but let's move on. Mika, you mentioned what's changed. We found a new solution and it's in place. But looking ahead, let's have a vision about AI-driven mill. Where does the partnership go next? Ricardo, you start.
This device gave us the data. And now Agaria is building the brain. They are building a data historian. They want to record 100% of process data and use AI to correlate data from motors with process data to prevent failures not seen yet. That is where we want to go. Mika already has an example to share.
In the image we are storing data about the current of the motors. This is the way you can see the process in a different way. Now with this data we can analyze failures we have in the plant. We have a fault in the motor and we can understand how it works before going to the field. It's a new way to understand our own process.
Looking at the last two minutes, you have a booth here. For the audience, what do you want to share for their journey? I'll start with you, Ricardo.
We strive to be the technology partner of our customers. So if you have a problem or challenge, let's work together. Let Siemens work with you to find a solution. It's possible. Agaria opened the door for us.
My final message: never be afraid to do something new. Important that we can change ideas into reality. This is our goal.
Wonderful. And Philip, I will never forget you. Can you tell us why this is relevant for other customers? It's a blueprint for transformation. It's about trusting and developing together. I want to highlight Pablo who is the brain behind the product. At the end of the day, it's key that we put all our strengths together. From Agaria as a customer, be open to each other. We are proud to go this journey. My key takeaway: never fear to try out something new. Thank you gentlemen.
Thank you very much. We have some presents waiting for you as well. So, let's go. And as we move on, my colleague Christine in a lovely pink outfit is going to take over.
Thank you, Militia. Pink is because of the energy I tried to bring onto the stage. And I thought that's maybe some symbol of what's going to happen. Ladies and gentlemen, how are you doing? Hello, everybody awake? We're going to do it one more time. Hey, and there are free seats. So if you want to make the most out of Hannover Messe, first of all, you're in hall 27. Second, you're on the Siemens booth. If you want to know anything about innovation, that's the right place. Hello, LinkedIn live. It's not LinkedIn live, that's later at 15:20. YouTube is there and those watching from home. We're going to dive deep into low volume to high speed manufacturing. Hanov Messe is all about automation but AI has entered. We'll talk about scaling virtual PLC applications. It's about how software-defined automation enables flexible, scalable, and efficient large-scale manufacturing. Please help me in welcoming the two experts: a big round of applause to Peter Steel from Audi and Aniimar from Siemens.
Hi Peter. Hi great you're joining here and Anarie great you're there. Welcome to the sofa of wisdom. How are you? Perfect. I like the atmosphere of the booth. It's buzzing here. Hannover Messe is always special. I'm vice president for data and technology-driven production and supply chain at Audi. Actually when I started as trainee with Audi 30 years ago, the first friend I made was from Siemens. Today we're together talking about collaboration of Siemens and Foxwang and Audi.
You have a lot of expertise in digitalization and you're also a mother of two. Absolutely. I need to find some robots to bring home. Now, why did Audi question existing automation concepts? Peter, I'm going to start with you.
Audi itself is a line builder. We have innovation cycles for a car seven years, infrastructure 15 years, but software industry one year. That was one driver to get into faster loops. The other thing is virtualization. 10 years ago we spent double-digit millions in PCs and Windows software which are slow. So this was key to start with virtualization.
If we think about the digital factory of the future, a lot of things are not possible with hardware fixed installations. What we're doing here is future-proof, where we can add innovations.
How did you take virtual PLC from concept to high volume production? What does high volume mean? For us 30 to 200 cars per day small production, 500 to 1,000 is mass production.
Everything innovation starts with a vision, bold crazy guys. Then lab in 2018/2019. Then small production. We proved that VPLCs work in small series. Next step is mass production. That's what we aim for.
And that's why we're looking for bold innovations. It's not just a test. It's a trust thing. You need partners you can trust who share the same vision.
So Animari, with automation background, what strategic role does software-defined automation play?
We believe the production of the future will be fully autonomous, self-optimizing. Technologies like AI allow us to change from fixed task-oriented automation to goal-oriented AI-assisted production. Self-optimizing systems are not possible in hardware-centric architectures. We need the principles of software-defined automation with flexibility, openness, scalability. The virtual PLC is one important building block. It allows deploying, updating, and optimizing control logic at the speed of software. Software-defined automation is to autonomous production what an operating system is to a smartphone.
Great insights. Peter, what worked and which lessons were crucial in large-scale deployment?
We did a revolution in an industry with this maturity. You need a team of collaboration inside our house of hardware and software people and with innovation partners. You will always face roadblocks. You have to react fast, get into loops of try, fail, learn. Collaboration, trust, a team that will go through hard work for years. It only worked with commitment and expertise.
Which added value does virtual PLC deliver in daily production, Animari?
Speed and flexibility. With VPLC, scalability: computing resources scale instantly. Flexibility: product handovers happen in software, not hardware. Speed: commissioning time reduced, updates rolled out instantly. Greater time to market and competitiveness.
The goal is to reach old KPIs: reliability, investment. We proved it's working. Now we can add value.
What are Audi's plans for the future?
We have many factories. We're not doing lighthouse projects just for marketing. The spirit is to do something bold and scale it. Next steps: horizontal expansion to each new car product and factory, and vertical expansion to add values. We need building blocks. We have some. We will scale and add innovations. That's how we bring iconic cars like the Audi RS e-tron GT to life.
Which is a nice one. I'm going to unveil where you can see that car. Animari, what are next development steps?
We want to set the next standard in automation along four core elements: connected hardware, edge ecosystem, runtime platform, and AI integration. If we do that well with partners like Audi, we will build the next generation of automation, just as Siemens set the standard with TIA and Simatic for the past 30 years.
Here we come together again when 30 years ago the Siemens friend you made in China came to you. We talked about that car. You can see it outside hall 25. The picture outside was taken yesterday. If you need a test driver, I volunteer. Thank you for your insights. Thanks for having me. Thank you. We have a little gift from Natura. Thank you so much. Big round of applause. And with this, I am handing it over to the next session.
In the shortest time in the next panel up on stage here. It's Accenture who is the partner we are happily working together with. And I am proud to introduce the three ambassadors of that topic. It's AI which is shaping the future of autonomous operations. Please welcome on stage Dr. Ronny Henry, Dr. Ralph Vagnner and Vlad Larich. Welcome.
Hi there, welcome to our session regarding AI from Accenture and Siemens together. Looking back at the last Hannover Fair, AI has changed tremendously, at a speed we didn't encounter or envision. Today, agentic AI systems are not just answering questions, but they are planning, checking, and acting. What was so tremendous is looking at OpenClaw, one of those orchestration systems, 6 months ago started and already has more than React or even Linux on GitHub as a favorite. I think decades of communities have not much more favorites than OpenClaw only after 6 months. Every major player right now has an agentic AI system which has a multi-step approach with little humans in the loop, if at all. When you look at our innovation area, you will see how Siemens and visions at Gen AI moving from a chatbot into a coworker on the shop floor. This is what we want to talk about today in our session. So let's get started on this discussion.
Ralph, looking about what we are doing with agentic AI and what we are doing with especially autonomous operation when it comes to manufacturing, what do you think about autonomous operation, what are your ideas and where do you think are we going and heading?
So in manufacturing production, when we talk about autonomous operations, we actually mean an AI-centric system with agents which increase the independency and reduce the manual interference of humans in the production process overall. So many companies see this as part of their digital transformation and this is actually the right way of looking at it. So they implement SCADA systems, they implement MES, they implement IoT, they implement PLM system, which is a very good foundational starting point to get actually to what we call autonomous operations because then the next step, and this is what we will talk in the next 10 minutes or so a little bit more, is you need to add context to that data systems because system data sitting in MES and IoT and PLM are typically still a little bit siloed today. So an industrial knowledge graph helps you to get these data together and contextualize those. And if you then add an agentic system on top of it which actually can utilize the capabilities of MES, IoT, M systems as well as being guided through the relationships that is being created between these data silos with an industrial knowledge graph, then you have enough kind of power put together which then gets you to something which is close to autonomous production where you can reduce actually the human interference stepwise over time.
And if we think about this in that way, Vlad, where do you think we are today and how long does it take us to get there?
Yes. So what we see today is that manufacturers are already applying AI in the vertical solutions. So we have powerful device solution to use it in planning, for example, in solutioning of the products itself, in maintenance. But what we are missing in many cases is a cross-solution integration, this horizontal integration. So many manufacturers are optimizing the steps but they miss opportunity to optimize the end-to-end workflows. And it's not a question of the technology in the first place. It's about the scalable application of the framework behind and a red data foundation. I frequently compare it to autonomous driving, where a lot of value can actually be created without full autonomy, but it's important to apply it in the areas where AI already today can deliver cost improvements and efficiency.
So having heard all of this, Ronnie, Siemens is a company with many, many factories around the world. Does Siemens apply these kind of approaches what we heard from Vlad and the definition of what autonomous operation means? Do we apply this in Siemens' factories?
Sure. I think Roland Busch and Jensen Huang announced one of our cooperations, the factory in Erlangen, the electronic factory Erlangen, with our partnership with Nvidia for example. We are right now investigating how we can use physical AI and also agentic AI use cases on the shop floor in the factory on operations level. Depending on the urgency of the customers right now, as our internal customers also, we treat our factories as customers. And together with Nvidia, we are evaluating right now different use cases. By the way, some of those are inspiring also our areas here. So when you look at the innovation hub area, there are two robots doing bin picking, flexible grasping, and this is a wave three use case. So we use VLMA, visual language action models, that's the buzz word right now, where we have a system which is not trained on every step but is really trying to identify what do I need to grasp. This is for picking stuff and putting into boxes. This is what we are evaluating there for example. Another one also in Erlangen, right now we are investigating something we call autonomous production planning, where it's really about rescheduling and reacting to changes in material flow. So Erlangen is a high variance, high frequency factory, which means we have 1,000 different products, you have 24,000 materials. Imagine when one part is missing of a truck reaching to the factory, what are you doing then? How can you reschedule, how can you adapt? This is also inspiring our work with Accenture for example also in the innovation hub. In the middle, when you go there, you will see a flexible production use case where we discussed also not about how Siemens is doing it in orchestration of multiple agents coming together, but also we know that our customers are not Siemens shops from end to end, so how can Accenture come here support the orchestration of orchestration systems for example in this factory. And third but not least, when you look there with agents right now, Pepsi, we are doing agents are supporting there with the pop-up factories. So imagine the soccer World Cup right now, you have different kinds of teams coming together, semi-finals, finals, we don't know, Pepsi does not know where to do so, the Gatorade production agents will support there and help what kind of Gatorade product do I need then on the different sites.
Yeah. Great. Ronny, thank you very much. I believe also this is the reason why people join sessions like this. To see the real life examples how AI can be applied today. We see many cases today. Right. Maybe you could share one example where AI today already on concrete example can help to get insights faster, to be more efficient, and came faster to right actions.
So let's get to a very concrete example down to the machine level. Imagine an injection molding machine producing certain plastic parts and an agent is detecting that there is variations in quality because of difference in size. You want to have constant size and not sometimes smaller, sometimes bigger. So the agent is detecting and then another agent is being informed to do a root cause analysis looking into the contextualized industrial data graph and they find out that there is a correlation between the quality index and the material viscosity. So the material viscosity is measured from the plastic pellet that goes into injection molding machine. So if this is varying then it has an impact on the quality. So the agent goes further into the industrial knowledge graph and finds out there is a correlation between the viscosity and the age of the plastic pellet. Oh okay, this is maybe something we can do about it. So the agent comes up with a proposal which says if you actually introduce a FIFO system for the consumption of the plastic pellet which ensures that you always use the newest one first, you have the lowest fluctuation in viscosity and you get your quality index back. So this is what you get out of the context of the industrial data coming from the different systems we mentioned before. Now that recommendation the agent gives back to the operator of the injection molding machine and that this is something you should actually apply in order to get your quality fluctuation back to a high level again with good quality. So this is a workflow that can be done by a human, can be done in daily standups, but in the future and today can be done already by an agentic system in a way that we described before.
Now that was a good example. We talked now a lot about features and the things we need to change, but agentic AI will also bring a lot of culture change and organizational change. So with you, Accenture has a lot of consulting and interactions. What is the transformation challenge and ideas you see, Vlad?
I think the transformation is exceptional because we have a classical three-body problem we're dealing here with. Technology, people, and processes behind. The first one, AI is not just a technology because it's already today directly impacts the decision making process and the business core processes of the whole organization. So compared to other technologies, it's not a machine learning topic anymore. It's also not IT topic. It's actually core part of the transformational strategy of each organization. Second, what we see is that you cannot just layer AI on broken processes and fragmented data. You will still have the same fragmentation just with another tool. And it takes time for organization to adapt and redefine these processes so they are AI ready. And as you just recently mentioned, to make also the data contextualized and ready for AI to consume. And the third component what we see a lot at the moment is that AI today which we use is a non-deterministic tool and it's quite new technology to use in our highly structured industrial processes. So people need time to trust this new solutions, adapt, and find a way how to use it in the right manner. But what we also see a lot at the moment, once they see AI giving value to their daily work, once they see this value created, they actually adopt it and organization can move forward together.
Very good. Looking at the time, maybe you have a little bit of an insight about traps and misconceptions down the line. What did you something accomplish?
There is actually many, many misconceptions out there in the market and with customers when it comes to AI and autonomous production. Let me give you three of them. The first one is AI is a magic box. You switch it on and it works. This is certainly not true. We talked about that you need to have a solid foundation. You have to have a digital transformation program running and then you put other building blocks and expand those capabilities. It's not a switch on and it works. Second misconception is once you have it, you deployed it, then you can let it run forever. Also not true. AI has different challenges. So you don't need to worry about maybe the last security fix. But an AI model has drift. This is something completely new which we haven't experienced in the past. This is something that came with AI. So you need to monitor model drift. You need to bring a model back. If you have a model which is self-improving, you need to evaluate and monitor that new model with the new capabilities that you still have the quality and the replies that you expect from that. So there's different things which you need to do once you run a deployed model. And last but not least, third misconception I want to share with you is just throw more data in AI and it becomes better. It's also not true. It's about the quality of the data and even more important as we iterated several times about the context of the data to give the AI as much structure and guardrails as possible. So it's about having the right expectation, build a solid foundation, and then have a human-AI centric combined approach.
And talking about time, we thank you very much for listening to our conversation here. Let's see what is happening until next year in AI. I mean, looking at the time and for you, I think the time is to act now to start this journey because we think it's really the when you need a signal, this was it, just start with AI. Thank you as much.
Thank you very much gentlemen. Something from our customer Natura for you. Nice things from Brazil in there and Cena has something for you too as well. Oh, two nice bars of soap also from Natura who you'll be able to find in our CPG area. So, thanks again. Enjoy the rest of the fair. Bye-bye.
From one partner session to the next, our ecosystem is big. It's getting bigger. It's evolving all the time. Today's manufacturing landscape demands a holistic approach to operational excellence where every investment contributes to long-term value. And in this next session, we're going to explore how Siemens Op Center is redefining manufacturing operations management with the help of a strategic shift to the cloud. You will hear how this transition is not merely a technological upgrade but a powerful lever for substantial total cost of ownership reduction, optimizing resource utilization and enhancing operational agility. So for this please welcome the three guests. We have SVP for manufacturing operations management at Siemens Tobias Lang, head of business manufacturing operations software also from Siemens Metin Kaplan, and from Deloitte Consulting US smart manufacturing leader Tim Gaus. Welcome gentlemen.
All right. Pleasure to have you here, Tim Min. We are here to talk about MES modernization. It's really in everybody's mind nowadays. What do we need to do to take the MES, the manufacturing execution systems, to the next level? How can you modernize it in a way that is compatible with your existing infrastructure, with your existing processes, with your existing factories? We as Siemens, we see a lot of trends around this regarding building out more modular systems, more systems that you can configure, that you can scale up. That's one of the reasons why we invested so heavily throughout the last years in the Op Center portfolio where we really build out a modular approach that is cloud native, cloud first but not cloud only. That's something we believe strongly in and we believe in the end-to-end story of not just the MES space but really the manufacturing operations management extending to spaces like planning and scheduling, like quality, like the intra-plant logistics, but also linking to things like the PLM systems Teamcenter, the IoT and the shop floor. But that's our point of view. So Tim, I'm eager to hear what you hear from the industry. What is the things that the market has a point of view that maybe hasn't been the case? Because I mean you've been in the manufacturing space for decades. What's the thing that you're seeing?
Well, you're making me feel old here. MES has always been a part of our manufacturing fabric, but now it's seen as truly the driver of transformation. Like that is a mindset shift that our clients seeing cross industry and is really underpinning a lot of like the AI things we were just hearing about because you need that core manufacturing data in a staged way. To your point, that modular architecture is not even an option, it's a requirement these days to get to the scalability you have across globe. And quite frankly, a lot of us manufacturing folks are pretty uncomfortable with the idea of cloud on the shop floor. That's shifting. We understand and we see folks not asking for it but actually expecting it as a delivery mechanism. So core manufacturing transformation, cloud enabled, not cloud dependent, but definitely as an option.
All right, that's really reassuring because that's the things we are hearing as well. But maybe, Metin, you want to share a little bit about where you see the trends moving in the next 3 to 5 years from now.
Yeah. MES is system of records and it's moving to system of intelligence. So what I mean with that is MES is moving towards a system that can actually predict manufacturing bottlenecks, prescribe optimal process parameters set in real time, and even self-correct to improve quality and efficiency. And AI is moving. I mean we have also a lot of new things coming up. I mean AI based visual inspection is doing a lot better than human beings. And also AI based predictive maintenance to minimize the downtime. And one more thing, there's edge computing is getting stronger and stronger and AI in real time close to the machine is actually really enabling the closed loop manufacturing that we are supporting with Op Center, but more of a closed loop manufacturing that can actually adapt and learn by its own. So I think these things are coming up.
That's absolutely reasoning and I like the thought of having more than just the workers' eyes on the quality of the product and really utilizing AI for doing that. That's absolutely a trend we're seeing and absolutely a trend that the industry is going. But talking about visuals, Tim, I see a lot of things regarding trends in the shop floor regarding the user experience. Are we finally at a time where it's not just about feature functions, but really making it centered around the human?
