CEOInterviews.AISiemens | Live from Hannover Messe 2026

C

Christine15:51

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.

M

Max18:00

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.

M

Militia20:13

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?

M

M21:41

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?

S

Sebastian Hüll21:57

Absolutely. Day one was amazing. We had lots of interesting conversations with customers. So, I'm excited for day two.

M

M22:04

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.

S

Sebastian Hüll23:07

Great. So direct current sound promising but also challenging. What makes protection in direct current networks so challenging?

M

M23:16

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.

S

Sebastian Hüll24:05

Great. And which protection devices can really switch DC safely? Which technologies come into question here?

M

M24:12

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.

M

Militia25:14

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.

P

Paramod Rao26:33

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.

J

Jorge Mendoza Salcedo26:53

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.

P

Paramod Rao37:07

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?

J

Jorge Mendoza Salcedo38:22

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.

M

Militia38:57

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?

P

Paramod Rao39:18

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.

M

Militia39:47

Yeah. Okay, thank you. And what about you, H? What have you been talking uh about with the with the visitors here?

J

Jorge Mendoza Salcedo39:52

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.

M

Militia40:10

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?

S

Shini Vaza42:06

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.

N

Naresh42:35

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.

M

Mark43:02

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.

M

Militia43:23

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?

S

Shini Vaza44:20

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.

M

Militia45:33

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?

M

Mark46:05

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.

M

Militia48:42

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?

N

Naresh49:33

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.

M

Militia51:12

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?

S

Shini Vaza51:34

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.

M

Militia53:25

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?

M

Mark53:41

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.

M

Militia55:16

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?

N

Naresh55:43

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.

M

Militia57:00

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?

M

Mark57:27

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.

M

Militia58:03

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.

C

Christine58:51

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.

F

Fabio Koga1:03:06

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.

A

Antonia Vareo de Godoy1:03:36

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.

F

Fabio Koga1:07:54

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.

A

Antonia Vareo de Godoy1:08:38

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.

F

Fabio Koga1:10:58

Perfect. Now let's talk about digitalization process in AXAP. Do you have any aspects to share with us?

A

Antonia Vareo de Godoy1:11:07

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

A

Axia Representative1:12:13

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.

H

Host1:12:27

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.

A

Axia Representative1:12:48

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.

H

Host1:13:38

Wow. Amazing.

A

Axia Representative1:13:41

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.

H

Host1:14:34

Well, amazing. So, there is a lot of opportunities for Simmons and for AXIA to cooperate and I hope so.

A

Axia Representative1:14:41

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.

H

Host1:15:28

Okay. So name of Simmons. I would like to thank you very much sharing this huge experience with us. Thank you so much.

A

Axia Representative1:15:36

Thank you so much.

H

Host1:15:38

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.

J

Judith Va1:17:11

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.

E

Eduardo Gorks1:18:37

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.

J

Judith Va1:19:36

Alejandro, what about you?

A

Alejandro Brainval1:19:38

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.

J

Judith Va1:21:22

Pablo, do you want to tell us the Brazilian perspective as an Argentinian?

P

Pablo Faba1:21:28

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.

J

Judith Va1:22:06

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.

A

Alejandro Brainval1:23:03

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.

J

Judith Va1:24:11

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?

E

Eduardo Gorks1:24:31

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.

J

Judith Va1:25:54

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?

P

Pablo Faba1:26:31

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.

J

Judith Va1:27:39

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.

A

Alejandro Brainval1:28:49

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.

J

Judith Va1:30:31

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.

E

Eduardo Gorks1:30:47

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.

J

Judith Va1:32:28

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.

P

Pablo Faba1:33:07

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.

J

Judith Va1:34:35

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?

E

Eduardo Gorks1:36:08

Let's avoid football, right?

J

Judith Va1:36:09

Yeah. Yeah. Yeah. That's a big one. Soccer.

E

Eduardo Gorks1:36:11

In a couple of months, we will not be working together. will be working against each other.

J

Judith Va1:36:16

Against each other? Yeah.

E

Eduardo Gorks1:36:17

Oh, where is this going?

P

Pablo Faba1:36:19

No, Brazil, Mexico, Argentina. We have a meeting in the US and Mexico.

J

Judith Va1:36:25

So, World Cup, we would want to welcome everyone to the World Cup in coming up and looking forward for those interesting games.

E

Eduardo Gorks1:36:36

Yeah, I wish you all the best. I'm sorry. I feel sorry already for Argentina, Brazil, but um yeah, you're anyway.

J

Judith Va1:36:44

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.

E

Eduardo Gorks1:36:52

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.

J

Judith Va1:37:16

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?

E

Eduardo Gorks1:37:32

That's right.

J

Judith Va1:37:34

Exactly. Good. So with that, thank you. And please go off and explore if you haven't done already. Thank you.

E

Eduardo Gorks1:37:41

Thank you.

A

Alejandro Brainval1:37:44

Thank you.

P

Pablo Faba1:37:45

Thank you.

H

Host1:37:45

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,

E

Eduardo Gorks1:38:59

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.

H

Host1:39:18

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.

J

Judith Va1:39:50

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.

H

Host1:40:13

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.

E

Eduardo Gorks1:40:22

Thank you so much.

A

Alejandro Brainval1:40:23

Thank you.

P

Pablo Faba1:40:24

Thanks.

H

Host1:40:25

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?

R

Rhina Brim1:42:30

Excited, of course. I mean, look at this.

H

Host1:42:32

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?

R

Rhina Brim1:43:37

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.

H

Host1:44:20

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?

R

Ref Liberadian1:44:42

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.

H

Host1:46:10

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

R

Rhina Brim1:46:27

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.

H

Host1:48:48

Mhm. So when you say tasks which can be done is it like packaging or loading or you have some examples.

R

Rhina Brim1:48:56

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?

H

Host1:49:19

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?

R

Ref Liberadian1:50:07

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.

H

Host1:52:41

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,

R

Ref Liberadian1:53:07

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.

H

Host1:53:49

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

R

Rhina Brim1:54:05

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.

H

Host1:55:25

Mhm. And where are we at Seammens at this journey looking into our sites and production facilities right now?

R

Rhina Brim1:55:34

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.

H

Host1:57:18

Mhm. Okay. So what's Nvidia's role in here when we see all that you said you've been working on that together

R

Ref Liberadian1:57:25

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.

H

Host1:59:10

Perfect match. And it seems you have it under control. But what's the toughest problem you haven't solved yet?

R

Rhina Brim1:59:16

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.

R

Ref Liberadian1:59:45

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.

H

Host2:01:01

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.

R

Ref Liberadian2:01:54

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.

H

Host2:02:39

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.

R

Ref Liberadian2:03:52

Thanks so much.

H

Host2:03:54

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.

R

Ramy Ganipana2:04:21

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.

M

Michael Sharp2:05:24

Yes. So Dr. Michael Sharp part of Semen's director for goto market for data and AI

D

Dr. Jawatub2:05:32

Dr. Jawatub u responsible for proprietary AI models for Andreser and driving the strategy in the company

L

Lucas Mosko2:05:42

and I'm Lucas Mosko I'm heading the strategy and portfolio management of Bosch connected industry.

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Ramy Ganipana2:05:49

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.

M

Michael Sharp2:06:27

Yes, Ramen. Thanks a lot. And as Ramen said, so let's start with the design and planning phase. And um what

M

Michael Shrup2:06:36

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.

C

Christine2:07:56

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?

M

Michael Shrup2:08:01

Yes.

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Christine2:08:01

And also to configure products before commissioning. So Michael, what have you in engineering and how is AI helping there?

M

Michael Shrup2:08:13

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.

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Christine2:09:37

Thanks. After engineering comes installation and commissioning. So Chawat, which use case have you brought?

C

Chawat2:09:47

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.

C

Christine2:11:19

Maybe a short one afterwards.

C

Chawat2:11:20

Okay, a short one after that. Listen closely. We'll have a test.

C

Christine2:11:25

Thank you, Chad.

Now the operation and optimization phase is serious. Lucas, you brought two use cases. What role does AI play there?

L

Lucas2:11:45

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.

C

Christine2:13:05

Got it. And the second use case?

L

Lucas2:13:09

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.

C

Christine2:14:48

So they work complementarily.

L

Lucas2:14:50

Yes.

C

Christine2:14:50

Thanks. We have one more case from Michael.

M

Michael Shrup2:14:55

Yes.

C

Christine2:14:56

Awesome.

M

Michael Shrup2:14:57

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.

C

Christine2:16:15

That might extend product lifetime, but modernization may be needed. Chavat, what have you done for modernization?

C

Chawat2:16:33

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.

C

Christine2:17:24

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?

M

Michael Shrup2:18:02

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.

C

Chawat2:19:09

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.

C

Christine2:20:00

Lucas?

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Lucas2:20:00

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.

C

Christine2:20:51

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.

M

Michael Shrup2:21:34

Thank you.

M

Max2:21:34

Thank you.

C

Christine2:21:36

Thanks to Ramen, my host, and our external speakers. Lucas, thank you. Here are gifts from our customer Natura, made with Seimens technology.

C

Chawat2:21:42

Thank you.

C

Christine2:21:42

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.

M

Michael Shrup2:22:56

Wonderful.

C

Christine2:22:58

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?

M

Michael Shrup2:24:13

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.

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Christine2:26:02

When we release to market, we test hardcore. We have two pilot customers here. Michael from Andred, why is the IEN engineering agent important?

M

Michael Lou2:26:48

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.

C

Christine2:28:14

Kevin, what have you used the IEN engineering agent for?

K

Kevin Feruzan2:28:21

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.

C

Christine2:31:15

Great. Michael, are there any limitations? It seems everyone is happy.

M

Michael Shrup2:31:33

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.

C

Christine2:32:43

Michael from Andred, what is your experience?

M

Michael Lou2:32:48

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.

C

Christine2:34:55

Michael, what differentiates this product from other AI assistants?

M

Michael Shrup2:35:04

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.

C

Christine2:36:38

Kevin, where do you see the biggest benefits?

K

Kevin Feruzan2:36:45

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.

C

Christine2:39:23

Great. Michael, what is the future perspective?

M

Michael Lou2:39:34

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.

C

Christine2:41:57

So, exciting times. Michael from Seimens, what is your vision?

M

Michael Shrup2:42:54

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.

C

Christine2:44:11

I feel your passion. Is there a QR code for more info?

M

Michael Shrup2:44:24

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.

K

Kevin Feruzan2:45:15

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.

C

Christine2:45:45

CSMT commercial break. Thanks, Kevin. Good luck. We have gifts from Natura. Handing over to Max.

M

Max2:46:31

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.

N

Narrator2:47:42

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.

M

Max2:49:35

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?

J

Joanne2:50:26

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.

M

Max2:51:08

Michael, how does your solution help?

M

Michael Alfen2:51:22

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.

M

Max2:52:24

What does prefabrication mean for organizations?

M

Michael Alfen2:52:34

You don't have to do work on site, not dependent on weather. It speeds up. It's like Lego blocks.

M

Max2:52:52

Joanne, your view?

J

Joanne2:52:55

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.

M

Max2:53:42

Michael, any challenges?

M

Michael Alfen2:54:30

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.

M

Max2:55:53

Joanne, anything to add?

M

Michael2:56:11

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.

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Christine2:56:35

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.

J

Joan2:57:00

It's a big step up.

M

Michael2:57:01

It's a big step.

C

Christine2:57:02

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?

M

Michael2:57:15

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.

C

Christine2:57:49

Yeah. Did you just say 30 years? So that's what we can call maintenance-free. 30 years I assume. Yeah. Okay. Okay.

M

Michael2:57:51

About 30 years more but the tests do not go further than that.

C

Christine2:58:01

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?

J

Joan2:58:19

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?

M

Michael2:58:42

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.

C

Christine2:59:06

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?

M

Michael2:59:20

Absolutely. I'll be here for another couple of hours.

C

Christine2:59:22

Okay. And anywhere at the booth or a particular place you're going to be?

M

Michael2:59:26

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.

C

Christine2:59:36

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.

J

Joan3:00:20

Yeah. Thank you.

C

Christine3:00:28

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.

A

Amadep Singh Rana3:01:19

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.

C

Christine3:13:01

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?

M

Militia3:14:46

I am ready.

C

Christine3:14:48

Awesome. Then I'm going to hand it over to you. All the best.

M

Militia3:14:49

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?

R

Ricardo3:16:46

I think it's possible. We made it possible. Yeah.

P

Philip3:16:51

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.

M

Militia3:18:21

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?

M

Mikail3:19:06

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.

M

Militia3:21:21

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.

M

Mikail3:22:04

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.

R

Ricardo3:23:06

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.

M

Militia3:24:22

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?

R

Ricardo3:24:48

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.

M

Mikail3:25:36

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.

M

Militia3:26:14

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.

R

Ricardo3:27:02

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.

M

Mikail3:27:35

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.

M

Militia3:28:26

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.

R

Ricardo3:28:41

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.

M

Mikail3:29:07

My final message: never be afraid to do something new. Important that we can change ideas into reality. This is our goal.

M

Militia3:29:36

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.

P

Philip3:30:32

Thanks.

M

Militia3:30:34

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.

C

Christine3:30:48

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.

P

Peter Steel3:32:56

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.

C

Christine3:34:15

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.

P

Peter Steel3:34:57

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.

A

Animari Gree3:35:40

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.

C

Christine3:36:04

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.

P

Peter Steel3:36:19

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.

A

Animari Gree3:37:14

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.

C

Christine3:37:57

So Animari, with automation background, what strategic role does software-defined automation play?

A

Animari Gree3:38:10

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.

C

Christine3:40:07

Great insights. Peter, what worked and which lessons were crucial in large-scale deployment?

P

Peter Steel3:40:23

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.

C

Christine3:41:33

Which added value does virtual PLC deliver in daily production, Animari?

A

Animari Gree3:41:51

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.

P

Peter Steel3:43:34

The goal is to reach old KPIs: reliability, investment. We proved it's working. Now we can add value.

C

Christine3:43:58

What are Audi's plans for the future?

P

Peter Steel3:44:06

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.

C

Christine3:45:10

Which is a nice one. I'm going to unveil where you can see that car. Animari, what are next development steps?

A

Animari Gree3:45:19

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.

C

Christine3:46:37

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?

R

Ralph Vagnner3:50:53

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.

C

Christine3:52:37

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?

V

Vlad Larich3:52:45

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.

C

Christine3:53:45

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?

R

Ronny Henry3:54:04

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.

C

Christine3:56:45

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.

R

Ralph Vagnner3:57:09

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.

C

Christine3:59:05

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?

V

Vlad Larich3:59:25

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.

C

Christine4:00:56

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?

R

Ralph Vagnner4:01:04

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.

C

Christine4:02:51

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?

T

Tim Gaus4:06:37

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.

C

Christine4:07:24

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.

M

Metin Kaplan4:07:37

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.

C

Christine4:08:44

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?

T

Tim Gaus4:09:21

Yes. And right, so the expectation of usability and experience that your operator is going to have that feels comfortable, natural. You know, we always talk about bring your home to your workplace. That absolutely is true in manufacturing and that's where a lot of the AI modernization is going to help us get there. I would also say we're at the cusp of physical AI becoming a much bigger portion of the way we think about making and moving things and the role of that interaction model is going to shift, which is why the composability of your MES system needs to be able to adapt to that next evolution as well. So I think in both sides, the user experience the operators on the shop floor have to have something that feels natural, but we also have to prepare ourselves for a spot where that role of the human is shifting as well.

C

Christine4:09:58

And about this human centric UX, I have to hint at one thing in Op Center. We have this huge advantage that we have a world-leading low code platform in house and we modernized our UX dramatically. We have out of the box role-based process-centric apps that we deliver and customers love it. They like to actually use them and also to be able to configure and extend easily. So I just wanted to give a shout out to Mendix UIs that we have on top of Op Center.

T

Tim Gaus4:10:33

No absolutely I love that and it's a great advantage for us at Siemens.

C

Christine4:10:35

Obviously I often like to say we're standing on the shoulders of giants. We can leverage a lot what we as a company do with world-class user experience provided by Mendix with the leading provider for low code and extending on that. So that's really great. I think that's one thing that already helps the customers take care of the complexity and face it. Manufacturing is complex. We have to connect also to a lot of different systems and from my experience a lot of the customers are actually saying well this factory is really special and this factory is really special. And every factory is really special. So Tim, I'd like ask you what's your point of view regarding how to deal with that complexity especially when you scale MES rollouts and MOM rollouts across multiple factories and what are the key elements for that from your perspective?

T

Tim Gaus4:11:38

Yeah, there's no doubt this is the core challenge of manufacturing since we started manufacturing things. Like, I guess Henry Ford got it right and we lost our path afterwards. There's a couple of things here. One, I would say modern data management has shifted which is enabling from a bottoms up way a much different way to think about scalability. You know, your abstracted contextualized data model allows you to connect in a cloud-based MES in a much more seamless fashion. But that doesn't get away from the need for having at least some degree of a global template that then can be deployed and governed in a central way. So you kind of combine the two things together where you manage your shop floor data in a much more scalable way, but you also manage the overall controls and governance from the template in a much more global fashion. Those things together get you the scalability.

C

Christine4:12:22

I like that a lot. I mean it's what we like to say is sort of the global consistency but also local flexibility. And that's one thing we are really proud of that our technology about the metadata-driven experience allows you to do to have these template based approaches where you can have a global view on things and then you can have local adaptation to the local processes. And of course you get the ability to upgrade your systems in a much easier way as well which obviously is a huge challenge. So maybe since you are one of our prime partners around this, the partner ecosystem is really important to us. Is it something where you feel what is it that you contribute to these kind of programs and what is the unique value proposition that you have around it?

T

Tim Gaus4:12:49

So I love our partnership. I think we do a great job together. The thing that Deloitte consistently brings is that manufacturing expertise and understanding. You know, you can't just put in a system or a system stake. You actually have to drive value and outcomes. And Deloitte brings those insights to the shop floor because you know, like I was a plant manager, that's where I came from. That's what I did. And so I can put myself in the client's shoes and help them navigate through that. Quite frankly, in all these MES transformations and modern transformations, the technology not no assertion is the easy part. It's actually the people that's the hard part. And what Deloitte brings is the ability to people bring through that change and the adoption curve along with putting in the technology itself. I also, by the way, love the innovation you guys bring because it makes our job a heck of a lot easier.

C

Christine4:13:59

Love to hear that. So, so great. Maybe as a bit of a closing, is there any piece of advice that you want to give to manufacturers how to approach that innovation? Maybe Metin you go first.

M

Metin Kaplan4:14:19

Uh yeah, what I would say is during this transformation, don't see it as just a replacement. A fresh look at the process and the existing solutions and how that fit together and work with a trusted partner and world-leading solution Op Center to define and work on that system of intelligence that can drive your manufacturing for the decade to come.

T

Tim Gaus4:14:45

I would say this is the moment for me to become the driver of manufacturing transformation and you need to think about it from a transformation landscape. I mentioned the fact that this is about the people change as much as it is about the technology, but don't forget about the technology. You have to think about scale from day one. I think Tobias you said the other day, you know, start small and scale fast. That's 100% correct for these type of transformations. If you bring that global mindset, you bring a people first approach, the technology will work, but you have to take it in a very kind of a stated way.

C

Christine4:15:17

And I love that because that's one of the things we as Siemens take as very important. It's both technology and people and I think those two things when they come together, that's where the magic happens. So it's really all about that and what we're doing. So from my perspective, that's a great summary that we are invested heavily into obviously the portfolio, driving the technology forward, integrating the breadth of it, and really investing into the people, be it the adaptability and that the shop floor people actually get a great user experience, but more importantly that they are part of that change process so that you can start rolling it out to various factories and scale. And that's one of the things where it's important to have that ecosystem, the collaboration of working together on these kind of global rollouts that we see across the world. So from my perspective, that's a wrap. Thanks so much for being here. We appreciate your time, Tim.

Thanks to the three musketeers presenting their stories. I have a little something for you from Natura, our customer from Brazil. Soap bars produced with Siemens technology. And our external speaker gets a bigger bag with cosmetics also from Natura all the way from Brazil. Thank you so very much and enjoy the rest of the day here at Hannover Messe. You can find those gentlemen and the team at the booth over there. And we're going to move on with topics here in regards of the industrial metaverse and how it has been implemented and in use at Rush, an ecosystem shaping the future. Ecosystem is a big topic here because alone we cannot do that anymore. What challenges arise and for this we are working together with partners, partner companies in that case like Accenture and Nvidia supporting us delivering best solutions for our customers. And I am very very honored to hand it over now for a great panel coming up here for the next 20 minutes to my colleague and she's in charge for global vertical strategy and execution planning manager for life sciences and CPG at Siemens. Please welcome with a big round of applause Susan Faustst.

S

Susan Faustst4:17:51

Thanks and also warm welcome from my side. Imagine you are a big pharma company and you are planning a green field plant in the US. A state-of-the-art site should rise from an empty plot, but nothing inside is certain. No one knows how lines, AGVs, buffers, and conveyors will interact with each other. The factory only exists in single pieces. Impossible to design or to optimize with confidence. So how to handle all these kind of uncertainties and ensuring the construction time stays in schedule. This kind of question we have discussed last year here at Hannover Fair at the Siemens booth together with Rush. Imagining a digital twin integrated in an industrial metaverse for Greenfield plant the new lighthouse project for Rush. One year later, so today, this is how it looks like. This is how it looks like when this imagination becomes reality with the power of a strong ecosystem between Rush, Siemens, Nvidia and Accenture to unlock multi-million savings for Rush, a pioneer in the life sciences industry towards digitalization. And therefore I'm more than happy that Rush will give us first some insights about this project called Apollo 11 and afterwards we're moving into a panel conversation together with Siemens and Nvidia and Accenture. I hope you are curious because this project and these insights are really a game changer in the life sciences industry. And with that I would say a warm welcome to our first speaker Yan Vocett. He is the director for smart manufacturing at Rush and he is the head of digitalization for this project Apollo. A big applause to Yan. Yan come on stage and tell us more about this project.

Y

Yan Vocett4:20:18

Yeah, warm welcome also from my side. Thank you for having me Susan. So it's a pleasure today to present you our project Apollo and this project is called Apollo for a reason. Because like the missions in the past we had to deal with a lot of unknown areas and uncertainties. For Rush it's a completely new product with high volume, a completely new site, and we decided to invest nearly 2 billion to build it. And I would say nowadays planning something in 3D is a no-brainer, so everyone is doing that. So we decided we need to shift this a bit and we wanted to simulate every process, every footprint and every heartbeat of the facility itself before the actual foundation is laid. And doing this we are very under high pressure from a time perspective. We decided to start small in our headquarter in Basel and scaled from there to this big project we will talk about now.

S

Susan Faustst4:21:23

Wow, that sounds exciting. But I would say also quite challenging. So Yan, what kind of challenging you were facing in this first phase and what have you learned out of it?

Y

Yan Vocett4:21:33

So first we dealt with the so-called investment paradox right. So you have to dive through a cloud of a lot of AI buzzwords and you need to figure out okay where you want to start. So we started quite small and simulated just little pieces of the facility itself before we went more to a more granular approach and starting from there suddenly a so-called snowballing effect kicked into place. So the users contacted us and have seen what's possible and we figured out more and more use cases we never expected before with that.

Next slide. Exactly. Our lessons learned. Of course, I would say from technology perspective, this is not a big thing. It's quite possible, but the data accuracy was a big thing. We needed to rewrite all tender documents. We had similar discussions with our vendors, whether hardware or software side, to not receive static documents, but also dynamic models at the end. And for us the mindset shift at the end was a big thing because we wanted to enable the simulation-driven planning which is a big difference to what you now I would say do in classical projects.

S

Susan Faustst4:22:50

Mhm. And talking about simulation-driven planning and the industrial metaverse, quite often I'm hearing about the industrial metaverse. It looks fancy but what's in it? Can you give us some insights? What is the real business value for you from Rush side?

Y

Yan Vocett4:23:00

So I'm with Rush for more than seven years and I would say I was part of all digital twin initiatives but honestly none of them really scaled until now and the big shift here is because we shift from this rear view mirror to actually the quantified process prediction and this is a big change in what we did in the past and by today with having this in place I can tell you with surgical precision you will have this OEE at that at 9:00 a.m. in the morning, right? And I haven't been able to do this in the past. And the result is actually that we shattered our initial business case by 40%. Because of these we were able to realize multi-millions in optimizations and savings.

S

Susan Faustst4:23:53

Wow. I would say that is a great example how also a strong ecosystem can create this kind of value together. Talking about ecosystem, before we continue with the panel Yan, can you also give us some insights why you have decided to go with this triangle between Siemens, Accenture and Nvidia?

Y

Yan Vocett4:24:12

Because of the results of our PoC in the small scale we figured out okay there's a special DNA needed if you work in such an environment and now we decided to have this triangle because first we needed the engine like the technology nucleus where Nvidia comes into case. Secondly, the connector. This is where Accenture helped us the most with getting through the fragmented data silos we had in the past and connecting all the different dots. And third, then the brain and this is where Nvidia where Siemens comes into place because technology with logic is just noise and we needed this industrial logic being the brain behind of all of that.

S

Susan Faustst4:24:55

Great. Thanks for sharing these kind of insights and I would say it's a good time to also right now bring additional insights from these three key players on stage. Therefore I would say Yan let's continue. Let's move on and let's also welcome on stage Henbid from Accenture, Florian Ganta from Nvidia and also Stuart Mcatchen from Siemens. Come on stage.

Great to have you all here right now the full power of this ecosystem. I'm really excited to also get right now your perspective on this topic and to start with I would like to ask also Siemens and Nvidia the first question. So the term of omniverse and industrial metaverse is currently everywhere and for those of us who are perhaps not so familiar with this term. What is the omniverse or the industrial metaverse about? Stuart, can you give us some insights?

S

Stuart Mcatchen4:25:56

So the industrial metaverse is really a place to experience the digital twin. You know the end goal is to how to make engineering decisions faster leveraging data from both the physical and virtual world is integrated. Right? That's the essence. Now with Siemens, we've been on this journey for 20 odd years bringing virtual and physical worlds together. We got a very comprehensive portfolio of software for simulation to be able to simulate all different aspects and really develop the most comprehensive digital twin. That's part of the journey we've been on. And you know it's basically we've been working very closely with Nvidia now to embed Omniverse inside our portfolio right to develop out-of-the-box solutions that customers can deploy. So with this bringing a set of libraries and toolkits and we're integrating as many of those as we can into our portfolio and really developing commercial joint solutions. You may have heard of Digitality Viewer, Digital Twin Composer. These are start of solutions that we're rolling out to help deliver the industrial metaverse in an out-of-the-box way.

S

Susan Faustst4:27:14

Okay, great. Florian, from an Nvidia point of view, what would you add?

F

Florian Ganta4:27:18

Yeah, well, first of all, let me thank for being on the panel. It's a real honor being with you with three of the greatest partners and also I want to thank Rush Yan for a great vision and driving that. So, when it comes to the industrial metaverse, first of all, it's a huge opportunity for all of us. It's a huge opportunity to shorten timelines, to increase efficiency, to lower cost, production planning costs. From a more technical point of view, and from Nvidia perspective, the industrial metaverse brings the industrial world, the actual facilities, the production sites, the warehouses into a shared virtual environment, but still obeys the laws of physics. And this is the place where people, AI agents and robots are going to plan, they're going to simulate and also continuously iterate before anyone touches the real hardware. And this is where Nvidia also helps our ecosystem and provides libraries to the ecosystem, the underlying infrastructure to the ecosystem as well as open AI models and data to fine-tune those models and recipes to bring things together to build industrial metaverse applications and physical AI.

S

Susan Faustst4:28:37

Okay then thanks first of all for setting the scene for this panel and right now I would be curious to also understand how to get started with such a project. Looking to you, I mean Accenture is the system integrator for this project. We've already heard about this project from Yan. From an SI point of view, how you are starting with such a project?

H

Henbid4:29:01

Great question Susan and thanks a lot for having us and thanks to all the partners for this really great collaboration and openness. I would like to call out four key elements to focus on. Right? So point number one, start as early as possible. Right? The earlier you get started, the earlier you can set the right angles. Secondly, I would say, you know, also aligned with what Julie Sweet and Roland discussed yesterday on the stage, focus on the value. Be laser clear on value, on the business objectives you want to achieve. Then also make sure that you assemble the right team and last but not least try to be very clear on what is needed during the journey. Agree very early on what needs to be shared by all vendors that you don't create any challenges along the way.

S

Susan Faustst4:29:57

Okay. So I would say Yan start early we can say check. My question to you is what's next? So what are Rush priorities for the next phase and which kind of outcome do you expect?

Y

Yan Vocett4:30:09

So we have actually a dual approach. So of course we want to expand what we already have now in place in our existing green field projects but nevertheless we want to go further and use this also then in the brown field.

S

Susan Faustst4:30:23

Okay. So that means we stay tuned and let's see what is coming.

Okay. And we have already heard this project is a lighthouse project for this industry and when we are having a look into other industries looking also to Stuart and Florian which value brings simulation AI and the omniverse and other projects. Florian can you give us some examples?

F

Florian Ganta4:30:46

Yeah sure. So the big challenges we are attacking they're not tied to a specific industry. They are universal because if you think about that changing something which is physical is always very costly and time consuming and sometimes it can be actually dangerous. So the power of simulation and the power of AI is helping to overcome those grand challenges across all the industries. Doing what-if scenario simulations and identifying design errors very early on and optimizing energy consumption, cost, throughput, all that can be done in simulation first. So a simulation first approach is key across all industries. But still if you look at other industries and we stay in Germany, if you look at automotive, we see BMW being on the industrial metaverse journey for some years and they achieved efficiency gain of 30% in production planning. We can also look at logistics with Kion group where we all work together with Kion who's simulating the AMR fleets in a digital twin to optimize the commissioning time. So we see a lot of values and use cases across all industries because the ultimate challenge is the same.

S

Susan Faustst4:32:07

Yeah. And Stuart from a Siemens point of view do you have also other examples from other industries?

S

Stuart Mcatchen4:32:14

Yeah. So it's very exciting. We've got many projects in many different industries. So, it's very exciting. You know, three years ago, we demonstrated batteries plant. We've done high-end ships. We demonstrated last year. We've been doing our own plant in Erlangen. So, we're doing projects in many different industries. And this year, we're highlighting Kion, which was presented yesterday with Roland and actually PepsiCo over here in the stand if people want to see it in more depth. And these are exciting new projects, right? PepsiCo was on stage at CES this year with Roland Busch and they actually came out and said based on the project that we achieved in 10 weeks, they can achieve a 10 to 15% savings in CapEx and 20% improvement in their OEE. And this was Athena, the CEO of Latin America and she's head of global strategy for PepsiCo actually presenting this. So it's pretty exciting. Kion very exciting as well. It's exact same relationship Nvidia Accenture together delivering solution for them and so it's just exciting times.

S

Susan Faustst4:33:28

And please stop and look in more depth at the PepsiCo because that's interesting because that's a brown field. So it's exactly what Ro wants to do next and that's exciting times.

Yeah, agree. And I think PepsiCo is also a good example how also standardization can help in terms of scalability. And when I'm thinking about scaling, I'm also thinking about reusability. Yan, how important is reusability for Rush specifically for phase 2? And what does also scalable success looks like?

M

Melissa4:41:12

Together. Yeah, that's a special space that I want to ask Claudia to explain a little bit better.

C

Claudia4:41:20

Okay.

M

Melissa4:41:20

But honestly, as Brazilian people, I'm really honored to be part of that. So Claudia, please explain to us a little bit about it.

C

Claudia4:41:29

I think you can give me a little – I can show everyone here.

M

Melissa4:41:33

Amazing.

C

Claudia4:41:34

What is Sip Pain about? When you talk about Sip Pain, people normally remember Sirius, our main facility. This is how the proximity between Siemens and Sip Pain started 10 years ago. But Sip Pain is not just one facility. We have a large number of national labs responsible for advanced research. Some people ask us during the event, I invite you to visit us at hall 12. We have an exhibit there. Sip Pain is a private nonprofit organization funded by the federal government of science and technology in the country. This helps us develop very advanced technologies in strategic areas. If a company wants to work with us in Brazil, they can apply through four possibilities: first, open facilities where researchers from anywhere can come and develop their own projects; second, internal research and development where we help the country position itself globally; third, innovation where I lead the area that connects industry and resources to develop new solutions; and finally, training and education extensions where we teach what we are doing. For example, in agriculture, healthcare, environment, and renewable energy, we think about what is necessary today to be better in the future using our internal resources. Siemens is fostering new technologies and helping bring together researchers, industry, and government to bring new solutions to the market. Our facilities cover 500,000 square meters where science is developed. It's fascinating. We asked to bring examples of how we develop science today. The strategic areas we approach show what you can do even more. We connect very high-talent, passionate people. It's not only about infrastructure; we have one of the main public investments in technology in the country, but it's also about training people to talk with industry and bring solutions they need. Synchronizing very specific knowledge is important, but it won't work without people doing their best. I want to add, Militia, that Siemens, as a tech company, is part of that. Part of the facilities has our technologies inside, including Sirius and the lab for biotechnology. So all the technologies inside Sip Pain often include Siemens technologies. That's fascinating for us as Brazilians to see everything working concretely. The science is part of our daily approach for technologies.

M

Melissa4:46:28

That is truly fascinating, especially thinking about an innovation-driven mindset.

C

Claudia4:46:33

Yeah.

M

Melissa4:46:33

And mindset is the right thing. So beyond all of this, if we think about where we are heading in the future, what that looks like – you speak about your campus, I say our campus now, that's how close we are to the partnership. You spoke about the strategic value it allows, which is important to us. If I think about anything that could help us move beyond all of this, what would that be?

C

Claudia4:47:11

Collaboration. I think this is exactly one of the main slides we have. Collaboration is the secret to make things real. When you look at our symbol, the way we construct the idea of connecting people to make what is necessary for our society in the future better. I think this is how we can impact the world more and more. That's the main point.

M

Melissa4:47:42

Yeah. And that's all about technology we've proposed, including collaboration, of course.

Because we can see the result of what we apply in terms of technology by the society at all. With research and science, all the people involved in such collaborations.

Yes. I love the fact that we are also talking about the cultural aspects. Claudia, earlier you said something that stuck in my head – you spoke about skills and having the right skills in place. Now maybe I can ask you, Adilson, are we doing that at Zeus as well? Maybe you can share how we push to have the right skills.

A

Adilson4:48:23

Yeah, sure. We know how to deliver the result of the technology inside the compass. We are quite confident that we can apply it to the daily lives of applications and operations.

M

Melissa4:48:39

Yeah. I think there's going to be exciting things happening in the next couple of weeks. I'm going to look out for the news and everything happening. For me, this is really the partnership that shows we're driving innovation, but we're not doing it alone. That is a clear thing – we're in the boat together. And if we work together, we can make this move to the next level. And that's what I love about this collaboration. And that's where AI comes. Because we want to introduce AI as part of what we can discuss together. What I also learned is that Brazil is really building from the bottom up. There's a lot of infrastructure and a lot happening, which has become so clear thanks to this being at the hand of a failure. Have you got to meet the CEO of Brazil already?

A

Adilson4:48:39

Yeah, we did get the chance to be with Pablo already, and I can announce to all the people here that we are going to sign a memorandum of understanding this afternoon as part of our collaboration partnership.

M

Melissa4:49:52

So he's going to be with you making the big moment – the golden signing.

A

Adilson4:49:57

Exactly.

M

Melissa4:49:58

That's going to be cool. So lots of exciting things. Signing a memorandum of understanding is big, and it shows this is a true partnership. I told you earlier today – nothing's abstract, we make it practical, and this is a great example. Thank you so much for being with us. We also have a little present for you.

A

Adilson4:50:19

Oh, thank you, Melissa.

M

Melissa4:50:21

Thank you so much for being with us.

A

Adilson4:50:23

Thank you, thank you, Melissa. Thank you.

M

Melissa4:50:27

And we will see each other definitely in the news, at the latest at the next press release.

A

Adilson4:50:32

Thank you. Thank you so much.

M

Melissa4:50:33

Thank you. Thank you so much. And thanks to our wonderful panelists and Militia. Awesome. Hey, my watch just beeped – 10,000 steps today. I don't know about you, but there are still a few free seats. That definitely deserves an applause. Usually I sit all day on that sofa. Ladies and gentlemen, please take a seat and listen to the next talk we're going to host here. It's about circularity and how Siemens and Spare Parts Now are taking circularity to the next level. We all know resources are important, and innovation isn't just about performance. Innovation is also about using the right resources and taking responsibility, making more with less or using what's already there another time if possible. I'm not the expert, but let's get closer information with those gentlemen on stage. I'm very pleased to welcome Dr. Christian Hoffford and Stefan Jagger, who have a very strong passion for circularity in industrial manufacturing. They will share how circular solutions can drive real business impact. Please help me welcome them with a big round of applause.

Hi Christian.

Great having you. That's a nice handshake, Stefan. Awesome. Let's take a seat on the sofa of wisdom in the living room of innovation hall 27. You're smiling. Have you been around at the fair?

C

Christian Hoffford4:52:18

Yes, a little bit. Really amazed by your booth here. Amazing. When I come here every morning I have this proud to be Siemens moment as well. And Stefan, have you been around?

S

Stefan Jerger4:52:29

Yeah, absolutely. Great what Siemens shows – what we can do, where we can really solve problems with technology. I'm really proud of what I see here. Big thanks to the colleagues doing the booth stuff. Really great job.

M

Melissa4:52:41

Oh, definitely that deserves an applause as well for the booth staff. They support here. The booth is packed. From 9:15 in the morning, this place is the center of innovation, the heartbeat of innovation. Let me introduce you: Dr. Christian Hoffford, CEO of Spare Parts Now, and Stefan Jerger, head of Circular Product Service at Motion Control from Siemens. Let's dive deeper. Christian, why is the transition to a circular economy so important right now?

C

Christian Hoffford4:53:38

Thanks, Melissa. If you look at the numbers, it becomes very clear. Today, about 9 million tons of industrial waste are produced globally every year. On the other hand, only about 7% of the global economy is truly circular. So there is an absolute mismatch. When I talk to maintenance engineers or production plant managers, they have one real issue: keeping machines running and avoiding downtime. For me, it's not whether there is an issue with circularity, but how we can contribute. These people don't think in sustainability targets; they solve their problems. With circularity, doing repairs and refurbishing helps solve their problems and creates a more sustainable world by reducing waste. This is why I'm here discussing this with Stefan. It shows that circularity is becoming a big topic in the industrial world. We presented our EcoTech label yesterday, and Stefan, you might talk about that as well.

S

Stefan Jerger4:55:38

Yeah, absolutely. There are reasons why we think we have to think about circularity. Look at geopolitical crises, unsafe or unsecure sources of raw materials, or tariffs that change overnight. Circularity can be an answer. But there is also an economic reason. Today, 93% of the global economy belongs to the old linear model of take, produce, use, and waste. That will shift. If only 20% of industrial products worldwide are recycled, refurbished, or recovered, we could save hundreds of billions of tons of raw materials. The economic evaluation shows a saving of $4.5 trillion.

M

Melissa4:57:08

There is a must – you need to put that into context.

C

Christian Hoffford4:57:11

Yeah, the value behind the circular economy is not only that we have to do it because of law or because we need raw materials; it's also a huge business opportunity for us as Siemens and for our customers and partners. We want to be frontrunners in circular business. We believe in it and are pushing circular economy because when we ensure our customers that our products can stay longer in their machines, their machines stay longer in the market, and the customer stays longer with us.

M

Melissa4:57:49

Okay, obviously. To put it in context, $4.5 trillion is about the size of Apple.

S

Stefan Jerger4:57:56

Or it's Meta, Alphabet, and Tesla together – the stock value of these three big companies. More than Apple today. Really a huge amount.

M

Melissa4:58:07

Absolutely. So circularity is definitely not just a nice-to-have; it's a must, a necessity. Christian, how does circularity look in action at Spare Parts Now?

C

Christian Hoffford4:58:21

We are a digital platform. A customer called us the Amazon for industrial parts, which was a big honor. On our platform, you find more than 5 million different parts, and circular refurbished parts are more than 1 million. We are the only one-stop shop that pushes refurbished parts. In these times of crisis and raw material increases, customers love us because we show they can save on average 50% or more. That's the impact we have on circularity.

M

Melissa4:59:12

It's brilliant. Any more points from you?

C

Christian Hoffford4:59:15

Yeah, I can give a recent example. Two weeks ago, a cruise line was on a harbor with more than 2,000 passengers waiting for departure. It didn't work due to a converter failure. No OEM could deliver, and no technical dealer had it. They found us, and we organized that part because a good player had it. We also solved the service. This shows how platform economy can contribute to companies that didn't know us before. I was amazed because it shows how we can solve real-life problems.

M

Melissa5:00:13

So how do I have to imagine it? You have a great network and know whom to talk to for spare parts.

C

Christian Hoffford5:00:22

We are a platform where companies with good quality original brands can put parts on. We show it like a store, and customers buy. It's a one-stop-shop journey. For circular parts, we bring them back to partners who repair.

M

Melissa5:00:52

Okay, brilliant. We are proud that Siemens launched circular repair services for refurbishment and remanufacturing. Can you give us more insights, Stefan?

S

Stefan Jerger5:01:06

When you're a producer of industrial goods, there are two main questions: do you repair what you produce, and do you take back what you produced in the past? For the first question, yes. A few weeks ago we launched our new repair service called Circular Repair Advanced and Circular Repair Premium. Circular Repair Advanced is our way to refurbish products. As the producer, we know the product best – its components, how to test it. We can restore performance to 100%. I mean 100% – no doubt. The customer can order refurbishment from Siemens.

M

Melissa5:02:30

Sounds like a premium service here, right?

S

Stefan Jerger5:02:33

Yes, Circular Repair Premium goes beyond performance restoration to look and feel. After a Circular Repair Premium, you get a product that looks like new, including packaging and manual. You cannot differentiate it from a new part. This is very interesting for many customers. They can choose between refurbishment and remanufacturing according to their needs. We are now offering this for most drives, like SimoDrive, and working on expanding to more product families.

M

Melissa5:03:40

Okay, got it. It's clear why our customers need Siemens and how circularity can be a successful business model. However, we cannot do this alone.

S

Stefan Jerger5:03:49

No, absolutely. The second question is do we take back what we produced? Yes, but we need a strong ecosystem of partners, takeback companies, and resale companies. That's why we are talking to Spare Parts Now. Christian, I'd like to hand over to you.

C

Christian Hoffford5:04:18

Yes, absolutely. Stefan is right. To bring circularity to the next level, we need to connect all parties in the supply chain. Spare Parts Now will take part in taking back, bringing to recovery, and reselling. We have a lot of sales power and the possibility to take parts back from thousands of customers – maybe 100,000 in the long run. Nobody can do it better than the manufacturer for best-in-class repairs and refurbishment. I'm convinced that even big companies like Siemens will strengthen resilience and cost resilience, especially in crises.

S

Stefan Jerger5:05:26

Yes. For us, this new business model of take-back of used Siemens products, covering them with our new Circular Repair Premium and Advanced, and selling them to the second-life market is an opportunity. Circularity will be the new standard for Siemens, for customers, and for the environment. It's already here and will stay.

M

Melissa5:05:55

We had a discussion yesterday with Volkswagen on stage. Same thing – Volkswagen is focusing big time on circularity. If an automotive industry leader thinks of taking things back to reuse, it's the only way to do it.

C

Christian Hoffford5:06:13

Absolutely.

M

Melissa5:06:13

Okay, great for sharing your success stories. We can show more at the booth. You may want to talk to our experts at station 120. You will be around for a while.

C

Christian Hoffford5:06:40

Absolutely, absolutely.

M

Melissa5:06:41

Okay, super cool. Christian and Stefan, thank you for sharing insights. We have a little gift for you. With this, I'm handing it over to my lovely colleague Militia for the next session. Thank you, Christine.

M

Militia5:07:28

Yes, we are getting on and on with major topics. It's not stopping. Electronics manufacturing is becoming one of the most complex industrial environments. We have to scale things fast, move with precision, and cannot afford mistakes. Today's conversation is about how industrial AI, automation, and strong partnerships come together to make this complexity manageable. I'm very happy to welcome Bobby Mitra, President of AI and CIO of Tata Electronics, and Rhina Bram, COO of Automation Business and CTO of Digital Industries. Welcome, Bobby and Rhina. Let's take a seat. Has it been a long day?

Yes.

B

Bobby Mitra5:08:39

Yes, starting at 7:00 this morning.

R

Rhina Bram5:08:44

I was here a little earlier too. When you see the place empty and suddenly crowding up, you get excited – that's when things are real and you can touch them.

M

Militia5:08:57

So let's talk about the real stuff. Tata Electronics is a young company operating globally. What role has Siemens played in your journey, and what is the value of the partnership?

B

Bobby Mitra5:09:17

Tata Electronics is a newer company of the Tata group, focused on manufacturing – both electronics and semiconductor. We are building our fab and OSAT, so we have an end-to-end chain. We conceive every manufacturing problem from an AI-first lens. Regarding Siemens, we've been using Siemens MES, Op Center, PLM, Teamcenter, and modeling. That's why Siemens is exciting – you have modeling, PLM, MES, and more in one place.

M

Militia5:10:23

You have it all in one place and build it right from the start. Rhina, tell us from Siemens' perspective, what makes the collaboration with Tata Electronics valuable?

R

Rhina Bram5:10:35

Three topics. First, we think in digital threads – how to design, simulate, and manufacture. Tata Electronics is one of the few companies that can fulfill the entire digital thread from silicon to product. Having that on a common data basis is exciting. Second, India is a growing market with significant investment from the government in semiconductor mission. Third, Tata Electronics is a startup on scale starting with AI-first. How can we support that vision with our technology?

M

Militia5:12:22

Amazing. Looking ahead, what fundamental changes do we see in electronics manufacturing? Bobby, let's start with you.

B

Bobby Mitra5:12:35

Many things are changing. Why does it need to change? Tata Electronics has both greenfield and brownfield factories across electronics and semiconductor. We need to build fast, ramp fast, and scale fast. That's why things cannot be done as in the past. What changes? Earlier, MES, ERP, PLM, and maintenance systems were separate. Now, with an AI-first approach, you don't need to put it all in one data lake. You can thread it through an orchestration layer. Workflows will get redefined. When you see a river flowing from isolated systems into one stream, that's what we need. Rhina, you can share points as well.

R

Rhina Bram5:14:17

What I like is the vision of an autonomous factory. In semiconductor, a control center that autonomously supervises the factory using an MES layer is an interesting view. Also, we are a producer of electronics. We look at boundaries to get more automation in electronic manufacturing. We showcase this with our factory in Alangan, where we apply AI to automate manual tasks. We've seen what we do with Nvidia to build the brain into the control system to automate tasks that were not capable of being automated. There are many manual tasks still in packaging and interlogistics. So we need to increase automation using AI-based technology.

M

Militia5:15:31

We still have a lot to do. Let's stay in the present. What is AI-first manufacturing? Rhina, your point of view?

R

Rhina Bram5:15:53

To go AI-first, you need to think data-first. Without data, you cannot apply AI. But Bobby is better to talk about that. Data contextualizing and optimizing workflows is important. If you get data straight on the context side, you can operate different tools via agents. You will automate entire workflows using orchestrator agents. I believe that's where the future goes, for both greenfield and brownfield.

B

Bobby Mitra5:16:40

AI-first is the motto of Tata Electronics and all Tata group companies. AI-first means when you look at a problem, you conceive it from an AI lens. Digital before physical. It's different from sprinkling AI in defect detection. It's a whole different upside-down approach.

M

Militia5:17:27

We're flipping the coin. So, how do we really unlock technologies like industrial AI and software-defined automation? What needs to change beyond technology? Rhina?

R

Rhina Bram5:17:56

You can do nice technology and proof of concept, but when it hits the shop floor, it needs to be reliable, secure, easy to use, and fulfill a business case. These four must be fulfilled. We treat our own factory as customer zero. If it doesn't get a return, we don't do it. So we need technology that delivers customer value with high reliability. There is great physical AI, but it's not yet reliable enough for my factory manager to take and run with.

M

Militia5:19:03

Now, Bobby.

B

Bobby Mitra5:19:05

I echo Rhina. It has to be business KPI driven. You can get excited with technology and lose focus. Business first, then technology. Also, the value of the ecosystem is critical. Tata Electronics is born global, so we realize the value of partnership.

M

Militia5:19:50

How can Siemens help you become successful on that journey?

B

Bobby Mitra5:19:58

We have the advantage of fab, OSAT, and electronics manufacturing. Across construction facilities and operations, everything needs to be connected. If there's a leak in a pipe during construction, you need to trace it back. Siemens has many pieces that can connect each other and connect up in a smart control room.

M

Militia5:20:54

I love that idea. Electronics manufacturing will be AI-powered, data-connected, and ecosystem-driven. If we summarize for the audience, what is your key message?

R

Rhina Bram5:21:28

Bobby said it's important to build and ramp up. The semiconductor market has huge demand. So ramp up fast with digital technology like digital twin and simulation. Also, how we can support India's mission to become a global hub for semiconductor and electronics.

M

Militia5:22:27

And your final message, Bobby?

B

Bobby Mitra5:22:41

Ramp fast, scale fast. That's the demand. AI-first is a huge opportunity. For example, defect detection: instead of many images, use synthetic AI to generate data. That helps in scaling fast and getting to the right yields quickly.

M

Militia5:24:01

Scale fast, ramp fast, but do it together. That's the message. Isn't this a cool feeling?

R

Rhina Bram5:24:12

Absolutely. This morning it was empty, now it's full. Amazing.

M

Militia5:24:16

A full day today and looking forward to tomorrow being packed.

R

Rhina Bram5:24:20

That's it. We have many topics. Thank you for being with us. Rhina, I love your shoes.

We manufacture over there, that's why I have them.

M

Militia5:24:36

That's the place to be if you want to know how it's manufactured. Visit the booth. Thank you so much for being with us.

B

Bobby Mitra5:24:53

Pleasure.

M

Militia5:24:54

And we always have a present for our partners. I'll hand it over to you in a second. Please move on.

See you in a few seconds. You go with me and then Ali in the center.

Welcome everyone. Good afternoon. I have the luxury of presenting this next panel. With me, I have Cedrik Neike, CEO of Siemens Digital Industries; in the center, Ali Perobi, VP of Supply Chain and Manufacturing; and in the far end, Stu Makuchin, VP of Industrial Metaverse – he's been in the company for 29 years and is responsible for the industrial metaverse strategy.

A

Ali Perobi5:26:04

Bit of noise.

M

Militia5:26:07

So, Ali, welcome to Hannover Messe and to the Siemens booth. Tell us about this project that was presented at CES and now here. What makes it interesting for PepsiCo?

A

Ali Perobi5:26:48

The demand for our product is growing, but our footprint isn't. The goal was to do it digitally first to expand manufacturing into the warehouse, make sure new lines fit, figure out flow and inefficiency before spending capital. That was the main reason. We had food and beverage feeding into the same mix. So we wanted to reduce risk by doing it digitally first.

M

Militia5:27:32

Cedrik, this is not just a digital twin of a line or machine. Why is this a milestone in our industrial metaverse strategy?

C

Cedrik Neike5:27:48

First, thank you to Ali. We have a long-standing relationship with PepsiCo, and we deeply admire what you do – serving billions of customers with billions of data touchpoints. The key question was how to make that better. How do you partner to rethink the fast-moving consumer goods industry? That's why thank you for letting us work with you. What is interesting is we always wanted to do something digitally that is simple. So we built the digital twin composer with Nvidia and PepsiCo as the first user. You can simulate and build scenarios extremely fast. This is what we wanted to drive forward.

M

Militia5:28:45

I think we have a video here.

We have a video, and we have a voiceover. Stuart, who is 29 years old, has built it. Let's roll the video and tell us about what we've done with PepsiCo.

S

Stuart Makuchin5:29:07

This is the application we developed for PepsiCo. It's a single pane of glass. We can look at different plants, the supply chain flow between them, goods in and out. It pulls data from 20 different systems, all managed securely in Teamcenter. You can fly through the current state of the plant, leveraging our NVIDIA partnership. For future states, I change the date and drop a Gatorade line into the plant. I have effectivity. Every aspect has a digital twin – for example, a mixing tank. It's a seamless experience. The full demo is on the stand.

M

Militia5:30:23

Fantastic. Ali, what were the operational challenges you aimed to solve with this?

A

Ali Perobi5:30:35

The biggest challenge was making a lot of decisions fast and getting it right the first time, minimizing investment and mistakes. The best way was digital first. We partnered with Nvidia and Siemens to do it in a scaled way because we plan to do this in many regions. It allowed us to make better decisions faster.

M

Militia5:31:16

Good reason. Can you tell us about the advantages and gains so far?

A

Ali Perobi5:31:30

We identified bottlenecks when adding the new line. We realized we didn't have enough dock doors, so we used an auto truck loading system to speed up pallet output. We optimized it all within 12 weeks. We proved everything fits and flows digitally first. I'm happy to report the project is expanding – they are asking us to add two more lines in the same facility. That's phase two.

M

Militia5:32:16

Stuart, what were the main challenges you faced?

S

Stuart Makuchin5:32:27

The biggest challenge was data. They gave us three simple plants that were 50 years old. There was no 3D data; data was in silos, some not even electronic. Pulling that together was the biggest challenge. Once we had that, we could build the application and prove value.

C

Cedrik Neike5:32:42

That data, we scanned the plants, we converted those to 3D models. We then use those to simulate physics-based simulation and optimization of as-is and 2D state. And this all had to be in a managed environment. And then just on top of that, we gave ourselves 10 weeks to do it. It's a lot of technology just working together seamlessly.

H

Host5:33:07

From physical to digital, from digital to physical again as many times as needed.

So that's fascinating and electric, but PepsiCo is not a small company. They have more than one plant, more than...

C

Cedrik Neike5:33:18

They have 300 plants. 300 plants. Yeah, exactly.

H

Host5:33:22

So, what are the challenges we had to face to scale this?

C

Cedrik Neike5:33:25

I mean, look, you have to first prove that it works, and I think we proved it, right? The amount of capex to redeploy the lines, the idea was how do I do with less footprint and more flexible environment. So the first thing was, can I actually make this profitable? And I think it's 20% throughput increase, 15% less capex. It's actually a great view to do it. The second thing which is great because it's modular, right? It's really the capability to put these things together. We can now apply it to other plants and expand it. And that's what we're really going for. And this digital twin composer is something we developed with Nvidia and which really enables us to do this, and you're the first one we're deploying it with. We hadn't expected that you would move so fast, and we have been moving at that speed with you. Now the question is, can we actually move it to the rest of the environment? And then there are certain really cool things you could do. I mean, you have your Gatorade with you. Imagine that there's a big football game or a big soccer game, wherever country you're from. What happens is that for drinks there's a spike. So that could be, I don't know, a Pepsi Max Mango, and we need to build this as soon and as close as possible. We build up the capacity and then ramp it back down again. And with that digital twin composer, we can do it. We can reconfigure the supply chain and we can reconfigure the environment. So PepsiCo is cool because they're fast. They give us tough problems. They're scalable and they're looking into the future to be even more adaptable. And these are making us the ideal partner. Exactly. Going in that direction. Manali, I don't know if you want to if I said everything correctly or if you want to correct me.

M

Manali5:35:05

Digital composer allows us to streamline the workflow. So we are one of the first, I understand, to use it, and yeah, you can kind of physically see the decisions you make and how they materialize all in the digital world before you actually go to physical. So that's a big advantage.

H

Host5:35:28

Ali, just one for you. We know that you are very aggressive in the way you want to deploy this technology across the different sites. But what's going to be the next plans for you? What do you have in mind?

A

Ali5:35:40

So our goal this year is to scale this capability across not only North America but LATAM, EMIA, and Asia. Ever since the announcement at CES, all of our country GMs are asking for this capability because they have plants and they want to test it in digital first. So our goal is to not only scale it but to show value right away. Show value and do it often.

H

Host5:36:10

And that's how we're going to scale this.

A

Ali5:36:12

Absolutely.

H

Host5:36:13

With the support of Siemens and other ecosystem partners.

And Stuart, Su, you've been in the industrial metaverse since the beginning. So you're one of the executives pushing for this technology, one of the visionaries. We have a lot of visionaries inside Siemens. But we have been one of the ones that we...

S

Stuart5:36:30

We're visionaries and we're executioners, and you have both. So you actually make things happen, which is very important.

H

Host5:36:36

What's next? What's the next thing that we should expect in the industrial metaverse from...

S

Stuart5:36:40

Well, I think this world is accelerating, right? I think digital twins are going to get more comprehensive. You're going to be able to do physics-based simulation of every aspect to give you insights into products. AI, we're scratching the surface of what AI does. AI is really going to help us deliver this at scale and really accelerate it. And so there are all these multitude of new use cases. I could go on. There's around humanoid robots, virtual commissioning. We're envisaging all these new use cases that we haven't even thought about because once you bring all this information together using our digital twin fabric, AI fabric into a single pane of glass, it enables so many new things.

H

Host5:37:25

Yeah. And the last one for Cedrik, I'd like you to use your crystal ball and not just for this industry. We're explaining how this industrial metaverse can change the food and beverage industry. But how do you see industrial metaverse impacting the industry in two or three years?

C

Cedrik Neike5:37:45

I have to give you a little secret. I am and was a gamer, right? And I love playing Sim City. I love playing also the more sort of interactive, we call them, cannot call them shooter, but real-time 3D sort of first person. And the dream was always, could we make our industry as fast, as capable, and as much capable of looking into the future as anything else? And we have achieved that. And I think what we really have done is we draw these ultra-realistic 3D perspective with the capability to simulate. Because what we do is actually we call it shift left. We shift the problem from solving it in the real world into the digital world. And in the digital world, you're not constrained by brick and mortar. You're constrained by how much compute power you throw at a problem. And it's been one of the most exciting partnerships, one of the most exciting capabilities we have because we've shown how fast this is possible. And I don't think we've had a lounge which has generated more interest in a short period of time. We have 350 customers who are wanting to work with us at the moment, and we have blocked the rest because we said we have to consume that. Pepsi being our absolute number one. You're our number one and we're working with you. But this has really hit a nerve. And I think that for us to actually do exactly what we have is to understand the real world, build the digital world, have the digital twin, accelerate them with AI, and really have a bottom-line impact is something which is super powerful. And I have to say we couldn't have done it without you guys, Stuart, and definitely a customer which is pushing us. But this is the new standard, and you should have a look at it because I think it's super impressive.

H

Host5:39:24

Mhm.

Thank you very much, gentlemen. Just to close this panel, it's been a luxury having you three. I would like to encourage you just to step by the CPG booth. We have an amazing demonstration of a pop-up factory also done in partnership with PepsiCo that shows how we can do a fully automated lights-out factory running with AI. So that's a really interesting thing to watch, but also you have all the projects we've done with PepsiCo. Just thanks so much. We love having customers like PepsiCo that push us, that let us show all our technology, and I wish the rest of the audience an amazing Hannover Messe.

U

Unknown5:40:07

Thanks so much.

Thank you. More to come.

Thanks. Thank you very much. Well done.

N

Narrator5:40:24

Welcome to the new industrial revolution. An era where data drives decisions. Where systems become adaptive, autonomous, alive. This is starting today, paving the way for tomorrow. Industrial AI is built on three key pillars: technology stack, domain know-how, strong partnerships. Let's see it in action. A new order comes in. Our production line needs to adjust. 'We've got a new variant on line three. Can you simulate the changeover?' 'Simulation complete. Changeover feasible. Estimated downtime 6 minutes.' 'Optimize for energy use and keep output stable.' 'Optimization complete. Parameters adjusted. System ready for deployment.' With just a few prompts, highly adaptive production with customized products becomes possible. This is industrial AI in action. Technology stack: software enhanced by AI, hardware that connects, compute to perform at the highest level, and data to tie it all together forms the foundation. Domain know-how ensures that AI understands the reality of industry, and strong partnerships make it scalable. Together they bring the real and digital worlds together. This is how Siemens is accelerating the industrial AI revolution with real solutions, delivering real impact in the real world. Get ready for the industrial AI revolution. Technology to transform the everyday. Siemens.

H

Host25:42:41

From food and beverage. We're talking PepsiCo. We're talking about so many things, but right now we're talking FFT. It digitizes products. But what is it? What is FFT about and who are they? I'll ask Matias. He's the marketing portfolio responsible for virtual PSC PLC at Siemens. Join me, please.

Thank you very much, Matias.

Matias, maybe you first tell us who you are, what you're doing so that everybody knows.

M

Matias5:43:10

So this is an introduction of our customer who digitized his production lines, and it's a very interesting topic for all of you guys.

H

Host25:43:19

Wonderful. So I'll hand over to you and we'll welcome Robert Vinta, product owner for virtual PLC at Siemens, Thomas Ganau, product portfolio manager also at Siemens, and Dominic Pink, head of corporate center digital factory that is FFT. Now I'm going to jump off the stage in a second, but you take your seat.

M

Matias5:43:45

And here comes the relaxing moment. Yeah.

H

Host25:43:48

FFT. We talk about FFT as in foundational technologies, but please tell us what does FFT stand for? I love the word. Listen,

D

Dominic Pink5:43:55

FFT means flex.

H

Host25:43:58

I'm going to... This is going to stay with me. Please, the stage is yours.

M

Matias5:44:02

Thank you very much. Take a seat, guys. Dominic, thank you for taking the time today here to give us cool insights about your experiences with the virtual PLC. And my question to you is, what was your vision? What was the reason that you decided to take this new path?

D

Dominic Pink5:44:22

So, thanks a lot for the warm welcome also on stage. I have to think about first how can we bring more data to the shop floor and then of course to the cloud. So let me first explain what it means to have the right data at the right time available that you can use for example AI as a multiplier, because first of all we think data is the foundation of everything. So that means how was our thinking regarding this topic? Of course we have the classical infrastructure in our cases, so that means we have the controls layer there with the PLC inside with all the IOs, with all the sensors, maybe intelligent, maybe not intelligent, but this is the normal control layer. And then we are talking about a revolution because we think about how can we bring a data-driven automation layer to the shop floor. So that means how we think about it is we are bringing the DDAP layer into the shop floor and bringing all the stuff out of the PLC to another layer. So in this case, does it mean that we are of course orchestrating data? If you want to connect to any other PLCs, if we want to connect for example with the FFT data bridge to the cloud, that we can also have the issues that we needed a separated thing, and the VPLC was in this case the best topic which we are going to get because we always need the connectors to the PLC, we always have the northbound connection and the orchestration layer. And our opinion was if we only have a northbound connection, how can we transfer data from left to right? So that means from east to west, west to east, and so on and so forth. So this was our idea how we can bring automation to another layer, and this is how we do it with the data-driven automation layer. So in our case, the software was already there. Siemens was always talking about software-defined automation, and we thought about how can we now bring the software also to the hardware back again, and this was a special thing which we developed together. Yes.

M

Matias5:46:49

Okay. That's a really cool forward-thinking approach and your mindset is definitely clear. You don't call it a dream, you call it reality. And that brings me to my questions to Robert. Robert, how could we as Siemens help to make this a reality?

R

Robert Vinta5:47:09

Yeah, thanks Dominic and thanks Matias for the info and the challenges. Yes, it's already a reality and we have it already running and available. So before going into details, let me give a short glimpse where the VPLC is coming from and why we are doing a virtual PLC. With a virtual PLC, we brought a hardware PLC or the software of a hardware PLC into a virtual IT environment and have all the benefits of scalability, flexibility, and future-proofing. Up to now, we did it with a hypervisor, especially for line builders. But we also have a new variant called Industrial Edge on device, where you can run the virtual PLC alongside WinCC Unified and other apps on one device without a hypervisor, reducing complexity and cost. We achieved running all apps on one edge device, bringing data from field IO to the VPLC and other apps, and to higher layers. So yes, we have the solution ready and available.

M

Matias5:49:09

Thank you, Robert, you gave us exciting insights regarding the controller part with the virtual PLC. Now let's talk about visualization. Thomas, how can WinCC Unified contribute to the vision of FFT?

T

Thomas Ganau5:49:27

The apps VPLC and WinCC Unified for Industrial Edge are a perfect match for FFT because they cover all challenges regarding operation and visualization. The industrial edge environment gives us the possibility for data-driven production with scalability, flexibility, simplified maintenance, and full expandability. Industrial edge devices can run every single app, including VPLC and WinCC Unified, on one device. They can be hardware or virtual, providing high scalability. Maintenance is simplified through central management, and it's a secure environment. WinCC Unified for Industrial Edge is a full-blown HMI solution that works perfectly with VPLC, with integrated functionalities like data logging and process diagnosis. It can also communicate with the industrial information hub to expand with any app running on the edge device. That's how we contribute to FFT's vision.

M

Matias5:51:39

Thank you, Thomas, for explaining that. With WinCC Unified, we have a great solution for visualization. And back to you, Dominic, your vision has become a reality. With our products, you were able to turn your vision into reality. The question to you is, how useful was the product from Siemens in this case?

D

Dominic Pink5:52:04

Yeah, in this case it was awesome because the hardware was the limiting factor, and now we had the chance to take the hardware out of the controls layer into a separated layer. So we don't have a control layer anymore; we shifted it to an upper level. We have the IO layer, and we were able to transfer data, analyze it, and make it data-driven automation. That was a key enabler because we are fully connected, no need for multiple connectors. It was very easy. We thought about data-driven automation: do we really need hardcoded software? For example, when I came to Hannover, I typed into my car 'I want to go to Hannover from Fulda' and it made the path for me. I only want to travel to the Hannover Messe, and how I get there is not necessary. So we changed our function blocks to have intelligent modules that interpret data and make output, transferring data left to right, not just north-south. We break through classical controls and talk about no-code automation. We only have data through function blocks, making automation more efficient.

M

Matias5:54:21

Okay, that sounds amazing. And for this, thank you very much, guys. A special thanks to you, Dominic, for this exciting insight into your data-driven production. As you can see, it's not just a solution, it's a world of possibilities: open architecture, flexible in application, scalable for whatever the future brings. This is how future-proof production is graded: not complicated, but smart, connected, and ready to grow. And this brings me to us. If you like to find out more or have specific questions, please visit us here in the smart manufacturing area. Let's take the next step towards the digital industry together. Thank you very much for your attention. And thank you very much, guys.

N

Narrator5:55:38

I could lift you up. I can show you what you want to see. Take you where you want to be. And we want to do these two objectives by the way while we leverage the power of AI. You need to create the digital twin of the product within the factory. So if you don't subscribe for industrial AI, you will be left behind. And you see companies such as Amazon deployed more than a million robots in our fulfillment centers, driving innovation and reducing cost and reducing speed. Physical AI is here now because it offers these huge opportunities. Usually the answer to this is industrial AI because we can of course improve by 30% heating and ventilation within a building. I mean we're always looking at new ways to innovate together to serve our customers. So, this is a coffee bar that needed to be able to be flooded and still keep working. We knew we could make something that was more durable while it was more beautiful. We were building the world's first hurricane proof coffee bar. What's great about Hattie is it has so many use cases across so many of the things that we as designers get to bring to life. It could be a monster door straight for Monstropolis. It could be as practical as a piece of show set for an entertainment offering like the King Louie platform that they've built for our entertainment partners. Or we're trying to develop rock work for our parks. But it could also be a piece of furniture that we put in thousands of hotel rooms at Walt Disney World. One of the most special parts about polymer 3D printing is we can take material that was something else before and now is used to turn into something else. You can take a water bottle and turn it into a chair and then you can take a chair and turn it into a water bottle. What Hattie does so well is we take digital instructions that came from a human and we turn them straight into an object that is very efficient, very repeatable, very accurate and allows you to move very quickly in iterations. The entire platform of NX is really helpful because it allows us to stay in one environment where we can perform our analysis and get our geometries correct with 3D printing. And because the system also tracks material usage and energy consumption, we shouldn't have any waste whatsoever. Patty is a 3D printing world. When we look at it, we've been in partnership with Siemens from the beginning to solve the most difficult challenges. When people walk into our microfactory, what they will see is they will see lines of robots in cells. Each Flexbot has the potential in the future to collaborate. This means it gives us ultimate flexibility within the cell and without the cell. As Hadti reimagines manufacturing through intelligent distributed microfactories, the future depends on environments that can adapt as dynamically as the production inside them. Autonomous buildings become part of a system continuously optimizing energy operations and conditions around people and processes. With Building X, this kind of adaptive infrastructure makes it possible for new manufacturing models like Hadtis to scale efficiently and sustainably.

H

Host36:00:00

All right, I hope you're all doing good. And now, as I've been saying the whole day, and I hope I'm not monotonous, it's all about partnerships. And when we speak about partnerships, it's this time about Siemens and AWS. The topic that we're talking about right now is quite the topic that we go across manufacturing, and I love to say it's one of the biggest challenges that we're all undergoing right now. We heard we need to connect the dots from design to production. How do you do that? I think the next session is going to be exactly that, which answers the questions. It's about digital threads and connecting exactly those dots. Before I was saying, hey, those threads feel like needles going through and weaving through. I think this is exactly what we're about to talk about: turning product lifecycle and data into something that actually works. So I'm very happy to welcome Tony, CEO of Digital Industry Software. Tony, will you join me? And Esa, GM, Automotive and Manufacturing at AWS.

Great to have you.

T

Tony6:01:17

Thank you very much.

H

Host36:01:18

Wait. Take it away, guys.

T

Tony6:01:21

Thank you, Oscar, for being here with me. I guess our job, or my job, is to talk a little bit about what we're doing with our customers and what we're doing with AWS and how we're going through the process.

So I'll start with just talking about our customers.

Look, we all know there's a lot of change and complexity with our customers. How do you use that complexity as a competitive advantage? How do you move faster? We do that with a strategy built on three components. First, the comprehensive digital twin: how close we can make the virtual to the real, representing electronics, software, manufacturing, mechanical design in one digital twin. Second, life cycle intelligence: AI requires trusted data. Our customers go to Team Center, the most proven PLM tool. But 50% of SMBs store data on file systems. So we created Team Center X, running on AWS, to bring structured data to any size company. Third, adaptive: our solutions work the same in cloud or desktop, same data format, no revolution when upgrading.

Once we have that, we talk about AI. We have faster engines, faster engineers, and design intelligence. For example, we created physics AI to create surrogate models, making decisions up to 200 times faster for structural analysis, and a thousand times faster for multiphysics. We also have SimSolid, which democratizes CAE by going directly from 3D model to meshing and analysis, 30 times faster, without needing an expert.

We put all this into our AI fabric. It's not enough to throw data into a data lake because it's a snapshot in time. Engineering changes constantly. We need data as close to real time. Also, we need to understand design intent and configuration. Team Center manages configurations, like 300,000 variants in a vehicle. Security is managed at the attribute level. If you copy to a data lake, you lose that. We keep data current, powered by AWS, and can link to other data. You can see this in our CPG area.

Again, all of that we've built is to handle these complex problems for our customers and scale it. We scaled that by leveraging AWS. So I'll let Oscar talk a little bit about what we're doing here.

O

Oscar6:06:12

Thank you, Tony, for having me as part of this session. Really a pleasure to be here. I think Tony set it up beautifully. We come to this event, and in the last few years AI is at center stage. One of the things we focused on this year is how to scale AI in an industrial way. I'll give you some pillars and principles. From a partnership perspective, the primary partnership we have across manufacturing to scale AI is through the partnership with Siemens, so we're super grateful for that.

When we think about this space, there are three pillars. First, data foundation: is your data AI-ready to support enterprise-wide scaling? Second, freedom to innovate: how do we give manufacturers, from large to small, the freedom to innovate across a fragmented landscape? Third, building trust to scale AI efforts.

Talking about data foundation, historically manufacturers have had disconnected data from shop floor, sensors, PLM, MES, ERP. Together with Siemens, we moved the Siemens accelerator software portfolio to AWS Team Center X to build cloud-based systems that unify data. When you unify data, you can build a common foundation to leverage full-scale AI. Many customers are now reviewing their data architectures to see if they are right for AI at scale.

The second pillar is freedom to innovate. We take this seriously at AWS and with Siemens. It's about open standards supporting industry-wide adoption. We give customers choice through Bedrock for foundation models, and we provide primitives that allow them to build what they want, from mega platforms to small use cases. It's about supporting innovation and freedom to build.

The third pillar is trust. Reports show that 85% of AI applications are stuck in pilot. Technology is not the reason; it's organization, process, people, and trust. Building trust across the organization, introducing human in the loop, and putting the right guardrails in cloud and AI infrastructure are crucial to ensure the output is correct and to make corrections.

Talking about trust, as we go into agentic systems, it becomes even more relevant. A few years ago we talked about chatbots, then agents, now agentic systems. There's much discussion about orchestration of agentic systems on the shop floor: how robots, cobots, humans, and future humanoids interact. How do we build trust in the orchestration layer with partners like Siemens to allow these systems to scale?

A great example we worked on with Siemens is with PepsiCo, leveraging the digital twin composer, Siemens products on Nvidia Omniverse and GPUs on AWS. This delivered a 20% increase in throughput and 10+% capacity utilization by creating a photorealistic image of the physical environment to identify idle capacity. So maybe I turn it over to you, Tony, for final words.

T

Tony6:12:52

I would just say, if you remember one thing from our talk, executives often ask where to start with AI. First, you need data you can trust. Team Center is where our customers go for reliable data. I was at an executive event where the CIO of Applied Materials said they trust Team Center; they don't want AI agents going to old SharePoint files. A PepsiCo colleague said if they did that, their factory would burn down because old data doesn't apply. So we show that with Team Center, you can structure data reliably. We bring it to any size organization via Team Center X on AWS, allowing you to start efficiently with few users. Copying to a data lake is a snapshot in time; you need real-time data with configuration, context, design intent, and security. This is a product, not a project, and we have unique ways to do this with AWS.

Thank you so much for the conversation. Thank you, Oscar, for the partnership. Thank you all. Thank you.

H

Host36:15:32

Thanks to Tony and Oscar. Great stories being told, great achievements we realized. Looking forward to hearing more of those projects.

T

Tony6:15:43

Thank you.

H

Host36:15:43

You're a busy man, great that you shared your stories. Thanks Tony and Oscar. They get gifts from Natura. We have a bit of time left. Next session will start at 20:3. Here at AWS with partner presentations: Accenture, Nvidia, Capgemini, Microsoft, many partners. This is the place to be for innovation and AI in production. Every year at Hannover Messe we showcase best examples, customer success stories, and partner ecosystem innovations. We're in Hall 27 for the first time, with great neighbors like DMG Mori. We have 55 exhibits with machines and robot arms. Sustainability and circularity are core topics. Brazil is partner country this year, with huge potential. Let's get some inspiration.

H

Host46:18:59

I don't know about you, but I get the feeling I want to fly to Brazil after that video. There are many Brazilian colleagues and customers here. Pablo Favo was on stage talking about success stories in Brazil: Axia Energia, Agaria, CNM, Embraer. We have much to explore at this booth, even glasses to smell the Amazon. Stay curious. We're live streaming to Siemens corporate channel and LinkedIn live. Welcome to YouTube community. This is the place to be. Now it's time to kick off our session about technology with purpose and strengthening communities respecting the Amazon.

It's not every day that I get to learn a new word in Tupi, one of the indigenous language families in the Amazon. The word 'moodi' stands for mutual help. That's exactly what we're going to talk about: how Siemens brings help to our customer and helps families in the Amazon. Allow me to introduce the persons best to speak for their project: Judith Viz and Charmaine Love.

You got a fact out here. This is wonderful. The food is packed. Awesome. Dude back on stage. Wonderful having you. Great you're here. Thank you. Take a seat right here, right underneath your profile picture on the LED.

Great you're joining us. First of all, thank you, Charmaine, for those wonderful gifts you sponsored. Every external speaker gets those gifts, and they're happy. Thank you for that kind gesture supporting our stage activities. A little treat from Brazil. I'm glad people are enjoying them.

Very exclusive. So thank you so much. I'm going to do a quick introduction. We have Judith Wiese, member of the managing board at Siemens, chief sustainability officer, and head of people and organization. Thank you, Judith, for coming. And we have the global ambassador for Natura, Charmaine Love. I love your last name. It brings fun and emotion to the topic. Most panels here are about deep core technology and large-scale industrial automation and AI. But we're discussing something very different: the digital transformation of a small set of communities in the Amazon rainforest. I'll start with you, Judith. How did this project come about? How did Siemens get involved?

J

Judith Wiese6:23:56

Well, I think it's the magic of a long-standing customer relationship we have with Natura for the last 15 years. Natura has deeply rooted knowledge and connection with the Amazon communities for over 20 years. And COP was the trigger point to say how can we as Siemens truly bring technology to Amazonia.

H

Host46:24:26

Sorry, if I may just introduce: COP 30, the Conference of the Parties, last year end of November.

J

Judith Wiese6:24:32

Yes, exactly. Thank you for that. So there were a number of things we wanted to do to bring technology to the Amazon. In this particular case, what makes it so special is that we're partnering with Natura, who know this so much better and have long-standing relationships. It's about preserving the tradition of the Amazon communities and their knowledge of nature, and marrying that with technology, like Industry 4.0 digitalization. The scale of this initiative is small, but it has the opportunity to scale with our partner. We wanted to set an example of technology with purpose, cherishing nature that is important for the world, the lung of the world, with regenerative practices infused by modern technology, lifting up the community in skills and prosperity.

H

Host46:25:53

Yeah.

And from your perspective, how do you see that collaboration amongst us?

C

Charmaine Love6:26:00

Well, I think this has been a beautiful partnership since the very beginning, and I think it's because we have a sense of mutual respect. Natura is a Brazilian-based company, we've been around for a long time operating in the region. In the Amazon, we've been working for 25 plus years cultivating these relationships. We also have a real commitment to the forest. We have already worked in partnership with others to help support and conserve 2.2 million hectares of the Amazon.

N

Natura Representative6:26:32

The Amazon forest is a huge part of our business. We are close to the forest and protect it, because our factories and operations are in the region of Bame. We also have relationships with traditional communities and indigenous peoples, supplying ingredients for our products. This partnership with Siemens is grounded in symbiosis, connecting people, forest, environment, and business. The sociobioeconomy creates economic models that keep the forest and communities thriving while being viable. It's a natural partnership, values-aligned and deeply committed to the people.

H

Host6:28:32

Wonderful. Thank you. So what kind of ground challenges do these essential oil communities face in the Amazon? Let’s visit APROM comp, two hours outside Bame. It’s rich in tradition, run by women, led by Josie. I visited the agro-industry facility in 2024. They gave candles with scents from harvested ingredients, and in Portuguese it says, 'There are days that mark your soul.' Visiting that community marked my soul. The traditional steam-powered extraction process is analog, labor-intensive, and depends on operator intuition. We saw an opportunity to increase yields and efficiency in water and energy use. How can we modernize this while maintaining tradition? We were thrilled to connect with Siemens, because the more efficient we make those communities, the more value stays there.

So how did we get involved, Judith? How did it start?

J

Judith6:31:08

A lot is preserved by tradition, based on intuition. Technology can make it more repeatable and better, building on what’s there but more reliable and efficient. Even small operations can benefit. We created a digital twin of the extraction vats, managing steam flow, temperature, and pressure. We halved water and energy consumption, lowered and remotely monitored pressure, and improved safety. This also raised the community’s skills. It allows a regenerative approach and lifts prosperity. Sustainability is not just about the forest; it’s about lasting impact with people having agency and tools through a small investment that can be scaled.

H

Host6:32:54

When we talk technology, we think of large industrial projects, especially with Siemens. How were you able to introduce these new technologies to traditional communities? I imagine convincing them to try something very different was not easy.

J

Judith6:33:22

Working in co-creation with local communities is critical. Agency is key — making sure they have power and control. It’s a triangle partnership among Siemens, Natura, and the APOCMPE community. We blend artificial intelligence with ancestral intelligence. We learn from traditional processes, train on new technologies, and ensure operational control stays with the community.

H

Host6:34:41

So the benefits are rainforest, people, and business. Still, we’re talking about a small cooperative deep in the Amazon. Can a project this size truly apply to a global technology company like Siemens?

J

Judith6:35:17

There’s no contradiction. We work with large customers like Natura and many SMEs. If you scale and replicate insights, you have a model that scales. We already partner with Natura on larger operations — we’ve done extraction for 15 years. Now we’re going deeper into R&D, recipes, supply chain. We can scale what we’ve learned to other communities, and there are many ways to do so.

H

Host6:36:52

So I think there are many ways to scale. Any plans to apply these lessons learned elsewhere and what’s next for us?

S

Siemens Representative6:37:04

You’re talking to me now. We’ve got some other examples of technology for Amazonia, like nanofactories — containerized mini factories that can be brought to the Amazon. We’re working with an NGO to protect seedlings for reforestation, decreasing mortality from 40% to 2%. We also use UV and visual inspection to detect contamination of nuts. These same principles can apply to different places. We’re also involved in education through the Siemens Foundation and local institutions.

H

Host6:39:08

Wonderful. Any plans from your side in leveraging this knowledge?

N

Natura Representative6:39:12

Yes. We’re expanding our innovation center to create a Silicon Valley for the sociobioeconomy in the region. We want to scale these agro-industries and nanofactories, helping communities capture more value and maintain agency. I’m excited about Amazon 4.0, bringing together technology with the wisdom of these communities.

H

Host6:40:18

Wonderful. And Judith, you mentioned the VR goggles this morning. Have you tried them?

J

Judith6:40:31

I’m going immediately after this panel, but I’ve already visited the communities in person. I encourage everyone interested in the sociobioeconomy to find ways to experience it.

H

Host6:40:53

I just thought if you get homesick, you can go there. Thank you, ladies, for the insights. Great story. See for yourself — smell the Amazon. Thank you, Judith and Chian. Goodbye to the LinkedIn live community.

N

Natura Representative6:41:37

Thank you. Oh, I see you displaying the lovely products. They smell amazing. This is all about partnership. And speaking of which, we highlight another partnership — Freezeland Company. How do brands like PepsiCo and Pringles ensure texture, moisture, and quality every time? Spray drying is sensitive and energy-intensive. Industrial AI brings real-time stability. I’m pleased to welcome Joanna from Siemens and Mark from Freezeland Campaign.

H

Host2 (Melia)6:42:45

Hi, great to have you. Let’s take a seat. Before we start, look around you. How do you explain what you’re seeing right now?

M

Mark6:42:59

Intense and amazing. The booth looks great. I’ve been here last year, and it looks amazing.

H

Host2 (Melia)6:43:06

This is my first time here, so I’m overwhelmed. Mark, can you tell us more about Freezeland? What do you do?

M

Mark6:43:19

We make dairy and cheese, but a lot of our products go to professional bakeries and specialized nutrition — infant formula, medicinal nutrition, pharmaceutical products. I’m proud of what we make from grass to glass.

H

Host2 (Melia)6:43:56

It’s about changing lives. Joanna, Siemens is leading an industrial AI revolution. What does that mean for spray drying?

J

Joanna6:44:20

Industrial AI combines real-time data with proven process models to improve operations daily. For spray drying, it’s very important in CPG and dairy because it’s energy intensive, has tight quality specs, and requires experienced operators. We bring real-time optimization to improve the process at Freezeland.

H

Host2 (Melia)6:45:03

Mark, why is the process so important for Freezeland?

M

Mark6:45:08

Spray drying is complex. For products like infant formula, we need full control. Variables affect quality, and external factors like air humidity impact the process. Even small changes can push product out of spec. We can’t afford waste — a pallet can be worth 70k.

H

Host2 (Melia)6:46:38

Joanna, what caught your technical eye when you analyzed the process?

J

Joanna6:46:50

We saw clear optimization potential: increase yield, decrease energy cost, and control moisture better. Operators can do it if experienced, but disturbances make an optimization layer very useful.

H

Host2 (Melia)6:47:24

Let’s talk about the spray dryer optimizer.

J

Joanna6:47:32

The spray dryer optimizer is advanced process control that sits on top of the control system. It looks at multiple variables around the dryer and gives recommendations or acts in closed loop. Behind it is a digital twin with a science-based model strong in predictive capability.

H

Host2 (Melia)6:48:16

Mark, operators need trust. What gives them confidence with a digital twin?

M

Mark6:48:29

People don’t want to let go of control unless they understand what’s happening. The solution provides guardrails and visibility through a simple dashboard.

H

Host2 (Melia)6:49:04

What changed once operators started running it?

M

Mark6:49:28

We saw a more stable process, reduced moisture variability, and lower energy consumption. In one showcase at a specific factory, it generated about 150-200k additional income annually for one spray dryer.

H

Host2 (Melia)6:50:19

Joanna, what measurable effects did you see?

J

Joanna6:50:36

Improvement in yield, better moisture control, and reduced energy costs. We’ve achieved these results and still have more to go.

H

Host2 (Melia)6:51:02

How are you solving these challenges? Working with partners?

J

Joanna6:51:07

In this case, we worked directly with Freezeland Campina, which has the right experts. It’s a full-scale rollout within a joint business plan.

H

Host2 (Melia)6:52:14

Why does this work when many pilot projects don’t?

J

Joanna6:52:27

Trust with operators was key. We involved them from the beginning, gained their trust before handing over the solution.

H

Host2 (Melia)6:53:08

What makes this solution scalable and repeatable across factories?

M

Mark6:53:15

We have a vehicle in the joint business plan, bringing resources together, and we can push it forward on the roadmap. Both Siemens and Freezeland have strong technicians collaborating.

J

Joanna6:53:44

In the joint business plan, we target factory performance improvement with digital twins, process optimization, and real-time monitoring across sites. We already have several spray dryers in the pipeline for implementation.

H

Host2 (Melia)6:54:11

AI is not just a promise anymore. We have scalable examples. Mark, we have a little present for you from Natura. Thank you for being with us.

M

Mark6:54:52

Thank you.

H

Host2 (Melia)6:54:57

We’ll go in this direction. I’ll hand over to my colleague.

H

Host36:55:04

Ladies and gentlemen, how are you doing? Good. It’s great being here at Hana Messi. We have great solutions and technologies, but you also need financing. Our alliance for impact focuses on technology and financing driving manufacturing transformation. From snacks to soda, speed, quality, and flexibility are key. But you need digital intelligence and the right financing model. I’m pleased to welcome Dr. Kevin Thunder from Siemens Financial Services and Mika Lexel from Digital Industries, Automation.

We hear about ONE Tech. How do Siemens and SFS combine to uniquely enable CPG manufacturers to transform? Mika, you want to start?

M

Mika Lexel6:57:49

The software is super comfy. CPG market faces volatility, cost pressure, and regulation. Winners will be those who maintain assets smartly, upgrade, and deploy technologies that free up growth. Siemens is uniquely positioned with an unparalleled technology stack — simulation, AI automation, smart devices — plus financial solutions that allow deployment without emptying the bank.

H

Host36:59:23

Kevin, how does Siemens Financial Services support digital transformation?

K

Kevin Thunder6:59:32

One Tech drives customer value by combining technology and finance. The third element is collaboration — working with the ecosystem and customer to find the right solution. In CPG, that means supplier technology, OEMs, and customers working together.

H

Host37:00:12

Mika, what are the top three automation or digital innovations for CPG lines?

M

Mika Lexel7:00:27

Simulation and digital twin to optimize before existence; AI-enhanced automation for productivity; and MTPs — modular plug-and-produce concepts that save time and capital.

H

Host37:01:36

Kevin, how do your financing models derisk tech adoption?

K

Kevin Thunder7:01:50

We assess credit and asset risk. Siemens technology helps evaluate asset quality and residual value, enabling solutions that preserve liquidity. We also offer innovative models like outcome-based or availability-based financing.

H

Host37:02:57

Share a recent CPG customer success story.

K

Kevin Thunder7:03:18

Freshly, a German dairy producer, needed capacity expansion. With Tetra Pak, we structured a flexible hire purchase that matched their liquidity needs.

H

Host37:04:08

And another example?

M

Mika Lexel7:04:54

Drinkpack, a US contract beverage producer, uses our technology with SFS financing to access state-of-the-art equipment while spending capital on growth.

H

Host37:06:03

What should CPG leaders prioritize in the next 6–12 months?

M

Mika Lexel7:06:29

Start with a clear data strategy. Understand what value you want from data, then combine technologies with financing that doesn’t burden the balance sheet, and use the ecosystem to go the last mile.

K

Kevin Thunder7:07:28

The ecosystem is key. Build strong partnerships to strengthen resilience and unlock AI’s value at an inflection point.

H

Host37:08:16

AI is not years away — it’s six months. What AI technologies are you using in project evaluation?

K

Kevin Thunder7:08:56

AI helps us assess transactions more quickly, screen the market, and make better-informed decisions. It also rethinks processes.

H

Host37:09:20

Thank you. You can find our experts at the booth. Mika, where can we find you?

M

Mika Lexel7:09:43

I think I will stay here.

H

Host37:09:44

Probably in the CPG area. Solutions for Pringles, PepsiCo, and Natura are shown there. VR glasses let you see the factory of the future. Thank you both.

You can dive deeper at our stations. The main stage is still the source of information. Next, we’ll turbocharge drive engineering with DriveSim Engineer. We will hear from Christoph and John from Underwood.

M

Max8:08:12

Well, great example. Thank you for sharing that and for bringing that along. Now, we're slowly approaching the end, but maybe we can share a few key takeaways that our audience should have after this talk. Anil, would you like to go first? What's the most important thing that they should take away?

A

Anil8:08:31

The most important aspect is to have scalability in mind from the beginning. Bet on standardized elements which are available. That will make your life so much easier, especially in the replication and in the scaling phase.

M

Martin8:08:48

From our end, it would be the fact that AI is capable of delivering real value in production today. It is not a matter of if the capabilities exist. It is a matter of how they are handled, deployed, and maintained. I think that moving forward this is going to be one of the big challenges to tackle. But I'm looking forward to tackling it with the support of our technology partners at Siemens.

A

Anil8:09:19

Our portfolio is distributed on multiple dialogue stations. Happy to see that. You can find us after this presentation on dialogue station 180 and we are more than open to discuss details, our experiences, and questions that you have about industrial AI and industrial edge.

M

Martin8:09:43

I will be standing with Anil. So you can find me right there.

M

Max8:09:48

Yeah. Okay. Well wonderful. With that, it's time to close. Can we have a big round of applause please for Martin and Anil? Thank you so much for joining us. And I'm going to hand over to Christine. Join me on the sofa of wisdom at least once. But we're going to put that for Friday with some popcorn maybe.

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Christine8:10:35

Ladies and gentlemen, how are you doing? Are you as exhausted as I am? Or maybe even as excited as I am because we're heading over to our next session with one of our partners, Capgemini. You can't do it alone anymore because the challenges are definitely vast. And we know a lot at Siemens. We have a lot of innovation and technology. But sometimes you need to have clever friends who team up with you and deliver even better solutions. The title of the next presentation is 'From Fragmentation to Flow: How to Deliver Standards-Driven IT/OT Convergence with Operations Software.' Please welcome Mark Hinsbow and Frank Lublnau.

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Frank Lublnau8:12:57

Fragmentation first of all is a reality. It comes from legacy, M&A activities, and plants or organizations making their own decisions. When you want to scale something or improve KPIs, it hurts because you don't have a homogeneous starting point. This fragmentation is not just technical; it's also a decision-making, architectural, and operational question. It's a hurdle to implement at speed and recover in times of uncertainty.

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Christine8:13:44

We've been talking a lot about scalability. When we scale pilots across sites, what typically breaks? You start a lighthouse project and it works, but can you replicate it? Often you cannot because of a different starting point. You don't have an integrated layer. This hurts implementation of new ideas, not just AI but basic improvements. It's not only a technology game; it's also a business transformation that must be thought from the beginning.

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Mark8:15:15

I'll focus on two patterns. First: an extremely heterogeneous landscape because we empower individual sites. There's nothing wrong with that, but having five instances of the same thing is a problem. We've standardized finance and product development, but not the factory floor. Second: treating software as hardware. If you think software can be as unchanged as a big asset, you get trouble with 30-year-old software trying to run agile methods on top of fragmentation.

Step one: get a clear architecture. We have joint reference architectures embedded in digital threads. Step two: use a modular interoperable portfolio. Don't rip and replace everything; start small with the end in mind. Use partnerships to help on the journey.

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Frank Lublnau8:19:18

We help clients enforce standards through collaboration with Siemens. We provide a clear architecture and a modular approach. We also help with rollout, education, and moving from lighthouse projects to best fit. This combines technology and business transformation, making the path more predictable and improving ROI.

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Mark8:21:45

A specific example: Syensqo needed to put new products in market quickly and adapt to regulation changes. With our systems, they saw 70-80% improvement in driving changes and ramping production. It's about quick adaptation to change. Simulate the factory like you simulate a car crash test to get first-time right.

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Frank Lublnau8:22:50

Another example: a client in the space industry moved from project-based to mass production. We integrated a new PLM and MES system with a Siemens-first strategy along the digital thread. This enabled streamlining operations; without it, the fragmented landscape would have made it impossible.

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Christine8:23:34

We got two more minutes. One last question: if an executive does only one thing to move from fragmentation to flow, what should it be?

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Mark8:23:45

Treat your factory the same way as you would treat your product. Build a digital twin. Crash test virtually 100,000 variations of your factory to find maximal output. Link product and factory changes to get to market quickly.

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Christine8:25:02

My takeaways: flow comes from standards, modularity, orchestration, and leadership. Solutions are at our booth in hall 15 and station 113. Thank you Mark and Frank for your insights.

Thank you so much. Big round of applause. And we have presents from Natura, a company from Brazil. Thank you.

And I'll join you as Max will be taking over again. Thank you so much.

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Unknown8:26:43

So for this next session we're going to be switching to German. Feel free to stay for that. After 15 minutes we'll be back in English for the final session. [German session on production zone, wireless communication, edge layer, and digital twin composer.]

And now we are switching back to English. We have reached the final session of the day. How can companies in CPG turn data into value and accelerate from idea to consumer? We'll find out from Svetlina Nicolova after this video.

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Narrator8:41:58

[Video: Industrial AI and digital enterprise for CPG. Topics include data-driven product design, digital twins, knowledge graphs, and examples with Pringles (10% higher capacity), Natura (50% reduction in water and energy), and PepsiCo (90% issue identification before implementation). The solution enables end-to-end data flow and the industrial metaverse.]

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Christine9:01:38

Svetlina, thank you. Amazing keynote. Now we have this interesting cup here. Can you share what that's about?

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Svetlina Nicolova9:01:48

Absolutely. If you go through the digital enterprise showcase and collect stickers, you can win this coffee cup at our welcoming desk. I invite you to talk to our experts.

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Christine9:02:40

Thank you. Now Christine and Militia are joining us. We're going to have a short session. Ladies and gentlemen, thank you for staying. We'll have a happy hour today. Let's start with highlights. Militia, what were your highlights?

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Militia9:03:20

My highlights: AI is here already, shaped by decisions made early. The African proverb: if you want to go fast, go alone; if you want to go far, go together. Gravity building green steel from scratch and Friesland Company improving processes with less waste. Trust is an engineering requirement. Digital comes early or not at all.

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Christine9:04:47

Awesome. Militia, it's your first time at Hanover Messe and you're so passionate. A round of applause.

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Militia9:05:05

Great organization. We're a dream team.

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Christine9:05:09

Handing it over to Max. Highlights of today.

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Max9:05:13

I had a session with someone from the Netherlands about grid operators. With Siemens, the whole country's grid can go green. And shout out to our first timers Izzy and Michael.

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Christine9:06:07

My highlight: I signed up for an Audi test drive with our PLC software. And Natura, the Brazilian cosmetics company sponsoring our sessions. Thank you to our backstage team.

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Max9:07:46

Shall I do customers and you do partners?

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Christine9:07:49

It's all the partners. We got nine.

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Max9:07:53

TeamViewer, Microsoft, Capgemini, Nvidia, AWS, Accenture, Greylogix, FFT, Snowflake, and Zeta.

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Christine9:08:07

That's it. Max will host the partner session tomorrow. Also EMEA and India tech champs will be here.

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Max9:08:36

What about customers?

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Christine9:08:39

Pringles, Coca-Cola, Aerospace Center, Aramco, RWE, PUM Innovation, Bulah, GEA Group, Amazon Robotics. Many more.

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Svetlina Nicolova9:09:21

Most asked question: what is the role of industrial AI? We have hands-on examples like Pringles and the IEN engineering agent.

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Christine9:10:03

Militia, what are you looking forward to tomorrow?

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Militia9:10:07

I'll be in the gallery. Topics with Microsoft Sandy Gupta, Buhler, GEA Group. Autonomous factory and digital twins.

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Christine9:10:42

Wonderful. If you're still wondering about coming to Hanover Hall 27, it's time to go. Cheers. Be back tomorrow. Happy hour now!

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Max9:11:10

Cheers. Bye-bye tomorrow. Bye-bye.

Siemens | Live from Hannover Messe 2026

About Cedrik Neike