Hey everybody. Yeah, you. Hey, hey, hey. Hey. Hey. Hey. Hey. Hey. Hey. Hey. Hey. Heat. Heat.
Welcome to Hanover Mesa 2026. We're streaming live from Hall 27. The place where the digital twin comes alive, where industrial AI finds its perfect playground, where co-pilots take commands and execute flawlessly, and where my co-pilot is standing right next to me also taking commands. He will tell us now more facts about the boost, the setup, and what you will be able to explore here in Hall 27.
Yeah. So, good morning Christine. Good morning, everyone. Yeah, once again, amazing to be here. It's buzzing already. Our booth is divided into three big sections: a digital enterprise showcase focused on consumer packaged goods, a technology deep dive area with four islands (electrification and buildings, smart manufacturing, advanced machine engineering, accelerated product design and system engineering), and an innovation hub with physical AI, robots, AGVs, and AI-powered shoe sole production. We also have a gallery studio and a main stage.
There are 260 speakers over five days, including partners like Accenture, AWS, Microsoft, Nvidia, Capgemini. Customers will share success stories on less energy, less waste, first-time right production. The German chancellor and Brazilian president Lula are visiting with a delegation.
The Brazilian delegation highlighted their partnership with Siemens, mentioning projects in Rio de Janeiro and Santos, and the importance of the Brazilian market for industry and mining.
Brazil was the first partner country 45 years ago. Siemens installed the first telegraph line to Brazil 158 years ago. Brazilian customers like Embraer, Nura, Axa Energia, Agraria, and Meatronic are at the booth. We are handing over to Militia.
Hey, thank you. Welcome everybody. This is very exciting. We just saw the chancellor walk past. I'm a little nervous. We're bringing industry, government, and politics together. I'll be hosting sessions on cyber security. Let's hear from our running reporters Izzy and Miki.
Thank you, Militia. I'm excited about the innovation hub and how Siemens is optimizing production with digital twins. What are you excited about, Izzy?
This is my first Hanover Messe. The buzz is contagious. We have the CPG stand with partners like Nura, PepsiCo, and Pringles. I'll be talking to them later.
All right, I will take you to our first station: the energy and buildings area. Come with me.
We are waiting for our tour guide. Here you can see areas for additive manufacturing, smart manufacturing, accelerated product development, advanced machine engineering, and electrification and buildings. You can take a tour with our guides. Also, listen to our stage colleagues Christina and Max at the gallery studio.
We're back on the gallery studio with three guests: Christian Scrader, Peter Aman, and Maria Schwartz. Maria, you kick off.
Maria, this is a huge fair. Can you give us insights from a project management perspective?
We start nine months in advance. This year we had many changes. We build around the concept, architecture, and content. We work with partners and customers. Over 100 people work on the core project. It's a wild ride but we are happy with the result.
The organizers have a new concept with three focused exhibition areas. We have a new home in Hall 27. What does it mean for us?
We have a new theme: AI and manufacturing, which fits our strategy. We are in a modern hall with new formats like master classes and center stage. Today we kick off with Roland Bush and Frit Meatson at 11:00.
Let's ask our colleagues from other halls. Peter, you are in Hall 11. How do you like your new living room?
We have an OTC fair concept for the first time. We are an agile team with multiple BUs. We have an OTC stand in t-shirt sizes. We are present with partners turn2x and admin. We focus on hydrogen, making it economical. China is reducing oil imports with renewable hydrogen. We are a partner in digital twin for energy.
Hydrogen is a topic you should not miss. Hall 11 D20.
Christian, share insights on the defense industry.
We are present in the defense production area with our production powerhouse. We address production in defense with a digital thread from design to ready-made product. We have a closed-loop operations based on a drone showcase. Partners include chef machinery, Avilios, and Kongspec Innovation.
Wonderful. We also have a drone at our booth. Max, that's your cue.
We are handing down to Miki. We offer guided tours. If you're online, use the booth navigator. Over to Mickey.
Thank you, Max. With me is Miriam, one of our tour guides. She will tell you about the electrification and buildings area.
I'm Miriam, a business developer and tour guide. Our electrical infrastructure is under pressure. I'll show you our portfolio: blue GIS switch gear (SF6-free), smart switch gear with data collection, electrification X dashboard, Sivacon low voltage switchboard with Ecotech label, and busbar trunking system. We also have DC solutions. Tours start every 30 minutes.
Thank you, Miriam. How do I join a tour?
There's a tour counter at the left end of the booth. No registration needed. Tours every 30 minutes in German and English.
Roughly 30 minutes. We talk about IE, partners, and Brazil as partner country.
This is my first highlight. We did a small tour in the SE area.
That's cool. You can ask for me, but all tour guides are great.
All tour guides are great. We have broad knowledge. I recommend joining any tour.
Have you done this before?
It's my fourth time. I'm proud to work for Siemens.
Can you tell us more about Siemens in this context?
In Europe, fluorinated gases are regulated. We developed clean air technology without SF6. We are ready for more than 2 years.
Energy and sustainability are important with AI. Tell us more about sustainability.
We need to use electricity efficiently and use green energy. The main CO2 footprint is during operation. With green electricity, no CO2 emissions.
Is there another exhibit you are interested in?
The robots are cool, but there's a beer production machine with a digital twin. You can simulate the process.
Thank you for the tour. We head back to colleagues.
Thank you, Miki. Great insights. Next, we talk about Siemens Accelerator. It's our open digital business platform. I hand over to Sebastian and Shen.
Thank you, Max. I'm excited to be here. We have three colleagues: Dirk (head of foundational technologies), Linda (SVP of Siemens Accelerator ecosystem), and Cal (head of research). Let's start with research. Cal, what does research look like?
Research is about looking around the corner and finding innovations to solve customer pain points. We take ideas through multiple stages until they become products.
Dirk, can you shed light on foundational technology?
I grew up in mobile, then cloud, now AI. Foundational technology is not about technology itself but solving customer problems. We provide the right technology so product teams can build faster and cheaper.
Linda, how does Siemens Accelerator transfer support into customer value?
Siemens Accelerator is the digital business platform. It has three pillars: portfolio, ecosystem, and marketplace. We have about 2,000 offerings, 900 from partners.
Great.
Cal, you recently joined from Google. Were you surprised?
I was stunned. At Google we had platform teams. At Siemens we do the same but for physical products. The span of products and the process from innovation to product is unique.
We have domain expertise and are opening up the ecosystem. We partner with everyone to solve customer challenges.
Dirk, how does the end-to-end chain make Siemens different?
Foundational technologies combines four disciplines: user experience, platform elements, developer workforce (10,000 developers), and research. This end-to-end approach is unique.
It provides a lot of value.
We take heavy lifting away so product teams can focus on customer problems. We are the machine room.
I love that analogy. We are proud machine room people.
We also make it easy for partners to work with us through simplified technical governance.
Dirk, how do you decide which research to back?
We work backwards from the customer. We give the environment for sparks to catch fire. It's always about solving problems.
Cal, elaborate on collaboration.
Innovative ideas come from intersection of university research, internal research, and tech companies like Nvidia and Google. We collaborate globally.
What is the single thing you want the audience to remember? Cal?
The most innovative product you'll see next year is being developed now in my labs and will come faster than you expect.
Siemens Accelerator provides ecosystem and marketplace as launch pad for AI and digital assets.
We do this to solve big customer problems. Industrial AI has the biggest impact.
Well put. Thank you. From research to accelerator to the world.
Most questions answered. I'll spend the next days learning more. See you at the booth.
Bye. Thank you. Thank you very much. That was great. Thank you. Thanks. Thank you so much. Stay here for a while. That was amazing.
Now, we have a few minutes left. I wish you fun. Before handing over to Mickey, I want to give more insights into our booth.
Our booth is divided into three sections with Siemens Accelerator integrated. Starting with the CPG showcase, which is about getting products from design to shelf faster. CPG companies face rising costs, regulations, lack of skilled workers. Siemens uses software-defined systems, comprehensive digital twin, and industrial AI. The showcase goes along the entire value chain starting with data. Then integrated lifecycle management solutions like RapidMiner or Team Center make data usable. Enterprise recipe management links specifications to real-world data. Electrification and buildings ensure lab safety. Production comes to life with advanced machine engineering and smart manufacturing, validated with digital twin. Example from customer Buler with milling machine. Industrial co-pilot for engineering helps with modular machine design. Example from GA with separator machine. Module Type Package (MTP) enables plug and produce. Example from Proxies for batch processing. Filling and packaging with connected automation and AI for high-speed quality. Example from Super Track Conveyance with visual inspection co-pilot. Processes like mixing, cleaning, spray drying optimized with digital twin. Supply chain traceability and lifecycle intelligence. Pop-up factory as industrial metaverse, autonomous, modular, flexible, enabling production close to demand, e.g., near World Cup stadium. Three companies showcasing: Pringles using digital twin for dough and can filling, Natura for essential oil extraction, PepsiCo for factory and logistics optimization. Also technology deep dive area with four islands: electrification and buildings, smart manufacturing, advanced machine engineering, accelerated product development. Innovation hub with three sections: flexible products (AI-orchestrated shoe sole production), smart infrastructure (DC technologies, AI orchestrated building), flexible production (industrial foundation models, data fabrics, physical AI). Now handing over to running reporter.
Hello, yes Max, we are at the innovation hub. Next to me is Casten Hoer, VP of additive manufacturing. We are scanning feet to create personalized shoes. Casten, what are we doing here?
Yeah, Michael, let's do it.
So you already have your shoes off. Stand on the scanner. We'll do a pressure map. Red means more pressure. Do you want midsoles softer or harder?
A bit softer, we can tweak it. Now ready to make your personalized product.
Which parts of Siemens portfolio are playing a role here?
Once we switch off, the process starts. It uses MindSphere for orchestration, NX for outer dimensions, Team Center for storage, Simcenter for simulation and optimization. It iterates based on your initial scan.
Which role does it play in the production process?
Once we have a digital product stored in Team Center, we need to produce it. Mass customization at scale is now possible with robots, AI agents, and the full software stack from Siemens. We have an AOS machine to produce it.
This can be applied to more than shoe soles?
Yes, mass customization at scale can be applied to medical implants, eyewear, helmets, automotive, electronics, mold and die.
Thank you, Casten. If you want your own foot scan and learn more, head to the innovation hub.
Thank you, Miki. Thank you, Ken. That looks great. Now up next, connecting engineering to impact. Products, machines, plants becoming complex. Success driven by seamless data flow. Found in technology deep dive area. Handing over to David.
Thank you Max. The number 35,000 is the decisions we take every day. Information is key. In industry, data is fragmented. Digital threads are like itineraries for digital transformation. Examples: systems engineering and accelerated product development (40% flying range, 20% faster design, 50% faster time to market), advanced machine engineering (10% shorter design, 30% less engineering time, 50% shorter commissioning), smart manufacturing (end-to-end virtual verification, adaptive automation), electrification and buildings (reduce CO2, power consumption). Visit the tech deep dive area with 100 stations and 200 experts.
Thank you David. What's your role?
I've been responsible for the digital threads area for five months and I'm the area speaker for manufacturing. Happy to give tours.
Thanks again. Handing to Christine.
Welcome to Hanover Messe 2026 at Siemens booth. AI is a big topic. Make data work for you. Nikolova will present.
CPG companies need to get smarter from data to value and faster from idea to consumer. This requires combining real and digital worlds. Digital enterprise with digital twin, software-defined systems, industrial AI. Data fabric and knowledge graph contextualize data. CPG journey: capture data, connect in data backbone, use digital twin for packaging, GenAI assistant, lab with identity control, recipe management, production planning with dynamic scheduling, WCC unified sequence for recipe execution, AI-powered filling and packaging, visual inspection at 2400 images/min, optimization of buildings and processes like spray drying with live digital twin, supply chain traceability. Results: Pringles 10% higher capacity, Natura 50% reduction in water and energy, PepsiCo 20% higher throughput. Visit the CPG showcase.
Thank you, Svetlina. Great presentation. What about this mug?
If you go to the CPG end-to-end journey, you can win this mug. Get your coffee.
Very motivating. Welcome to Hall 27, the living room of industrial AI. Sustainability is core. Please welcome Martin Baya and Erin Devola.
It's uplifting to be here. We'll talk about disruptions. Resilience is becoming a core competitiveness driver alongside profitability and sustainability. Examples: localized power supply, water management, proactive cooling, supply chain replanning.
Disruptions are our new reality. Natural disasters doubled in 30 years. 220 billion economic losses in 2025. Industries need to prepare.
Companies historically focused on cost and efficiency. Now resilience is key. Resilient companies anticipate and adapt. Examples: power outage, drought, geopolitics, extreme heat.
Example: Siemens factory in Wendell, NC. Installed intelligent microgrid with solar and battery. Achieved 100% energy independence, 100% CO2 neutrality, $400,000 annual savings from avoided disruptions.
Pierre Fabre used digital twin of water management to anticipate and optimize water usage.
Extreme heat days doubled. Use industrial AI for proactive cooling of buildings. Results: 30% energy cost reduction, 30% CO2 reduction.
Radeberger Group used digital twin of logistics network to anticipate disruptions, achieving higher reliability and productivity.
There's one more superpower: Siemens Financial Services offers flexible financing to reduce upfront costs.
Smart financing helps companies become resilient while preserving capital.
Thanks to both. Great insights on sustainability.
Sure and our solutions. Have a great time at the fair. Erin, we will see you one more time later on in the afternoon together with Volkswagen. So, how is everybody doing? Do I see smiling faces? Oh, come on. Where is the energy? This is Monday morning. You still need to have a bit of energy at that time. If you want a seat, there is still a few left. So take the chance and sit down and enjoy what's up next here on stage. By the way, stage is a good cue for me because this is not the only place. We do have a second stage which is a bit further up. That's our rooftop terrace studio where we are also streaming live. And I'm handing it over to Max up there. He also got great content to cover. And for those who are staying here, great having you here. If you wonder where you can see maybe more of that topic, that's on our website the siemens.com/hm26, which obviously stands for Hanover Messe 26. You can watch recordings, you can browse through our program and you can maybe even tag some of those sessions which you want to rewatch on demand. That's also possible. And also the YouTube community is joining. Great you're with us. Take a cup of coffee, buckle up and sit down. Now we will be talking about the autonomous factory of tomorrow and trust and companionship you for sure all agree are the basis of success. Machine tool leaders DMG Mori and Trumpf teamed up with Siemens and the results are stunning. Manufacturing innovation at its best is the outcome. In our first panel at Hanover Messe, we'll hear what can be achieved when three big players collaborate. Autonomous production, data standardization, and shared AI developments are key topics we will touch upon now. Please join me in welcoming our three musketeers in this field. Alfred Gisler, Stephanie Frank, and Thomas Schneider.
Wonderful. Hi, great joining us here. Welcome Stephie and Thomas. Hi. Good morning. Take a seat wherever you feel comfortable. Maybe the three of you come together. Exactly. And I sit down here. So, we are right in the center. That's perfect. Um, great you're joining us. You're taking the time. I know you have busy times here at Hanover Messe and I'm quickly doing the introduction run. So we got the who's who, we got Alfred Gisler, he's the CEO of DMG Mori. We got Dr. Stephanie Frank. She is with Siemens and big times in machine tool systems. And we got Thomas Schneider. He is with Trumpf vector machine also big player in regards of machine building. So industry leaders as I said before are teaming up are coming together. Cooperation is the big buzz word here when we talk about you to shape the future of manufacturing and to see what that means when someone is partnering up. We're going to get some inspiration now in this video first.
Imagine a future where innovation is no longer driven by individuals alone, but by true collaboration in a digital ecosystem. Where standardized data becomes a unified digital twin for every asset from hardware and software to the entire shop floor. And this is where connected data becomes something more. Industrial AI that turns complexity into clear recommendations. And when this intelligence extends beyond a single machine, it enables smarter production, more resilient supply chains, and manufacturing that is more flexible, adaptive, and closer to customer needs. Innovative, secure, together. AI powered manufacturing.
Innovative, secure, together. I think these three keywords are essential and Alfred, I'm going to start with you. First of all, I really like to welcome you as our new neighbor in our exhibition grounds. Um, we know you from AMO where you are like the key player with one full hall with your exhibition tools. So that is really great to see that your first time now also at place in Hanover and we're happy to have you as our new kid in town right next door. And um let me start with you in regards of when we talk about that these three companies are represented here together how you interact and collaborate on innovative projects and um we always call it like that you are shaping the future of manufacturing. How is that done?
Well, um, thank you. Um, we think the future of manufacturing and especially autonomous manufacturing is built on collaboration across the total ecosystem of our customer and our ecosystem here. And it's not about only isolated technologies, but it is about creating a shared digital backbone.
That enables systems to work together. Open standards like OPC UA and the asset administration shell provide a universal language for interoperable production environment and in initiatives like the IDTA, the digital twin technology association, the platform 4.0 and manufacturing X. Those are driving standards on a broader scale. In factory X, the vision is being translated into a concrete industrial application together with our 47 partner consortium. And when we talk about DMTI, we transform this what we call MX transformation, MX machining transformation. And this consists of four pillars. It's technology integration or process integration. It's automation, it's digital transformation and also green transformation that enables sustainable production. And we combine this also with a clear focus on data sovereignty. That means customer remain in full control of their data while also benefiting from a connected ecosystem and what matters for creating an automation autonomous production is AI optimization self-learning systems intelligent automation all built on an open standardized foundation. Yeah. And that's exactly why collaboration matters.
And it's a powerful collaboration. That's for sure. That's very valid points which you have mentioned here. Now Steffy, let me ask you, do we already have some concrete examples where we can look upon in regards of this essential foundation which has been created through this partnership? Um and how are they really making a difference?
Yeah, let me maybe use three examples and what we already started. Um the first one is talking about engineering. When you think about engineering today, it's always a quite long process, quite labor intensive but also challenging um to get it done in the speed required. So what we now um did together is to really imagine what the future can be with a faster engineering. We built up a tool together and this is reducing our engineering time by 80%. Oh 80% um is really the key number here and I think this is impressive because this helps all of us to improve on the challenges necessary. Secondly, let's talk about the shop floor. Who on the shop floor is not looking for really what can be done in order to do the upgrades as secure, as safe as needed. And we're doing that of course with standardized systems but also with our standardized model um in the updates via the asset administration shell as a called and with that we can on the one hand side make sure that it's faster on the other side also make it secure and talking about my time when I was working at the shop floor the most important point is to keep the in automotive industry you're always talking about the loss per 1 minute um cars not being produced and to keep that time down. It's of course key to do the updates as secure as possible. And last but not least, it's about our digital circular economy. We gather all the relevant data we can get them from our cinematic from the synamics and of course we want to get there the digital condition of the machines to really understand of what we then can do to detect where the machine is standing. But also of course um to upgrade it to make it more sustainable and more secure.
Great examples and it's just um highlighting on how important it is to work together. Now Tom, let me come to you and to Trumpf um from your perspectives. What are the most significant challenges your our customers are facing and how is this collaborative ecosystem addressing these challenges?
So I would say there are two things we have to have in mind. So the manufacturing industry is fragmented. It's uh and our customers are mainly family-owned customers. 80% of them are family-owned. So it's uh it's our obligation as leading companies like DMG Mori, Siemens, Trumpf to guide them through this digital transformation. So collaboration is our imperative to guide our customers because they have huge pressure. They at the end of the supply chain. They provide the small parts into the big OEM players and they have tremendous cost pressure. They have uh the need for speed in their product. So we have to set up digital ecosystems which helps them to manufacture because scrap is the biggest enemy of their EBIT. So we have to provide systems which give guidance assistance to the operator that they don't do wrong products wrong parts. It's at the end it's always a question of a small part, it's a sheet metal part or one part on a DMG Mori machine and these shop floors they are also fragmented so we need to collaborate we need to set up the standards for all our competitors for all the other manufacturing tool builders that we live in one ecosystem to support the customers in their um system and AI enables this and we have provided first assistance for the shop floor operators. So we start uh bottom up. So how can we empower the operators at the machine to to give them the data that they can decide on the right part.
Mhm. U it seems like that you came on stage here. It's um a good project what you have been working on otherwise we would not talk about it. So Steffy um we can do a check on okay pilot and test phase everything's nice. Um what about future steps amongst the three of you?
Yeah, I think that's a critical point because we're always talking about what is then coming into the future. And when we talk in about the future, of course, we talk about our vision of the autonomous factory. Even more important with the lack of labor, with all of the challenges um going around going for autonomy is definitely the vision that we want to create. And that all starts on the one on the first step of course with the autonomous machine. The more automated you can do it, the better of course it is for the shop floor operations. That can be done with our CNC. What by the way, the current cinematic one has a 10 time faster PLC than the previous generation, which helps for the speed. But of course this is the basis then to connect to get in the next step connected machines which is why we decided jointly together that this is the right approach because on the one hand side you have DMG Mori machines on the shop floor but you also have laser or metal bending machines on the shop floor and we know some customers I will not name um all of them that have on their shop floor DMG Mori machines and Trumpf machines. So why not to connect these machines together to make the shop floor work? And that and then the last iteration step of course is the perfect basis for going into an autonomous factory and you have seen it. We're building that in our plant in Alongen and this is definitely the vision that we're all aiming for. It's wonderful to see that this silo thinking this is mine, this is yours and there is no connection or if there is a connection that's the poor guy who is in charge of kind of speaking two languages and that this world is over now.
Now when we talk about the future, Alfred, what's your perspective?
Well, uh thank you. I think uh Stephie is absolutely right. Uh AI is the key to turning flexibility into competitive advantage. At DMG Mori, uh our vision is the autonomous factory realized through three systematic steps. The first one is vertical integration. We start with autonomous machine equipment with self-learning AI like uh our AI chip removal system and adaptive control systems and integrate them seamlessly um into existing shop floor through standardized interfaces like OPC UA. And the second topic is horizontal communication. Autonomous machines must communicate with each other through end to end digital twin with Siemens. We run the virtual world parallel to the real production enabling simulation optimization and also let's say replay functionalities for failure analysis and also continuous learning and third this is a cross system orchestration the autonomous factory requires different machine types and production lines working hand in hand coordinated by AI-driven systems and supported by autonomous logistics like for example our AMRs but what is most important we empower people for example our robot to go solution can be programmed within 15 minutes by operators without special robot knowledge and expertise. We are not replacing workers. We are enabling them with intelligent tools.
Great. I mean that's very powerful and very motivating as an employee there with the new tools and it's everything in ease. So what's your vision on that Tom?
So I think that uh the only way we can cooperate in Europe and make a difference is that we across the borders of the technologies enable the manufacturing shop floors and this AI technologies and especially the tools we provide together these will uh instantly work at the customer operator side and I think that is a huge advantage of the future that on the shop floors the future will be driven by supporting assistance systems which we can build on edge devices and on AI and this enables our customers to be competitive in a competitive world and based on one ecosystem which is where the datas are secured where we have a close cooperation on cyber security and we have on data exchange which enables these products in the futures and that is where I think all the initiatives we have started with Siemens and also in our VDMA and VW where we have a tremendous power of the European industry to work together and drive this into the future to become an export leader in sovereign manufacturing autonomous manufacturing of the future.
Mhm. Stephie, we got one more minute to cover. So a quick one, which factors matter most in regards of bringing this to a success and turning this vision into a reality and um what do we as industry players need?
Um so I have again um three wants to share. Yeah. Um the first one is trust represented by reliability. We need to make sure that it's a safe and secure environment to do that with standards that we define together but also to make sure that the shop floor operations what is probably the heart of the manufacturing when you talk about the machine tool industry because the machines need to run and need to be reliable in the production side. That's point one. Second, speed matters. Of course, we need to be fast. Um, we need to be fast in changeover times. We need to be fast in the ramp up times. This is the second step. And last but not least, Alfred mentioned it, you said it. I think the most important point um Tom that we are always talking we cannot solve that on our own. We need to do that together. And this is why we are standing here to do that together. So trust, speed, and together.
I couldn't have summarized that better than you did, Stephie. Thank you so much. And also thanks to our guests presenting here. Guests do always get a special gift from us that you can take home. So you have nice memories. Those products are from one of our customers in Brazil, this year's partner country, Natura. That's some exclusive cosmetics produced with Siemens technology. So really nice. And um if you're interested to see DMG Mori, I mean, you can't miss it. They are right next door. But storm you are located Tom.
We are part of the small parts on every booth because sheet metal is everywhere but unfortunately we don't have a direct site but just look on the beauty of sheet metal and then you see Dr.
And I think hall 13 also covers some of the topics at the industry 4.0 booth.
A big round of applause to our three musketeers in regards of autonomous factory production. Thank you so much and all the best of luck and success at Hanover Messe.
Thank you. Thank you. Thank you.
And we're moving on to our next session kind of related as we will stay in the machine tool robot topic. Advanced machine engineering is the big buzzword here. Siemens cinematic machine tool robots offers up to and now that's a big number 300% better path accuracy for demanding manufacturing processes. But I'm not the expert to tell you more. This is our two experts from the Fraunhofer research associate. He is from his profession. That's Stefan Hansen and that's colleague from Siemens Ombberto Noert. He's the system manager for CNC robotics at Siemens. Please join me in welcoming them with a big round of applause. And let's play the jingle to get some motivation out here.
Thank you. Thank you, Christine. Yeah. What is it about the machine to robot? The machine to robot is something which is a new innovation. But let's take a step back and have a look of why robots weren't able to do things like milling steel before. In the background, you can see one application of cinematic robots um where we do path relevant applications such as additive manufacturing. Especially factories are equipped with cinematic robots due to their path accuracy and we use it for additive manufacturing such as components or furniture and robots of course can do also handling processes but when it comes to milling it was always a bit difficult. Cinematic controlled robots uh since yeah more than a decade now. Some might know it as a run my robot direct control. But milling steel was impossible. Aluminium was fine. So we challenged it and try to do the impossible. Why wasn't it possible before? Let's ask the AI.
No. Machining steel with a six-axis robot is generally impossible because it lacks stiffness and accuracy. It would result in chattering and high vibrations, making it suitable only for softer materials with reduced cutting speeds.
And what have we done? Well, in short, we've combined our hardware and software portfolio to with clever algorithms achieve an active dampening of the robot axis and therefore to encounter or suppress actively any vibrations that would occur on the tool. Furthermore, we can achieve up to 300% higher path accuracy and balance properties in the entire working space. Why is that so important? Have you ever tried drilling a hole into a wall to hang up a picture? What do you do? You go as close as possible to the wall and don't do it with a stretched out arm. Why? Because you then cannot have a stable process. And it's the same with robots. So we've given them factors higher dynamic stability to really consistently mill steel and achieve blue chips. So all of the heat of the tool is not in the tool, it is in the chips. And how does it all come together? As Siemens, we are not producing any robots. We just provide the technology which is then implemented into our partners' mechanics and therein we have currently for the machine to robot Autonox, a German company, and Danobat, a Spanish company, and this is done also through the drive technology Simotics and Sinamics and complemented with our great cinematic CNC controller with software and of course also digital simulatable in the digital twin. Combining all of these with a technology specific end effector is then the part of a machine builder which we just have heard in the panel discussion or a system integrator. And this all then will be sold to an end customer. These end customers are in various industries. So we for instance look at large additive large aerospace structure components where the manufacturing drilling milling processes is crucial where you have big parts and also if you look in automotive or general industry where you have post-processing of castings for instance or you put the robot on a linear rail or a linear platform to also have mobile repair cases. So not bringing the workpiece to the machine, bringing the machine, so the robot to the workpiece, that is definitely more cost efficient than the other way around and thereby always addressing the challenge of growing part complexity and therefore also growing manufacturing processes. This is for accuracies below 0.1 mm. So not the ultra high precision micrometer but 0.1 mm is very often sufficient for a lot of processes and to mention one of our customers looking at the productivity increase. We had a process or the customer had a process which took them 40 minutes and they installed the machine to robot and it took them only five minutes. So eight times faster. So a tremendous productivity increase and this all does not remain unseen. So the machine to robot was internally achieved the Siemens inventor's award of the year 2025 and also recently was awarded with the Hanover Master Robotics Award. And I've seen one of the machine to robot technology inventors around. Please welcome Stefan Hansen from the Fraunhofer Institute for Manufacturing Technologies and Advanced Materials. Hey Stefan, congratulations first to that achievement and maybe you can guide us a bit through the process of how the machine to robot was developed.
Uh yeah, thank you for the warm welcome Ombberto. So initially there's a question and our question was how do we advance industrial robots to be able to carry out machining tasks reliably and robust and we started by benchmarking industrial robots on the market. We noticed a lack in performance. So what we do is we applied advanced control systems, for example the cinematic controller with a path planning ability to really get the dynamics of the robots or recognize the dynamics of the robots in the controller. We integrated additional sensors just to have more information on the error we are trying to compensate and this all ultimately helped but there still was a gap when it comes to machining tests and then we ultimately started to design a robot prototype for machining applications and the key innovation here was a new drive system really improved the performance of industrial robots.
Perfect. And it made it to the product. You see the drastically accuracy improvement on this contour for instance. And now we have the product. But maybe you can give us a more practical example of where we can find this innovation today also in our daily life.
Yeah. To get a better understanding, noise cancelling headphones are a good comparison. Here as well we have vibration in terms of noise from the surrounding and we want to actively suppress those vibrations and this is quite an example of how our new drive system is working. So we have vibrations and those vibrations are actively suppressed by the new drive system.
Perfectly. And as you're using the machine to robot daily in your Fraunhofer facilities, maybe you can share some practical experience with this. So what experience do you and what kind of limits maybe do you also experience?
So we more or less have four key benefits here. First one is that we have a feature which is well known from machine tools. We now have a pose independent behavior of the robot which is quite important if you machine larger parts where you have different configurations of the robot during the process. The second one is you can now use the machining parameters provided by the tool manufacturers. We don't have to tune down the feed rate in order to achieve the path accuracy required from the process. And one very interesting property we encountered is that we had some tests carrying out and at some points the spindle was the limiting factor in terms of power which for our background never happened before that the spindle was a limiting factor on a robot and this ultimately led to a productivity increase during various machining operations. We are just faster.
Perfect. And also this accuracy is not always relevant if you have non-subtractive processes. So don't put a spindle on it. Have additive processes. Arc path welding. Save some material there. But now you might ask yourself where or how do I know if a machine to robot is sufficient or suitable for my process? Well, the limits for CNC machines were gathered over 60 years. And of course, the limits of the machine to robot are not fully discovered yet. There are gaps. So where we have to bring the puzzle pieces together. We Siemens and Fraunhofer as well as our mechanical partners can offer you some technological feasibility tasks to try out the process and see if accuracy and productivity is sufficient. So reach out to us if you want to challenge us and maybe Stefan you can tell us a bit more what is in your pipeline. What kind of technological feasibility tests do you maybe have?
Of course I cannot tell all secrets or all results from feasibility tests. But we have one interesting research project together with Siemens and Airbus where we want to machine large carbon fiber parts for the aerospace industry where the goal is a high throughput. The complex parts easily range 20 m and an industrial robot on a linear axis might be a very sensible alternative machining concept to increase productivity and adapt to processes when the design of the parts differs. So really large components where you will definitely benefit from the productivity increase up to eight times and also up to 300% higher path accuracy as well as the balanced properties in the entire working area and of course new machine concepts which can first of all look at existing processes but also have a look at processes which weren't feasible for automating before. So that is really a benefit and all this while making use of standard components staying in a standard environment. So not having any new programming languages. It is programmed standard with G-code or ShopMill Cadem system. The whole digital chain remains the same and this is really interesting. And now we are almost come to an end. I just want to show you if you want to see the machine tool robot live feel free to visit us in hall 26 in the production hall booth C70 where you can see the machine to robot live and we also have some exhibits there. You have some first images of the robot there. So feel free to step by and see the accuracy, see how the machine to robot performed on a 2 m long steel beam. So really wide, almost as tall as I am. And that's really great to see the accuracy, to see the surface quality and all this with recommended tool values and cutting speeds. So that is really beneficial and also things like Protec, NX machine 3D twin or ACM which can automatically adapt to your process and that is really great.
If you cannot come by now we also brought some parts with us, smaller ones, you can come to us afterwards and of course scan the QR code to visit our website. And with that we come to an end. And if you have a challenge for us, reach out to us. We'll sure be able to address the challenge of manufacturing with robots. Thank you.
Thank you, Ombberto and Stefan. And we from Siemens hand over a gift to all our external speakers. This is from Natura coming from Brazil with Siemens technology. It has been produced. So, a nice memory for being on stage here with us. Thank you so much for that introduction. And with that, I'm handing it over to Melissa.
Hey, everybody, welcome. It feels great to be on such an innovative stage here. I'm feeling pretty nervous. I hope you're feeling nervous, too. You shouldn't. But nevertheless, that was quite a cool example of how autonomous precision as well as automation is making that happen for all of us. So we want to move forward with everything. And as machines connect, as they become more precise and as they become more autonomous, there's one thing left to ask. How do you secure all of that? You see cyber security is moving in one direction but also we need to think about the main thing that is happening because AI is bringing incredible intelligence as well as speed to the entire industrial environment. So now I think the question is trust, building trust and security right in from the start. And to help me talk about this topic right now I'm very happy to welcome the global chief cyber security officer Natalia Oropza here.
And isn't it cool to see that we're dressed alike? Yes, this is what happened when you share confidential information, guys. So be aware of that. And just for your information, this is no, this is a coincidence but we love that. So it's also a great pleasure to welcome Kristoff Berlin from Microsoft. Kristoff is the vice president for engineering and UFO at Microsoft. Have you? Thanks here. So you don't share the same color, but you know what? I've got to be honest. I got to confess something. Go ahead. I had an XFile moment when I looked at your title, UFO. But believe me, I'm sure now that it's unified feature owner. I think you're the one they come to throttle, right? So take a seat. Let's let's take off. I think you are better here. Yeah, you really want to sit between us. Okay. So let's let's kick this down. Let's get closer to what's changing and what does this actually mean in practice. Um I'm very excited that I get to choose a topic like this that is actually more important than everything else that is happening here. We're all talking AI moving faster speed but the real question is what has to change in cyber security as we move. What needs to be done to make things safer? And um yeah maybe Kristoff I'll start with you.
Sure, happy to provide a point of view on all of that. So as you just mentioned, yes we are right in the middle of the AI transformation as we all call it. There's another way to look at it. It is the fifth industrial revolution that we are in. It's very obvious and cyber security will play an absolute key role in all of those things because at the end of the day digitalization, data, runtimes, control exposure, threat management all of that will get a velocity and speed that is unmatched. And so you really need to take care of your cyber security posture in a way that you have never done it before because you're exposed in a very different manner. But at the same time you can really embrace a fundamentally new pattern to actually leverage AI in cyber security to help with your other AI workloads. And this combination of using agents for security in a secure context is definitely something that will fundamentally change the entire game.
Okay. But now let's talk about the even bigger threats. How do we use that as an opportunity? Natalia.
Yes, we were talking about before joining into the conversation now and we say that the key word today is speed. We really need to drive cyber security at speed. Now we have seen a lot of examples about physical AI, about the AI in the OT especially in the factories, right? And then when you imagine what AI is doing like giving us the opportunity to configure a robot for instance more quickly without having to read all the manuals but just listening to the AI and making the configurations right or using your glasses in order to know what you have to do, then you realize if something goes wrong meaning the confidentiality or the integrity of the data gets compromised then of course you're talking about dangers for the human being because now these robots are cooperating with humans right or damaging the availability of the factories, stopping the factories, or damaging the integrity of the data which is worse than having a tire that is not done with the right quality. And those are exactly the risks that we are facing because of AI. It's wonderful opportunities we don't want to stop that but we need to do cyber security move faster and deploy it in real time. We talk about this Chris, real time patching. We cannot wait 12 months to patch something in the factory. Talk about this because I found his point of view very...
We can talk, we can talk a lot but now I'd like to know you know many leaders are looking at cyber security as more as something that is actually slowing you down. So in practice what does that look like and when cyber security actually becomes the foundation and not something that is stopping us down but rather speeds us up?
So if you think that cyber security slows you down I would be very transparent with you, then you're doing it wrong. Because at the end of the day, cyber security helps you to actually accelerate. It helps you to measure risk. It helps you to identify the risk vectors and how to think about these things. When people thought about cyber security as an anchor and all those things, that was really all about the wrong tooling, the wrong process, the wrong approach or a legacy approach into a software world that is ultimately not adequate any longer. So the way to think about it and exactly to your point is everything is speeding up. In the past, when a vulnerability came out people thought that they have 12 months to patch it and you don't have these 12 months because in 12 months you are already shut down because of the exposure. How can you help with that? You also use AI to accelerate the tooling behind it. So you don't have to have humans any longer that do the testing and validation and human based process. You actually do threat assessment management, you do all of those things in a way that it will accelerate whether you like it or not. And if you embrace a new software-defined pattern also for cyber security then you actually start to see how they can work hand in hand and they accelerate your business process quite honestly.
How about your perspective? I want to talk on top of what Kristoff had said because he's the VP for engineering at Microsoft and one of the key aspects you need to do in cyber security is to deploy cyber security by design. The word is shift left. Yeah. And what we do is to include in the tool chain of the engineering, the tool suite that you use to develop products, to include their cyber security and I think Kristoff can tell us a lot about this and...
The word is shift left. Yeah. And what we do is to include in the tool chain of the engineering meaning the tool suite that you use to develop products to include their cyber security and I think Kristoff can tell us a lot about this and...
Before I go with Kristoff, that brings me to the next topic that I think is key. We need to cooperate.
Yes. And for us to cooperate is not about sitting together drinking coffee and talking about the last incident we had but about sharing data. In this case, Siemens and Microsoft we have very good contracts and everything so that we can share that and can speed up the protection of our products and of our customers. Kristoff maybe you can tell something about the blockchain engineering.
The way I always describe it and exactly to your point, it is a shared responsibility. It is cybersecurity, software supply chain risk, all of those things is all about sharing the responsibility and the ownership and accountability as part of it. The way Siemens and Microsoft work together is we have our core competencies. We are really good in some areas, Siemens is good in other areas. But guess what? You need both areas working very well together in order to really get a grip on this new threat world, this new threat vector. And so when you think about this very heterogeneous environment that customers are always in, it is never a only Siemens, never a only Microsoft, never always Google or AWS or Schneider. It is a complex complicated world. And so at the end of the day, bringing our core competencies together and saying how can we lead this together is really about this aspect of acceleration. But when you ask me about AI, it is all about acceleration at the end of the day.
Let's talk about risks now. If we talk about the risks when AI starts scaling across factories, bigger risks can occur. The surface is larger than we can expect but how do we track what's happening? Everyone starts using their own tools. So much can happen. How do these partnerships and initiatives like Charter of Trust help to continue?
Yes. I mean AI is a threat but also a huge opportunity. And Kristoff and I were talking before the conversation today about this new AI models that give us the opportunity to find vulnerabilities quicker. From the testing that Microsoft is doing, we know that you can compress one year of pentesting into weeks. That's really the big opportunity and this is where Charter of Trust comes in place because we have a partnership together and we have this commitment of protecting the digital world by creating trust. And the way to create trust is protecting the digital world. In this partnership we share not only data but we actually act in order to protect the ecosystems where we coexist. Kristoff mentioned already it is not Siemens purely, not Microsoft purely, it's everyone together in environments of consumers like...
You name it. I just forgot to mention one of them. So yeah, what if I round it off? You're saying we hack into our system first and that gives us the security and the strength to avoid those kind of risks. What are you saying?
Well, the short answer is yes. The way Microsoft and Siemens work together is that we embrace the new pattern, the shift left, the tool chain approach to really look at our own solutions, how we can learn from each other, we share and all those things. To reflect a little bit more on your question as to AI introducing risk, I will be very transparent with you. It doesn't. It just accelerates the risk that we always had in the first place. Yeah, we just didn't know. AI by itself is just a way faster business process implementation of the things that we had. The attitude of ignorance is bliss may have worked for a long time but it definitely doesn't work any longer.
Love that. So let's become concrete. I want the nitty-gritty, tangible real stuff. Is there one situation where having the right security in place helped us move faster or helped us implement when it comes to AI just to get better outcomes?
Yeah, I mean it's clear and you can read that in many studies that if you do that by default, meaning at the beginning of the development, then cyber security is going to cost cents compared to what it costs if you forget to deploy it at the beginning and you have to deploy it at the end. It's very easy. You have to stop productive systems from running in order to apply security if you haven't done that at the beginning. So applying that at the beginning will save you not only a lot of time and a lot of headaches but a lot of money. That doesn't include the fact that if you don't include cyber security then the possibilities of getting an attack, and you will get that, but the thing is if you don't apply cyber security then you are very much into getting damage: stopping your factory, ruining your reputation and so on. And we have seen a lot of these cases already in the news.
And of course we have to be honest with you with the current political situation that we have. This is increasing the state sponsored attacks. This is increasing the motivation they have to attack critical infrastructure. And we coexist as Microsoft, as Siemens, as AWS, you name it. We coexist in the critical infrastructure.
I'm thinking the same for you. You probably have an example for us.
Sure. We have many examples because it happens every day. Well, let me tell you a story. Let me give you two very specific examples. One more of a root cause and the other how we mitigate. As you can imagine, Microsoft these days we are all into the data center business. Data centers are the factories of the future. It's not the fancy GPUs, it's not the fancy everything. It is all about OT. It's all about energy management. It's all about risk vectors. It's all about massive scale operations. Others call it factories, we call it data centers these days. And we are exposed to this like everyone else. So we partner with Siemens to bring our expertise into the infrastructure with regards to scale but also have Siemens help us with the aspect where Siemens' core competency really helps on automation, on energy management, cooling and all of those things. So this is a first party example of how we really work together between us. Another aspect when you take our joint customers: every customer that Microsoft has is also a Siemens customer. The way we think about these things and the way we go to customers is that we integrate solutions with each other because you don't have these silos any longer. Siemens has incredible process engineering offerings, incredible automation when you talk about virtual PLCs and all the new stuff, but you have to run it somewhere, you have to secure it. There's security from an OT point of view, security from an IT point of view, observability everything. So we ultimately come in and we say Siemens' product portfolio is amazing if it runs on Azure. Our cloud offering just gets taken up a notch further because they work together. That's important because it accelerates you.
So all that you've said I'm taking with me. It's about partnerships. We need to talk to each other and we need to partner to make this happen. Right. Last two minutes. Last question. Looking ahead just a little bit, what has to change in organizations to really work together to make AI both secure and scalable? Natalia?
I will take two things. The number one thing is speed, right? Real time protection. And I want to be more explicit and take what he told me minutes before the conversation because I think it's very important. The times that you could wait 12 months or whatever amount of time till your next maintenance windows in your factories, in your industry, in your warehouses, those times are gone. You need to push that real time immediately.
And you need to let AI help you to do that. I love those words from Kristoff. I definitely take it for me and I hope for the audience as well.
Yeah, if I add on to this, I completely agree with you. If you really look at AI in this entire context, AI gives you a lot of luxury. It gives you faster business processes. It changes the way you think about your business. But the same tooling is available to the bad guys.
So as you think about your own world, instead of thinking about a process for months, you've brought it down with the industrial copilot and AI tools. The same tooling is available to nation-backed actors. While you have this luxury, you gained a responsibility not to be complacent.
To round that up, cybersecurity is what turns everything into something you can trust and scale. It shouldn't be seen as a showstopper or barrier, but as an enabler.
Thank you Natalia and Kristoff. This was an amazing session on cybersecurity. I'm going to hand over to my colleague Christine.
Thank you so much for the partnership.
Thank you, Natalia and Kristoff. We're moving on at Hannover Messe. We have great experts: Raymond, CEO of Mendix, and Shri Iapulu from AWS. They'll discuss the convergence of gen AI, agentic AI, and physical AI. Please welcome them.
Fun all right, very good. All right, welcome everyone. I'm Shri Iapulu. We're talking about the fusion of AI and the real world. Over the last three years, AI innovation has been in the digital world, but the next frontier is physical AI. I see three categories: improving existing solutions, extending with natural language, and reimagining workflows. Humans shift from in-the-loop to on-the-loop. Three foundations: data, AI models, and bringing expertise to the physical world. At AWS, we have experts, tools, and solutions. Domain expertise from partners like Siemens is key. I'll hand over to Raymond.
Lots of new technology. It's about business solutions, and partnerships like AWS and Siemens are key. The agentic enterprise journey starts with data, then context, then intelligence, then outcomes. Graph Studio creates an intelligent data layer. AI Studio builds models with RAG. Mendix orchestrates applications for a hybrid workforce. Example: a factory issue with a robotic arm, where operators can query contextualized data and build ML models to optimize conditions.
Thank you, Ry. This is happening now. With Jable, over 100 facilities, we reduced operator burden using Mendix on AWS with AI, providing just-in-time natural language troubleshooting regardless of language or location. The impact is significant. Learn more at our sessions. Stop by our booths.
All right. Very good. Oh, whoopsie.
Thanks to the two of you.
You mentioned where we can find you. How long will you be around?
I will be here after the session on the corner. Come see us if you have questions.
Okay, perfect. Thank you so much.
That was AW. I forgot to hand over something. This gift is from our customer in Brazil. All right, we're moving on. Don't miss our next session on manufacturing of humanoid components with Microsoft. Welcome FA Alre and Christoff Balin.
Great to have you on the sofa again. Have you been around the fair?
You are in hall 17 with a big Microsoft booth, but this is your second home. Let's sit down.
Yes, I've been around a little. It's incredible to see the speed of things.
I walked by, still thinking we are in hall 9. I've seen a bit but not enough. There's more to see.
You still have time. Christoff, you work with this technology. How is AI transforming manufacturing?
AI is here to stay. It speeds up processes, increases quality, and efficiency. At Siemens, we deliver with partners like Microsoft.
From your point of view, Christoff, how is AI impacting manufacturing and what is the joint vision?
This is the fifth industrial revolution. It changes everything about speed and efficiency. Humans and AI collaborate. Our partnership leverages each other's core competencies.
I feel your passion. FA, where does AI fit in the manufacturing landscape and what problems does it address?
Supply chain disruptions and volatility. AI augments skilled workers. For example, CNC programming. AI analyzes data and trains on your data to speed processes.
Now, Christoff, on cloud and digital solutions, what is Microsoft's perspective on AI in manufacturing and the cloud?
AI accelerates everything. We are moving to a supervisor model. The cloud enables managing globally while executing locally.
FA, what are you showcasing together on AI manufacturing of humanoid components?
At the Microsoft booth, we show how a humanoid is manufactured. Using NX with a copilot, AI helps the designer with natural language, gaining 30% efficiency. In engineering, AI suggests tools and processes, leading to up to 50% efficiency. It's about speed and time to value.
One minute left. Christoff, what do you see in the near future?
Far is six months. We will continue the supervisory model. Humans become experts. AI handles hard skills while we focus on soft skills.
Great. Visit the Microsoft booth. FA, the near future is in consumer packaged goods.
We have a gift for our speakers. Thank you so much. Handing over to Militia.
Thank you, Christine. Let's move to green steel. Three companies will show how digital technology makes green steel a reality. Welcome Miriam from Gravity, Flora from Capgemini, and Alberto from Siemens France.
Gravity's vision is to produce low carbon reduced iron in France. We needed key partners, so Siemens and Capgemini joined as shareholders to develop a digital twin of the plants.
That sounds like collaboration at its best. Can you give me a tangible number that surprised you?
Gravity is over 3 billion euros. The power connection is 1 gigawatt, equivalent to 300,000 households. Each percent of cost reduction represents huge money. We co-wrote a white paper and found up to 10% impact. Digital adopted early becomes the operating system.
From your perspective, Flora, can you give a concrete result?
This greenfield project is a unique opportunity to build a digitally native plant. We ensure it is data-centric from the start, saving time and money across the lifecycle.
Many think cleaner means costlier. What does the data say?
The tradeoff is not structural. Green steel from scrap recycling is cost competitive. But we need green iron feedstock, which Gravity provides. The target is cost competitive green iron at scale.
Miriam, what surprised you?
How fast and far the collaboration works. Together, we moved from theory to concrete project details like energy management and BIM model, soon a digital twin.
Where does this go next? Alberto?
Gravity is ambitious but has an ecosystem. Cooperation agreements with neighbors share the burden. Siemens and Capgemini foster these projects for the whole industry.
Make the plant ready for future technologies. Ensure engineers have a single source of truth from design to operation.
It's about scaling a model across Europe, not just one plant.
We have a white paper with QR code. One closing message each. Alberto?
Come to us with your ambitious project. Together we can show that decarbonization and operational excellence can be cost competitive, with the right digital tools from an early stage.
I love that. That is a key takeaway.
Lovely closer year, what about you?
For gravity team, digital tools are key for us to design better and to operate better, and it's a very important topic for gravity.
I love that. What about you, Flora? What do you say?
For me, the key word is that digital and AI is not about tools. It's about how we make processes and operations much more efficient in everyday life.
Sounds amazing. That is very amazing as well because next up we'll talk about the automotive. They're going to talk about big partners like BMW. I'm looking forward to that. And how are you enjoying this by the way? Look at all the people.
Thank you. Thank you. Amazing.
I hope you had the time to take a snap of the QR code to download. I see some nodding. That's great. Thank you.
But we also have a whole... you're in hall 15, booth F52. If you want to get into close conversation and get a bit more technical, here's the engineer gravity.
You can meet them there, as well as Cap Gemini. I'm looking forward to seeing what the future holds for you. Thank you so much for being here with me.
Thank you.
Thanks, Militia, for having us. Thank you.
Thank you. Great pleasure.
And there's a present coming. Hold on. Here we go.
For our partners.
Miriam, thank you so much. It's lovely to have you. Great working with you. Thank you all.
And we'll see each other around, especially at the booth. Cheers.
Yeah. So, how do we go deeper now? Because before you build anything, the only question is how accurate, how fast and how real can that simulation get. BMW, Mercedes-Benz, Nazca, they're all finding out already. So please welcome Neil Ashton, distinguished engineer at Nvidia, as well as Alice Alb Govich, global head industrial metaverse at Siemens. Now you got to tell me how to say your name.
Did I say it right? Your name?
And your name I didn't. Alish. There we go. The stage is yours. Tell us more.
Yeah, I think we should sit. It's a heavy topic so we should sit.
I've been walking around so much today. I am happy just to sit down.
Yeah. So anyway, the topic today is CFD. Who knows what CFD is?
Well, a few people. So let me start. Usually we talk about CFD around a car. These are all the things when you're driving the car you don't see but it's happening. The flow around the car generates the force that you see when you're paying for gas or electricity because the air exerts force on a car, truck, or any vehicle, reducing energy efficiency. Simulating aerodynamics is computational fluid dynamics, computationally very intensive. There are two applications in automotive that take the most computational power: crash simulation and CFD. Do you know how much energy the lettering on tires takes away from fuel economy? It's usually about five to sometimes 10% that leads to 2 to 3% of fuel economy for those letters on your tire. This is complicated and important. Maybe Neil, you can share what you're doing here with CFD and what Nvidia is doing, and maybe on the next slide.
Yeah sure. Computational fluid dynamics and more broadly computational engineering is becoming the key trend of simulating everything before manufacturing. Physical testing moves more to the right as you can do more simulation. One bottleneck has been how accurate a simulation is, often bottlenecked by the amount of computer power. If you want really accurate, you need massive simulations on billions of cells. With Siemens Simcenter STAR-CCM+ moving to GPUs, we enabled bigger, more accurate simulations. For BMW, it was about 2.9 times lower cost, but also the ability to do things faster. It's been fun collaborating with Siemens and enabling customers to do bigger things. Can you tell us a bit about why companies do these simulations, their expectations, and how long a calculation usually takes?
Yeah. If you go to the next slide, this is a good example of the speed-ups people have been getting. If you were on CPUs and taking 15 hours, every year by adopting the latest Nvidia GPUs, that has gone down and down. This enables people to do much bigger simulations. One popular topic here is also AI. To train large models you need lots of data. Customers are focusing on which CFD code can give them the most efficient way of generating data. With Siemens Simcenter STAR-CCM+ GPU version, that gives them computational efficiency. This plot shows simulation time went from about 15 hours per day on CPUs to about 3 hours. That means for a practical design, you can do a few iterations in a day, improve the design. It's productivity. Your engineers are your most precious resource, so reducing time to get an answer is only good.
Yeah. Neil, you mentioned AI because I think there are implications there as well. You're using these results to train models that can be even much faster. Can you tell us a little about that aspect?
Yeah, absolutely. The use of AI to develop surrogate models is a new feature in Simcenter. It allows going from hours or days to real time or seconds. But you need data. It's not a large language model; it's training a neural network on prior simulation data. How good that model is depends on the data. So AI is not a replacement for traditional simulation but a close symbiotic relationship.
Okay. Just for my curiosity, how many simulations are usually needed to train an AI surrogate model?
It really depends on how broad you want your use case to be. You may be able to get away with as little as 50 cases to train a model, but then you may only be able to do a specific type of car. You can extrapolate and train on all your sedans, SUVs, convertibles. So it's use case dependent. But it's an exciting area driving down to the most efficient, accurate, easy-to-use code.
And related to that, when you're doing AI surrogate models, can you do it on any car or does each type need its own model?
Absolutely. Car companies have lots of data, and this is not just cars. We can move to the next slide to show other use cases. You could generalize across different cars. One thing is that AI physics is also applicable across many industries. GPU acceleration has gone from incompressible automotive applications to hypersonics, machinery, sloshing, so now you can simulate almost any application with the GPU version, which opens the door to AI.
Yeah. I assume you're saying that when you port simulation to GPU, it's a process. If I look at this slide, we start with incompressible aerodynamics, then add compressible, thermal, particles, multiphase flows.
Well, not to belittle it, but essentially now for most people there isn't much you need to do. Buy a GPU from your favorite location, switch to the GPU version, run your simulation. It's as simple as that. Companies literally switch from CPU to GPU and get big speed-ups. That's the value of the Siemens-Nvidia partnership: we do lots of work behind the scenes so end customers don't have to do much.
One question about GPUs: do people usually acquire their own or access them on the cloud?
It's completely dependent. We see both. The great thing is there is a great ecosystem where Nvidia GPUs are available everywhere.
But how about you? What has changed over the past four or five years thanks to accelerated computing?
Two things. First, everything is moving so fast. Porting simulation tools on GPU is happening really fast. We ported the entire STAR-CCM+ with all capabilities on GPUs in four years, which would have been surprising ten years ago. Second, having a full aerodynamic simulation of a car in 3 hours is still amazing; I remember it taking a week. And the surrogate models are working very well.
And maybe we switch to another nice visual.
Yeah, why don't we do that?
What's the other thing? Computational fluid dynamics color for directors? I've heard many versions.
We joke, but the key point is air is invisible. The reason we focus on visualization is because the whole advantage of doing CFD is that compared to physical testing, you can actually see what's going on.
Yeah. I think 20 years ago people would say it's colorful fluid dynamics, expecting it to be less accurate than experiments. About five years ago that changed; you could start using CFD to find mistakes in wind tunnels. I think CFD is computational fluid dynamics.
I guess the clock is turning red, nearly up. So the last thing is to make sure people go to the booth to look around. I've seen some great CFD demos here. It was good fun to chat.
Yeah, it was good talking with you, Neil.
Cheers.
Thank you so much, gentlemen. Thank you. And we have a small giveaway for you, sir.
Enjoy that. A small something from someone here at the booth. It won't take long for you to find out.
Thanks for joining us. Thank you as well. And enjoy the rest.
So, quick mood check. How's everyone doing? Tired, hungry, excited? We still have a few seats free. So don't be shy. Take a seat. We have a lot more coming up.
Now up next we have another one of our partners on stage. This time that partner is ServiceNow. This session is going to showcase the evolution of our partnership with ServiceNow and put a spotlight on the industrial asset hub software service for improved asset management. You'll learn how we're delivering true IT convergence and elevating efficiency for industrial environments, directly impacting your shop floor operations. So please give a warm round of applause to the head of internal startup industrial asset hub at Siemens, Miki, and global OT partner manager go-to-market at ServiceNow, Eric Fandango. Welcome, gentlemen.
We are here in this together as always. It's great to be here. Last time it was Hanover, Nürburgring, Paris, Munich. We've been all over. Why are we doing this? We want to give you a short insight into what a partnership can be and should be, and how this will pay into our customers' use cases. Let's jump right into it.
The use cases, the motivation. Over the past 15 years, Siemens has been managing IT assets—cell phones, laptops, servers—based on what we call myIT, a platform on ServiceNow. Now Siemens IT wanted to apply those processes to operational technology assets: drive systems, controllers, network gear, everything on display in our own factory. We want to apply that throughout Siemens for smart infrastructure, mobility, factories, and digital industries. It's astonishing to see how our collaboration has evolved from customer to partnership. Siemens is shaping knowledge in OT and capabilities in IT, converging those. That's the discussion we have with customers, especially with cyber attacks, the Cyber Resilience Act, and NIS2. Together we built an opportunity to remove silos and have a single pane of glass.
Michael, it's astonishing how our collaboration has evolved. We embrace the opportunity that Siemens is shaping knowledge in OT and capabilities in IT, converging those specifically in OT domains. That's the discussion we have daily with customers because they face cyber attacks. Today we have the Cyber Resilience Act and NIS2, changing the conversation. The opportunity to build convergences and minimize data silos and technology complexities is something we built with Siemens, removing silos and having a single pane of glass.
You mentioned it—everybody's talking about CRA. For us as device, automation, equipment builders, vendors, CRA moves responsibility from shop floor operators to vendors. NIS2 directive—people act as if it came out of nowhere, but we've been talking about it for a while. NIS2 and CRA didn't create the problem; they made it evident. The problem and challenge is you need that asset context, insight into your shop floors, understand where those little gray boxes are.
It's the foundation, Michael. The foundation that needs to be laid out, and the opportunity that could be correlated afterward.
Foundational enabler wisely said. Let's look at OT auto, the key persona. The OT administrator on the shop floor, Otto, is in charge that a production line is up and running. He faces challenges: a device goes down, needs to understand which model, hardware version, lifecycle. Security findings come, are we affected? When exchanging devices, why didn't we know earlier? All those minions run around with different tools and Excel spreadsheets trying to figure out where to get information. It's tribal knowledge. We came up with the Industrial Asset Hub. It's a software service. It's not only for Siemens PLCs or drives; it's for all device classes. We made it vendor independent because customers don't care if it's Siemens or another product. They think in use cases. The Industrial Asset Hub will discover, contextualize, and work with any devices on the shop floor. We're exposing these capabilities where users need them, enabling use cases like network management, vulnerability management, integration into analytics platforms like Siemens Insights Hub, and service lifecycle management into CMMS and workflow platforms.
And then we come in. As Michael elevated, building that foundational lever where the magic can happen—that industrial context is a requirement where we see new opportunities. With Siemens Brightly, we can accelerate the convergence between IT and OT. We're showcasing this within Siemens, eating our own dog food, testing it in Siemens factories. A great example is the factory in Bad Neustadt.
Before we step into that, let me drop one or two comments about Brightly. We've been focusing on cybersecurity use cases with Sinema Security Guard, but service lifecycle and maintenance aspects are super critical. With Siemens Brightly, we have domain expertise in enterprise asset management and CMMS. Integrating those workflows into the overarching workflow of workflows can only be a win-win. Our corporate IT is looking into how to benefit from that in other use cases. The CMMS integration in Bad Neustadt is one next step. Bad Neustadt is our motors factory of the motion control business unit. They are digital frontrunners and the first to utilize this new approach. They saw benefits and expressed them in figures. ServiceNow picked that customer reference and made it public.
The unique opportunity is how the capabilities of Siemens blend in and how they're leveraging it in factories. You've articulated the reductions and efficiencies from having visibility in your factories.
As you can see, this is not just a PowerPoint; we are putting new technologies and innovation to use on the shop floor daily. We're happy to take your questions here or at our booth. We have the opportunity to showcase Siemens solutions on the ServiceNow booth. Take the opportunity if you want to see what we've built in the last years together. Our booth is hall 15, stand D72. Perfect. Thank you so much.
Thank you for your time. Thank you for all your questions. Super happy to take them all and thank you for your partnership.
Thanks to the two of you. Great results when two powerful minds come together. We have a little gift for you from a customer. Natura company from Brazil produces cosmetics with our Siemens technology. Thanks to Natura, you can learn more about their production process in that area. Hall 15—it's raining now, but you might go later. Once more, Michael and Eric, many thanks for sharing your success story. Thank you.
How are you doing? Everybody in a good mood? There is a fan crowd leaving, but more people coming. It's about one Siemens, one journey, and smarter buying made simple. For sure, in private life, we buy first, search, click online with ease. You expect this to become as easy in business. In our next session, we'll talk about how Siemens simplifies the path from discovery to order, making digital buying faster, easier, more efficient. Join me in welcoming the four musketeers: Linda Krumhols, Rudy Basson, Dion Smith, and Frederick Jansen. Hi, Linda. Great having you here. Rudy, Dion, Frederick. Great. That's a powerful force. Let's take a seat on the sofa of wisdom.
When we moved into hall 27, which is the first time we're exhibiting here, we said we took the living room with us. Same setup. Everybody's happy. I'll do a quick introduction: Linda Krumhols, global senior vice president, Siemens Accelerator. Rudy Basson, CFO at Digital Industries at Siemens. Dion Smith, worldwide responsible for the ecosystem at Siemens. Frederick Jansen, CIO Digital Industries. Great that you're taking the time. I know it's peak time for those on the sofa. Now, as I mentioned, customers expect a simple, easy path from finding to buying. At Siemens, we made this available with our C portal. We're now connecting it further to create one seamless journey. Linda, how does Siemens Accelerator fit into this development?
Actually quite nicely. Siemens Accelerator is our digital business platform with a portfolio, ecosystem, and marketplace. This fits nicely into the customer's buying journey. There are customers who have a product ID in mind and want to search and buy. Others have a challenge and want a solution. In B2C, if you're invited to an event and have a challenge on what to wear, you don't know you want to wear orange clothes. So inspiration is key. Siemens Accelerator curates the portfolio according to customer challenges and industries, showing the art of the possible across all portfolio lines—hardware, software, services—combining with ecosystem solutions. Customers can explore, educate, and purchase. The most important thing is that the marketplace sits on Siemens.com. You have one checkout, one basket, and everything in there. While we still have our very successful C portal if you have a specific product ID in mind.
Fully integrated. That's also Rudy's role fully integrated in all processes, knowing where we need money to support creating solutions. Rudy, from a business perspective, why is digital sales so important and where do we stand today?
As CFO, it's in the interest of any company to scale revenue without scaling cost. Customers today are looking for simplicity—one checkout, one basket, one login ID. We believe we can scale digital revenue without scaling cost. If we can standardize, we'll see stronger product margins, and from that we get more benefits like live sales transactions creating data points for analysis. Where do we stand? It's not a vision. We have more than a million digital registered users, roughly 260,000 digital touch points, and we're transacting with about 150,000 customers electronically. Digital sales makes life easier for customers and for Siemens.
Great. And happily, they're here listening. Dion, thousands of partners work with Siemens every day. What makes these digital applications valuable to them?
As Rudy shared, 150,000 customers transact digitally, over 70,000 of those are partners. For partners, time equals money, and money is margin. It's incredibly important that we are easy to do business with. Ease of doing business is fundamental. Most partners work with many vendors, so our ability to work digitally via SELs where they can configure, price, and buy is a necessity. That digital backbone is critical. With the marketplace and accelerator, many of our partners have built innovative solutions on Siemens. We get over a million visitors to our marketplace—a phenomenal shop window. For partners to put their solutions in the accelerator marketplace gives them greater reach and visibility into the market.
Sounds very promising. I'm still astonished by the vast number of partners, clicks, and services. Frederick, from a technology perspective as CIO, why do companies need platforms to enable these digital customer journeys?
Customers these days expect an Amazon-like experience—they want to easily transact with us. That requires bringing different things together seamlessly. We want to help them find what they require, orientate, finalize exploration, and execute. The best way is to bring it all together in one platform so we have control, master data, make sense of it, and guide customers in their buying experience.
Mhm. You say it's easy, but convincing someone to change a running system is probably complicated. Linda, how do Siemens Accelerator marketplace and C portal complement each other?
It's a seamless journey but two separate execution paths. On one hand, you have comprehensive solutions on Siemens Accelerator marketplace with 1,500 offerings from Siemens and 900 from partners—curated solutions. On the other hand, C portal has configurations. 80% of customers looking for a specific product ID use C portal and want to continue. So they can do so. If you're on the marketplace and want to dive deeper into hardware, you can connect directly to C portal. The most important thing is we are on a path to integrate further, having one customer ID going forward. So with one login, one account, you see everything you do with us and partners, independent of channel. That's customer 360. You can still connect with sales representatives in regions. Everything comes together—one checkout, one basket, one customer ID.
Do we also check on the expectation of those customers, like how was your shopping experience?
Yes, feedback is requested, and we use agents and analytics to understand the customer journey and improve.
This is where AI and data comes in, ladies and gentlemen. You kind of see me talking here where this passes goes. Dion, when customers navigate complex industrial portfolios, where do they actually struggle most?
Customers are really looking for confidence that what they buy will work and deliver outcomes. In B2C on Amazon, we click four stars up. We need that level of confidence. The beauty of the accelerator marketplace is that everything we put in has been vetted and tested. Ratings? Not yet, but that's where we expect to get. We need that consumerized buying experience. For partners, having validation from the Siemens brand is a huge confidence builder. As more customers buy offerings via partners, that will build further.
I like that topic of trust. You definitely don't want to be disappointed. Now, technology is evolving quickly. We just talked before about the future that's not further away than six months. Frederick, how does AI help simplify digital customer journeys?
Platforms are the foundation to get started with AI, but AI can take it to the next level. AI picks up the customer where they are, helps navigate the complex portfolio, supports search, recommendations, and leads to the next best action. This helps customers drive smarter exploration. Linda was talking about omni-channel experience in B2B—that's what customers require. AI can capture rich insights of customer intent and help them transact. AI will help us be much faster in decision making, have a seamless experience, and drive value for customers.
Here we are, Rudy. How do we at Siemens ensure that these digital solutions deliver expected value?
We have a simple rule: be customer zero. We try it first in our own operations. The power is in the platform, but only if it works end to end. As Siemens, we have huge operations, so we test platforms in our own businesses. If they work, we refer to it as drinking our own champagne. Sometimes it doesn't work, and we call it eating our own dog food. Even then, we're happy because we prevented customers from eating that dog food. We test until we scale within Siemens. If we can scale in Siemens, it's a good trigger point for scaling with customers. And we learn from real transactions with customers. It's about customer zero and giving customers confidence to scale.
Great insights. As a matter of fact, we have lots of customer showcases exhibited here with Gula, Gia, Pringles, PepsiCo, Natura, and many more. There's also an innovative showcase in our innovation hub at Siemens Electronics Work in Erlangen—a production line with an augmented vehicle humanoid. Don't miss the chance to see what Siemens provides to Siemens as a customer. So what we've seen here is that Siemens is making the difference with simpler, more connected digital interaction. Will you walk around and explore more at the booth or are you tight in business meetings?
Right around, right on the field, into the...
I just need to change my shoes because looking at my panelist like cloud9 shoes—they symbolize digitalization and a lot of artificial intelligence, but still I need to walk for myself.
Okay. Big times. Thank you for the time you've been up here on the sofa of wisdom. Thank you.
Stage and a big applause for my four musketeers in digitalization journey and Siemens accelerator and making shopping easy. Thank you so much. All the best of luck and success at the fair.
Yeah, thank you.
One Siemens, one journey. We definitely take it serious and make it easy for you.
Now we go from automation to autonomy. Autonomous production that is a big buzz topic here at the fair as well, only possible with using the right technology.
How, how in process industries we can move from automation to autonomy. This is what our next session is going to feature. Soft sensors and advanced process control are just some keywords of what you're going to hear now. Please welcome with a big round of applause the chief product owner at Siemens Stefan Burka and the project manager and incubator lead at Siemens Dr. Adrian Kaspari. Thank you gentlemen. Enjoy.
Thank you Christina for this warm welcome. Stephan and me are very happy to be here presenting a topic from the process industry to you which is operational AI for the process industry: soft sensor and advanced process control. As we are facing some interesting trends and some mega trends in the process industry currently which you see here, we also observe opportunities within the area of autonomous operation, the need for autonomous operation and self-optimizing plants. What changed during the last time was while this was a future topic, a vision for autonomous operation, nowadays we have the technology available to actually realize autonomous operation and self-optimizing plants and we do it in our daily business. Basically this is due to the fact that we combine now three technology clusters: first automation and process automation, then the topic of optimization in particular real-time online optimization, and the big topic of artificial intelligence. We can combine those three topics for enabling autonomous operation and self-optimizing plants. That changed in the last years. So now autonomous operation and self-optimizing plants is not a vision for us. We actually work on this. We execute projects and sell products which enable an autonomous and self-optimizing plants. And two very important enabling technologies that we brought here to you is the topic of advanced process control and the topic of soft sensor which we are happy to present you here. In advanced process control we have usually a big chemical production plant, a refinery, a distillation column plant running, air separation plant running which is equipped with automation technology from process automation. We have control systems or SCADA systems or locally distributed controllers. So everything is there. The infrastructure is there and now an operating company might ask the question: I want to produce more. I want to reduce my cost. I want to increase the efficiency and that in operation. So in other words we talk about opex projects. The plant is running and an operating company wants to improve the operation of this plant. Then advanced process control comes into place. You can install a software which is control system agnostic. A software that can run on any control system. You install the software and within the software it deploys algorithms which in real time optimize the production of the plant. The operation of the plant at its core is a technology paradigm which we call model predictive control. And this is operational AI. Modern predictive control works as follows. You take time series data from production from your process historian. You identify a data-driven model. From this time series data from the historian, you identify a data-driven model that enables you to do quick predictions within the near-term future. You embed this AI model in an online optimization problem. You solve this online optimization problem in the closed loop permanently. You push the result of the online optimization to the control system and thereby to the production process and thereby you actually optimize the operation of your production process. This is the main paradigm we talk about and what we have done now is we have enhanced the very classical paradigm of model predictive control. And now what changed? We use machine learning models, a specific type of very innovative dynamic machine learning models that we can identify purely on historical process data embedded in an optimization problem. Solve this optimization problem online and optimize the production process. And there is a very specific particular type of AI model that we can use which is called soft sensor and that is what Stefan will present us now.
Perfect. Thank you Adrian. There are several types of soft sensors and you can use soft sensors whenever for example a physical sensor is simply not possible or is too expensive. But we also see in many cases that process still relies on lab measurements. So a critical parameter that is critical for the quality of your process. You still need to go to the process, extract the probe, bring it to some kind of laboratory environment, wait for a couple of minutes to get the result back and only then you have the feedback from the process. Usually we also see that this is not done very often but perhaps once a day or once a week and that is exactly where the soft sensor can provide help because usually you monitor that exact process with other kinds of sensors. So you already have a lot of insights to that exact process and those measurements that you're already doing, they correlate to the critical parameter that you only have your lab measurement for. So to use that data that is already there, extract the correlation and predict the real measurement. That is what the soft sensor is about. What do we need for that? So we have a three-step plan to achieve this kind of soft sensing technology. First of all, you need process data. So usually you would gather around a couple of months of process data. You store them and that is your basis because in there you have your real measurement that you're searching and you have all the input parameters with all the correlations. The second step is the model creation and we support you by that. We also provide software that can automatically create this kind of soft sensor model from your historical data. So basically you put in your historical data, you do data science and out comes our soft sensor model as a file. The third step then is the operation of the soft sensor. We have a soft sensor engine. We call it soft sensor engine IQ. A software platform which connects to the DCS system. You get all your input parameters continuously in it, continuously calculates the soft sensor value and feeds it back to your system. In that case you can integrate a model a soft sensor into your process and we've done that quite successfully. So you can see here that on the red dots we have the real lab measurement and on the continuous blue line you have the soft sensor data which now continuously comes in. You can make decisions based on that soft sensor value in real time and you don't rely on your lab measurements that only come in once and so often. So in general that makes running a plant, optimizing a plant way more efficient and that is what a soft sensor is all about. So in general we have seen that the environment in your plant is getting more and more complex. You need to react on that. Plants need to respond faster and need optimization. And with the soft sensor and the APC, we can achieve more autonomous behavior in our plants, in our customers' plants, and really enable a self-optimization for that. And with that, a higher degree of automation, higher quality, and a higher efficiency for your process and your plant. And I think if you have more questions, please visit us at the booth. It's number 413 right over there in the innovation hub. Please give us a visit and bring your questions to us. Thank you very much.
Thank you very much.
Thank you very much. Great presentation. Yeah, you can take that with you. Thank you so much.
Thank you very much. Enjoy the rest of the fair, Stefan and Adrian. So, quick show of hands. Who here has seen some of our employees walking around with blue shoes? Have you noticed the blue shoes? Our next colleague has them on as well. And I'm going to ask him on stage by himself for now. Can we please have a warm round of applause for our VP of additive manufacturing, Ken Hoer? Welcome, Ken. Hello. So, these are the shoes I'm talking about. I still don't have any. I'm very jealous. Ken, why have you got these on? Why have our colleagues got them on? What do they have to do with Hanover and anything we're doing here?
You know, I wear everything which is 3D printed: shoes, frames, and whatever.
And I think you may find 100 people at the booth having these nice shoes. Are you interested in a making-of?
This gives you a short glimpse into adaptive manufacturing enabled by industrial AI and additive manufacturing. I have the pleasure and please a warm welcome to my executive guests for today. Please come on stage: Marie Langer, CEO of EOS; Yuping Tang, General Manager from Orisol; and Vivec Koshik, Managing Director and Global Head of the Siemens Accenture Business Group. Welcome. So, additive manufacturing, industrial AI, robotics, how this all comes together. I think this is part of our discussions. Thanks for joining me here on stage and we've seen a first glimpse in the movie some snippets of what's shown here in Hanover with the technology to make manufacturing, and I think for many industries much more adaptive. And before we go into that topic, I think you might look that environment market demands are changing. We have unpredictable demands, we have localized production chains, and this is something I think we have the right tools and even with this kind of companies the right partners in place, and this is something where we have a shoe and we as well have aerospace drone parts with us. Totally different kind of industries, you may say. But if you look a little deeper into that, we will see performance improved parts for a drone in aerospace and performance improved parts in a shoe. And the main reason, the main enabler why this is possible in the same kind of machine and system, this is additive manufacturing. So Marie, EOS is probably known to many in the additive community, probably not to everyone here in Hanover. Can you explain a little bit what a drone and a shoe and additive has in common?
Yes, of course. So at a first glance, you might not see a lot of similarities, but when you look into it a little bit more deeply, you will actually realize that they both need high performance. They actually need lightweight but need to be robust. You want to have them in many variants. So you want to have a quick design iteration and they have some requirements that really demand an adaptive way of manufacturing and that's where actually additive manufacturing comes in. And we see that with many different customers actually. And it can be of course a drone, it can be an insole, but it can also be a turbine vane or a gripper. It's always about going from a digital design to a high performance part. And how many systems you have installed worldwide? I think you're working on polymer additive manufacturing and metal additive manufacturing. Can you give a glimpse of how large EOS is these days?
Yes. So our install base is over 5,000 machines actually. And we operate in all regions. We are a family business. My father founded it 40 years ago and we are actually working in all the relevant growth areas right now. Defense, energy, space, medical, tooling. So yeah, we have a lot to give.
Cool. Yeah, I'm very glad that you made it to Hanover. I had the pleasure of visiting you about 4 weeks ago in Taichung in Taiwan.
And people may have seen that the shoes are produced by Pou Chen group and Orisol is probably not so much known. Can you shine a little bit light? What is the role of Orisol in the shoe industry and what is your opinion on additive manufacturing?
Okay. Yeah, thank you. So I think a lot of people already saw the shoes and probably are asking how where to buy the shoes. So actually this shoe is made by Pou Chen group and also it involved a lot of automation machines but that actually also made by Orisol. So Orisol has been in the footwear manufacturing industry for over 30 years and we developed the first sewing system, computerized sewing system that will do the stitching sewing of the upper. And since the footwear industry is still very labor intensive industry, so actually the automation is only maybe like 10% even under. But after being acquired by Pou Chen group, we have already expanded our automation product portfolio from just a stitching machine to also including the robotic arm cementing the upper and the outsole and also the digitalization with IoT software and also in the additive manufacturing we use EOS to print the midsole and SLM for the metal mold and also we started to use the AI agent for the process optimization as well. So because this is a transformation from the traditional labor intensive industry to more advanced manufacturing. So also we try to pursue this kind of technology to help the footwear industry to become a more automated, digitalized and more sustainable industry as well. So that's why our mission is to make more machines to help you to make this kind of shoes in the future.
Yeah, impressive as well. If you look to the whole manufacturing innovation which happens in the shoe industry, I think in the past we have seen mass customization coming along in some companies and some failed. I remember some of these approaches and the reason behind was that companies could not manage the complexity in the digital workflow. You have a lot size one production. So you have every ERP order is one piece only and you need to manage production and shop floor at the cost level for a lot size one. And I think now the difference is with industrial AI it seems to be a game changer in this area as people will experience here in Hanover. And can you explain a little bit why Accenture and what is your role in orchestrating this whole manufacturing shop floor?
Yeah. So, thanks Ken. Absolutely. I mean we have a great presentation on the innovation hub where we are talking about the whole manufacturing orchestration. So before I go there I want to just carry on what Marie said about the complexity in the shoe sole or what you said about the labor shortage. I mean we see the similar trends across the industry. So products are getting more customized, complexity is increasing. There is a growing need of flexibility in the production and in order to achieve that we need to make our clients' manufacturing operations much faster and also much consistent. I mean and how do we achieve that? That's where we bring the orchestration coming in because the technology is there today as well. But the real challenge is those technologies today in the automation as well they are in silos implemented, they are not talking to each other in a consistent way and that's the real value we at Accenture believe that if you make these technologies come together, work together and create a concrete consistent actions out of it, that's the value you generate. So that's where the orchestration comes in. And let me explain a bit how we do the orchestration. So the way we are running in this program is we are creating a modular network of AI agents. Basically you can think about for a core value chain you think about design engineering going to production and after sales. So you create agents across the value chain. Then we automate — you talked about automation of the machine data. You automate all the processes and you bring the information in a consistent way and give it as a backbone to these different agents and what they do is eventually they orchestrate between themselves and create a coordinated actions out of various demands and that's where when you talk about adaptive manufacturing so that's where the agents can coordinate between themselves handle the demands online and then they give concrete actions and recommendations where engineers on the shop floor or operators they can take that guidance and make the right decision in a consistent way and that's where we are able to orchestrate the whole production and also design and engineering to extend this and really make it an AI at scale story. And to complete the thought, with Siemens we have launched the Siemens Accenture business group last year and we are basically working together create joint offerings in manufacturing and also in engineering space creating this kind of end to end value starting from the strategy consulting to implementation and managed services. And today we have brought with Siemens a joint thought leadership white paper on future of manufacturing which talks about how you can build factory of the future because it's very easy to put the PCs and run it but the challenge comes on the scaling part and that's where we are bringing with Siemens a way how you can bring the PCs at scale in the manufacturing space. It talks about implementation of AI agents, it talks about metaverse. So that's the white paper and thought leadership we are putting behind today with Siemens.
That's pretty cool and I think I showed you already and we could experience to scan your feet, it took about 30 seconds that we out of your feet pressure scan and we generated out of the Siemens technology stack a fully individual midsole product.
Yeah. And then we went over and we looked onto the Accenture AI orchestrator how it's working with the agents in the different software stack of the Siemens tools and we could really optimize and on the fly change the build job with your for sure highest priority order for your midsoles by using a chat prompt into this AI orchestrator. This is I think you probably have many customers using EOS machines and thankfully as well building on the Siemens automation platform maybe interested how are your customers or how are you as a company looking into using or working with your customers on industrial AI.
Yeah. So for example you just shared one way how the operation can be optimized. We of course are also looking into the matter of repeatability as this is something very important for our customers. So making sure that every single piece is looking exactly the same even if it's mass customization you still want to achieve the same requirements. Also here AI can help. We look into service and how we can optimize our service with AI agents and also making it easier for our field engineers to really only go to a premise if they have to do a superior job and solving all the other things through also some of that elements. So there's a lot also you can do of course in the sales side to speed up your cycle. So we look into product but also how we can really optimize our processes across the organization with AI.
And your opinion, Orisol? I learned as well during my visit that you're using as well chat prompts in observing what's happening in the factory. If you look on robotics, humanoid robotics, AI, how is it already in use in your shoe manufacturing industry?
Yeah. So in the footwear factory, even though it's still a lot of labors inside, but still there also a lot of equipment as well and most of equipment not so smart, not like a high-tech machines. So it does rely on a lot of maintenance work to do the daily maintenance and the process configuration. So for that kind of work actually you need a skilled worker, skilled engineer to do that. But actually for those factories, especially most of them are in the Southeast Asia, you don't really have that many engineers to help to work on this kind of work. So we do try to use AI agent, we try to create a chatbot like this kind of service to help you to do the maintenance work. Whenever there something happened maybe you can just type something, then it can give you a quick result and tell you how to do that, how to fix. And also we try to collect all the daily production data and then try to merge with machine status, this kind of information, and give you the advice to help you to try to optimize your process configuration to help you improve your productivity and also try to increase the machines utilization. That's how we try to start to use the AI agent to do this kind of work.
