CEOInterviews.AISiemens | Live from Hannover Messe 2026

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Unknown0:17

Hey everybody. Yeah, you. Hey, hey, hey. Hey. Hey. Hey. Hey. Hey. Hey. Hey. Hey. Heat. Heat.

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Christine16:54

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.

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Max17:26

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.

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Christine18:54

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.

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Brazilian Official20:52

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.

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Christine28:38

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.

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Militia29:58

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.

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Miki31:04

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?

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Izzy31:28

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.

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Militia31:58

All right, I will take you to our first station: the energy and buildings area. Come with me.

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Tour Guide32:49

Hi. Nice to meet you.

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Militia32:55

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.

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Christine33:49

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?

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Maria Schwartz34:33

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.

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Christine35:37

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?

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Maria Schwartz35:55

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.

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Christine36:52

Let's ask our colleagues from other halls. Peter, you are in Hall 11. How do you like your new living room?

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Peter Aman37:10

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.

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Christine39:27

Hydrogen is a topic you should not miss. Hall 11 D20.

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Peter Aman39:38

Exactly.

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Christine39:38

Christian, share insights on the defense industry.

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Christian Scrader39:52

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.

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Christine41:14

Wonderful. We also have a drone at our booth. Max, that's your cue.

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Max41:40

We are handing down to Miki. We offer guided tours. If you're online, use the booth navigator. Over to Mickey.

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Miki42:16

Thank you, Max. With me is Miriam, one of our tour guides. She will tell you about the electrification and buildings area.

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Miriam42:47

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.

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Miki48:37

Thank you, Miriam. How do I join a tour?

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Miriam48:50

There's a tour counter at the left end of the booth. No registration needed. Tours every 30 minutes in German and English.

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Miki49:12

How long does it take?

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Miriam49:15

Roughly 30 minutes. We talk about IE, partners, and Brazil as partner country.

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Miki49:37

This is my first highlight. We did a small tour in the SE area.

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Miriam49:45

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.

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Miki50:13

Have you done this before?

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Miriam50:20

It's my fourth time. I'm proud to work for Siemens.

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Miki50:47

Can you tell us more about Siemens in this context?

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Miriam50:54

In Europe, fluorinated gases are regulated. We developed clean air technology without SF6. We are ready for more than 2 years.

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Miki51:36

Energy and sustainability are important with AI. Tell us more about sustainability.

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Miriam51:51

We need to use electricity efficiently and use green energy. The main CO2 footprint is during operation. With green electricity, no CO2 emissions.

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Miki52:34

Is there another exhibit you are interested in?

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Miriam52:50

The robots are cool, but there's a beer production machine with a digital twin. You can simulate the process.

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Miki53:29

Thank you for the tour. We head back to colleagues.

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Max53:40

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.

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Sebastian54:36

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?

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Cal56:02

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.

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Sebastian57:06

Dirk, can you shed light on foundational technology?

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Dirk57:24

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.

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Sebastian58:45

Linda, how does Siemens Accelerator transfer support into customer value?

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Linda59:06

Siemens Accelerator is the digital business platform. It has three pillars: portfolio, ecosystem, and marketplace. We have about 2,000 offerings, 900 from partners.

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Sebastian1:00:53

Great.

Cal, you recently joined from Google. Were you surprised?

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Cal1:01:28

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.

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Linda1:03:18

We have domain expertise and are opening up the ecosystem. We partner with everyone to solve customer challenges.

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Sebastian1:04:23

Dirk, how does the end-to-end chain make Siemens different?

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Dirk1:04:54

Foundational technologies combines four disciplines: user experience, platform elements, developer workforce (10,000 developers), and research. This end-to-end approach is unique.

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Sebastian1:06:36

It provides a lot of value.

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Dirk1:06:39

We take heavy lifting away so product teams can focus on customer problems. We are the machine room.

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Sebastian1:07:13

I love that analogy. We are proud machine room people.

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Linda1:07:21

We also make it easy for partners to work with us through simplified technical governance.

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Sebastian1:07:47

Dirk, how do you decide which research to back?

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Dirk1:08:02

We work backwards from the customer. We give the environment for sparks to catch fire. It's always about solving problems.

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Sebastian1:08:47

Cal, elaborate on collaboration.

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Cal1:08:52

Innovative ideas come from intersection of university research, internal research, and tech companies like Nvidia and Google. We collaborate globally.

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Sebastian1:10:30

What is the single thing you want the audience to remember? Cal?

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Cal1:10:41

The most innovative product you'll see next year is being developed now in my labs and will come faster than you expect.

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Linda1:10:59

Siemens Accelerator provides ecosystem and marketplace as launch pad for AI and digital assets.

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Dirk1:11:22

We do this to solve big customer problems. Industrial AI has the biggest impact.

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Sebastian1:11:51

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.

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Unknown1:12:37

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.

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Max1:12:58

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.

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Miki1:23:32

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?

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Casten Hoer1:23:53

Yeah, Michael, let's do it.

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Miki1:23:55

Michael, let's do it.

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Casten Hoer1:23:56

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?

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Miki1:24:19

A bit softer.

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Casten Hoer1:24:19

A bit softer, we can tweak it. Now ready to make your personalized product.

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Miki1:24:32

Which parts of Siemens portfolio are playing a role here?

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Casten Hoer1:24:38

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.

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Miki1:25:25

Which role does it play in the production process?

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Casten Hoer1:25:36

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.

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Miki1:26:16

This can be applied to more than shoe soles?

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Casten Hoer1:26:24

Yes, mass customization at scale can be applied to medical implants, eyewear, helmets, automotive, electronics, mold and die.

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Miki1:27:04

Thank you, Casten. If you want your own foot scan and learn more, head to the innovation hub.

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Casten Hoer1:27:24

Thank you for coming.

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Max1:27:28

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.

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David1:28:25

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.

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Max1:37:39

Thank you David. What's your role?

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David1:37:50

I've been responsible for the digital threads area for five months and I'm the area speaker for manufacturing. Happy to give tours.

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Max1:38:06

Thanks again. Handing to Christine.

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Christine1:38:15

Welcome to Hanover Messe 2026 at Siemens booth. AI is a big topic. Make data work for you. Nikolova will present.

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Nikolova1:40:15

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.

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Christine1:56:42

Thank you, Svetlina. Great presentation. What about this mug?

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Nikolova1:56:53

If you go to the CPG end-to-end journey, you can win this mug. Get your coffee.

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Christine1:57:09

Very motivating. Welcome to Hall 27, the living room of industrial AI. Sustainability is core. Please welcome Martin Baya and Erin Devola.

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Erin Devola1:58:56

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.

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Martin Baya1:59:17

Disruptions are our new reality. Natural disasters doubled in 30 years. 220 billion economic losses in 2025. Industries need to prepare.

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Erin Devola2:00:38

Companies historically focused on cost and efficiency. Now resilience is key. Resilient companies anticipate and adapt. Examples: power outage, drought, geopolitics, extreme heat.

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Martin Baya2:02:52

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.

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Erin Devola2:04:07

Pierre Fabre used digital twin of water management to anticipate and optimize water usage.

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Martin Baya2:05:22

Extreme heat days doubled. Use industrial AI for proactive cooling of buildings. Results: 30% energy cost reduction, 30% CO2 reduction.

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Erin Devola2:07:29

Radeberger Group used digital twin of logistics network to anticipate disruptions, achieving higher reliability and productivity.

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Martin Baya2:09:20

There's one more superpower: Siemens Financial Services offers flexible financing to reduce upfront costs.

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Erin Devola2:09:21

Smart financing helps companies become resilient while preserving capital.

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Christine2:09:56

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.

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Narrator2:13:33

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.

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Christine2:14:26

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?

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Alfred Gisler2:15:20

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.

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Christine2:15:56

Mhm.

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Alfred Gisler2:15:56

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.

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Christine2:18:09

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?

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Stephanie Frank2:18:33

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.

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Christine2:20:31

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?

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Thomas Schneider2:20:51

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.

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Christine2:22:31

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?

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Stephanie Frank2:22:50

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.

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Christine2:24:31

Now when we talk about the future, Alfred, what's your perspective?

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Alfred Gisler2:24:38

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.

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Christine2:27:02

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?

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Thomas Schneider2:27:12

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.

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Christine2:28:26

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?

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Stephanie Frank2:28:38

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.

C

Christine2:29:35

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.

T

Thomas Schneider2:30:09

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.

C

Christine2:30:21

And I think hall 13 also covers some of the topics at the industry 4.0 booth.

T

Thomas Schneider2:30:26

For sure.

C

Christine2:30:27

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.

U

Unknown2:30:36

Thank you. Thank you. Thank you.

C

Christine2:30:39

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.

O

Ombberto Noert2:31:33

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.

A

AI Voice2:32:51

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.

O

Ombberto Noert2:33:18

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.

S

Stefan Hansen2:37:22

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.

O

Ombberto Noert2:38:46

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.

S

Stefan Hansen2:39:03

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.

O

Ombberto Noert2:39:33

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?

S

Stefan Hansen2:39:49

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.

O

Ombberto Noert2:41:04

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?

S

Stefan Hansen2:42:08

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.

O

Ombberto Noert2:43:41

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.

C

Christine2:45:10

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.

M

Melissa2:45:32

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.

N

Natalia Oropza2:46:50

To see you.

M

Melissa2:46:52

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.

K

Kristoff Berlin2:48:12

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.

M

Melissa2:49:16

Okay. But now let's talk about the even bigger threats. How do we use that as an opportunity? Natalia.

N

Natalia Oropza2:49:22

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...

M

Melissa2:51:09

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?

K

Kristoff Berlin2:51:30

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.

M

Melissa2:52:51

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...

N

Natalia Oropza2:53:20

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...

M

Melissa2:53:42

Before I go with Kristoff, that brings me to the next topic that I think is key. We need to cooperate.

N

Natalia Oropza2:53:52

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.

K

Kristoff Berlin2:54:25

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.

M

Melissa2:55:30

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?

N

Natalia Oropza2:55:55

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...

M

Melissa2:57:04

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?

K

Kristoff Berlin2:57:22

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.

M

Melissa2:58:08

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?

N

Natalia Oropza2:58:28

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.

M

Melissa2:59:36

Yeah.

N

Natalia Oropza2:59:38

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.

M

Melissa3:00:02

I'm thinking the same for you. You probably have an example for us.

K

Kristoff Berlin3:00:06

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.

M

Melissa3:02:17

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?

N

Natalia Oropza3:02:41

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.

H

Host3:03:21

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.

N

Natalia3:03:32

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.

K

Kristoff3:04:03

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.

N

Natalia3:04:39

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.

K

Kristoff3:04:57

Yes.

H

Host3:04:57

Thank you Natalia and Kristoff. This was an amazing session on cybersecurity. I'm going to hand over to my colleague Christine.

N

Natalia3:05:18

Thank you so much for the partnership.

K

Kristoff3:05:20

Thank you. Thank you.

C

Christine3:05:21

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.

R

Raymond3:06:48

Good to see you again.

S

Shri Iapulu3:06:48

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.

R

Raymond3:12:55

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.

S

Shri Iapulu3:20:52

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.

R

Raymond3:23:16

Thank you.

C

Christine3:23:17

All right. Very good. Oh, whoopsie.

R

Raymond3:23:19

Be careful.

C

Christine3:23:20

Thanks to the two of you.

R

Raymond3:23:24

Thank you.

C

Christine3:23:25

You mentioned where we can find you. How long will you be around?

R

Raymond3:23:30

I will be here after the session on the corner. Come see us if you have questions.

C

Christine3:23:36

Okay, perfect. Thank you so much.

S

Shri Iapulu3:23:45

Thank you.

C

Christine3:23:49

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.

F

FA Alre3:25:52

Hello.

C

Christine3:25:53

Great to have you on the sofa again. Have you been around the fair?

F

FA Alre3:25:59

I love being here.

C

Christine3:26:00

You are in hall 17 with a big Microsoft booth, but this is your second home. Let's sit down.

C

Christoff Balin3:26:25

Yes, I've been around a little. It's incredible to see the speed of things.

C

Christine3:26:50

FA, what about you?

F

FA Alre3:26:52

I walked by, still thinking we are in hall 9. I've seen a bit but not enough. There's more to see.

C

Christine3:27:10

You still have time. Christoff, you work with this technology. How is AI transforming manufacturing?

C

Christoff Balin3:27:50

AI is here to stay. It speeds up processes, increases quality, and efficiency. At Siemens, we deliver with partners like Microsoft.

C

Christine3:28:34

From your point of view, Christoff, how is AI impacting manufacturing and what is the joint vision?

C

Christoff Balin3:28:48

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.

C

Christine3:31:22

I feel your passion. FA, where does AI fit in the manufacturing landscape and what problems does it address?

F

FA Alre3:31:54

Supply chain disruptions and volatility. AI augments skilled workers. For example, CNC programming. AI analyzes data and trains on your data to speed processes.

C

Christine3:33:27

Now, Christoff, on cloud and digital solutions, what is Microsoft's perspective on AI in manufacturing and the cloud?

C

Christoff Balin3:33:44

AI accelerates everything. We are moving to a supervisor model. The cloud enables managing globally while executing locally.

C

Christine3:36:02

FA, what are you showcasing together on AI manufacturing of humanoid components?

F

FA Alre3:36:19

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.

C

Christine3:39:02

One minute left. Christoff, what do you see in the near future?

C

Christoff Balin3:39:13

Far is six months. We will continue the supervisory model. Humans become experts. AI handles hard skills while we focus on soft skills.

C

Christine3:40:06

Great. Visit the Microsoft booth. FA, the near future is in consumer packaged goods.

F

FA Alre3:40:35

Yes sir.

C

Christine3:40:37

We have a gift for our speakers. Thank you so much. Handing over to Militia.

M

Militia3:41:05

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.

M

Miriam3:42:36

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.

M

Militia3:43:59

That sounds like collaboration at its best. Can you give me a tangible number that surprised you?

A

Alberto3:44:13

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.

M

Militia3:46:30

From your perspective, Flora, can you give a concrete result?

F

Flora3:46:44

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.

M

Militia3:47:24

Many think cleaner means costlier. What does the data say?

A

Alberto3:47:40

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.

M

Militia3:48:48

Miriam, what surprised you?

M

Miriam3:49:05

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.

M

Militia3:49:53

Where does this go next? Alberto?

A

Alberto3:50:11

Gravity is ambitious but has an ecosystem. Cooperation agreements with neighbors share the burden. Siemens and Capgemini foster these projects for the whole industry.

M

Militia3:50:56

Flora?

F

Flora3:50:59

Make the plant ready for future technologies. Ensure engineers have a single source of truth from design to operation.

M

Militia3:51:48

Miriam, on scaling?

M

Miriam3:51:49

It's about scaling a model across Europe, not just one plant.

M

Militia3:52:05

We have a white paper with QR code. One closing message each. Alberto?

A

Alberto3:52:33

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.

M

Militia3:53:06

I love that. That is a key takeaway.

C

Christine3:53:08

Lovely closer year, what about you?

U

Unknown3:53:09

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.

C

Christine3:53:19

I love that. What about you, Flora? What do you say?

F

Flora3:53:22

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.

C

Christine3:53:31

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.

U

Unknown3:53:46

Thank you. Thank you. Amazing.

C

Christine3:53:48

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.

U

Unknown3:54:16

Thank you.

Thanks, Militia, for having us. Thank you.

M

Militia3:54:18

Thank you. Great pleasure.

C

Christine3:54:20

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.

A

Alice Alb Govich3:55:13

Great to have you.

C

Christine3:55:16

Did I say it right? Your name?

A

Alice Alb Govich3:55:18

No, my name is Alish.

C

Christine3:55:18

And your name I didn't. Alish. There we go. The stage is yours. Tell us more.

A

Alice Alb Govich3:55:22

Great. Should we sit?

C

Christine3:55:24

Yeah, I think we should sit. It's a heavy topic so we should sit.

A

Alice Alb Govich3:55:28

I've been walking around so much today. I am happy just to sit down.

C

Christine3:55:32

Yeah. So anyway, the topic today is CFD. Who knows what CFD is?

N

Neil Ashton3:55:42

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.

C

Christine4:01:26

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?

N

Neil Ashton4:01:43

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.

C

Christine4:02:37

Okay. Just for my curiosity, how many simulations are usually needed to train an AI surrogate model?

N

Neil Ashton4:02:50

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.

C

Christine4:03:29

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?

N

Neil Ashton4:03:48

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.

C

Christine4:04:47

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.

N

Neil Ashton4:05:14

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.

C

Christine4:06:10

One question about GPUs: do people usually acquire their own or access them on the cloud?

N

Neil Ashton4:06:22

It's completely dependent. We see both. The great thing is there is a great ecosystem where Nvidia GPUs are available everywhere.

C

Christine4:06:53

But how about you? What has changed over the past four or five years thanks to accelerated computing?

N

Neil Ashton4:07:02

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.

C

Christine4:08:12

And maybe we switch to another nice visual.

N

Neil Ashton4:08:16

Yeah, why don't we do that?

C

Christine4:08:18

What's the other thing? Computational fluid dynamics color for directors? I've heard many versions.

N

Neil Ashton4:08:31

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.

C

Christine4:08:48

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.

N

Neil Ashton4:09:32

Yeah, it was good talking with you, Neil.

C

Christine4:09:35

Cheers.

Thank you so much, gentlemen. Thank you. And we have a small giveaway for you, sir.

N

Neil Ashton4:09:44

Oh, thank you.

C

Christine4:09:45

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.

M

Miki4:11:25

Hi, Eric.

E

Eric Fandango4:11:26

Here we are again.

M

Miki4:11:27

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.

E

Eric Fandango4:12:07

Let's go for it.

M

Miki4:12:10

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.

E

Eric Fandango4:14:00

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.

M

Miki4:15:32

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.

E

Eric Fandango4:16:37

It's the foundation, Michael. The foundation that needs to be laid out, and the opportunity that could be correlated afterward.

M

Miki4:16:44

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.

E

Eric Fandango4:20:12

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.

M

Miki4:21:14

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.

E

Eric Fandango4:23:40

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.

M

Miki4:24:04

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.

E

Eric Fandango4:25:00

Thank you for your time. Thank you for all your questions. Super happy to take them all and thank you for your partnership.

C

Christine4:25:15

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?

L

Linda4:28:30

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.

C

Christine4:30:13

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?

R

Rudy4:30:31

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.

C

Christine4:31:46

Great. And happily, they're here listening. Dion, thousands of partners work with Siemens every day. What makes these digital applications valuable to them?

D

Dion4:32:02

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.

C

Christine4:33:38

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?

F

Frederick4:33:56

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.

C

Christine4:34:48

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?

L

Linda4:34:58

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.

C

Christine4:36:52

Do we also check on the expectation of those customers, like how was your shopping experience?

L

Linda4:37:00

Yes, feedback is requested, and we use agents and analytics to understand the customer journey and improve.

C

Christine4:37:14

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?

D

Dion4:37:31

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.

C

Christine4:39:27

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?

F

Frederick4:39:53

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.

C

Christine4:41:13

Here we are, Rudy. How do we at Siemens ensure that these digital solutions deliver expected value?

R

Rudy4:41:26

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.

C

Christine4:42:39

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...

U

Unknown4:43:45

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.

C

Christine4:43:56

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.

U

Unknown4:44:12

Yeah, thank you.

One Siemens, one journey. We definitely take it serious and make it easy for you.

C

Christine4:44:20

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.

U

Unknown4:45:06

Thank you. Thank you.

A

Adrian4:45:09

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.

S

Stefan4:49:23

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.

C

Christine4:53:19

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?

K

Ken Hoer4:54:10

You know, I wear everything which is 3D printed: shoes, frames, and whatever.

C

Christine4:54:16

Yeah.

K

Ken Hoer4:54:16

And I think you may find 100 people at the booth having these nice shoes. Are you interested in a making-of?

C

Christine4:54:24

Yeah, definitely.

K

Ken Hoer4:54:25

Yeah.

C

Christine4:54:26

So, movie.

K

Ken Hoer4:54:27

Yeah.

C

Christine4:54:28

Start.

K

Ken Hoer4:54:28

Let's take a look.

C

Christine4:55:44

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?

M

Marie Langer4:58:12

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.

C

Christine4:59:52

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.

M

Marie Langer5:00:04

Yes.

C

Christine5:00:05

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?

Y

Yuping Tang5:00:26

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.

C

Christine5:02:22

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?

V

Vivec Koshik5:03:29

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.

C

Christine5:06:58

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.

K

Ken Hoer5:07:15

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.

M

Marie Langer5:08:05

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.

C

Christine5:09:08

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?

Y

Yuping Tang5:09:27

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.

C

Christine5:10:49

Yeah. And I think this is something people and visitors can experience here as well with the production co-pilot with the agent and the orchestrator, Vivec, from you.

V

Vivec Koshik5:11:00

Yeah.

C

Christine5:11:01

How you can pretty easily access and work in many different software tools where you are probably not the expert, which makes it very easy. We sometimes say we democratize the use of complex software for many many more people. Yeah. And the last round of question is robotics. We we see the machines being unloaded, loaded with the build jobs with HVs. We have different kind of humanoid robots here in the innovation hub. Who wants to start? Can I program with agents as well robots and how is your view on that?

V

Vivec Koshik5:11:40

Yep. So I mean before I answer that question, just reflecting on what you said, the other thing we talk about productivity a lot but I think the greatest value from the AI and agents are also coming from the growth basically. You can think about business outcomes in the end and what you can achieve much more than just productivity. So of course right now we are in an initial phase. A lot of people talk about productivity but we need to also expand this beyond that and go into the growth path. And now coming to the question about robotics. Yes definitely we with Accenture and Siemens we now created other solutions together where we are gathering all the data models and basically training robots to do this kind of actions. For example, we are working together with pharmaceutical companies where you have a lot of material workflow between the labs and the factories and warehouse and you can simply train robots to do that work. So what it helps is it's not about eliminating jobs of the people but actually you uplift the job on a higher value for the people working on the labs and the shop floor that they can validate actually the actions and all the repeatable tasks can be done by the agents by teaching them over these learning models.

K

Ken Hoer5:13:05

And actually humanoid robots can also be perfectly built with additive manufacturing. We have one at our booth in hall 26, G44. So please stop by and see what this can be doing actually for universities, for students and of course also for the industry.

C

Christine5:13:23

So a big thank you. There are probably a lot of questions from our audience here. There's one spot people can walk over as well ask questions to you as well to ask questions to the team being at the booth. It was a pleasure for me to having this panel with you together today. It's about the adaptive manufacturing enabled by companies, by partners, but as well by technology stack from industrial AI combined with additive manufacturing. Thank you. Thank you for all of you listening in our session.

K

Ken Hoer5:13:59

Thank you, Ken.

C

Christine5:14:04

Thank you so much. And of course, you get a gift from our customer as well. Something for you to enjoy.

K

Ken Hoer5:14:11

And I've already screwed it up. There you go.

C

Christine5:14:13

Thank you.

K

Ken Hoer5:14:14

Thank you.

C

Christine5:14:14

Thank you so much for joining us, Ken. Thank you so much. You don't get one, but you have the shoes.

K

Ken Hoer5:14:20

Thank you so much.

C

Christine5:14:21

The shoes as well.

Thank you. Round of applause again for our guests. Thank you.

Good stuff. And now for this next session, I'm going to hand it over to Militia who's going to be your host. So Militia, the stage is all yours.

M

Militia5:14:33

Thank you.

C

Christine5:14:33

Enjoy. We'll see each other later.

M

Militia5:14:37

Yes. So, let's take a little closer look at something that every manufacturer is going through right now. More variance, shorter life cycles, and too much coordination. Something needs to change. And the big question is, how do you build systems that are smarter enough? And this session is all about that. So, it's a great pleasure to welcome Oliver, sector lead for industrial products at Deloitte, and Mark, head of operation software at Siemens. Great to see you both.

O

Oliver5:15:17

Thank you.

M

Mark5:15:17

Great to see you.

M

Militia5:15:20

So, we see the challenge from different angles. Let's become real here. Let's start with the reality. Oliver, when a small change in a product actually happens, what goes on behind the scenes before it actually goes to the factory floor?

O

Oliver5:15:39

Yeah, whenever there's even the smallest product change, right, this triggers a lot of changes in the different domains across the entire chain. It is from design to simulation to automation and then it goes to operations, right? And all of those domains they have their own tools. They use their own data systems. And there happens to be inconsistencies because it has to be transferred from domain to domain and that is a cause for inconsistencies but also for slowdown and therefore it sometimes takes longer than we would like to see it.

M

Militia5:16:17

So the problem isn't complexity, it's coordinating the complexity.

O

Oliver5:16:22

Absolutely. I mean we have the tools for many years. They are matured. They are good. And the problem is in the interfaces to really take the data and bring it to the next domain to the next team and have them work together. And that is the challenge today.

M

Militia5:16:40

But there's also some pain. Where does the gap hurt the most? Is it engineering simulations or once you hand over to operations?

O

Oliver5:16:50

The pain is all over but it gets bigger and bigger the further you get in the process. Right in the beginning it's still only the design. But if you put it through and then you have to change something, you have to change across the entire chain and you have to go back to design. So the more you proceed, the more pain happens.

M

Militia5:17:14

Wonderful. And Mark, yes, so how are we not getting it right to make this continuous?

M

Mark5:17:21

Well, I think listen, we're all engineers of some kind and I mean I use that loosely and we work in our disciplines and there's clear handoffs that have been established for years between those disciplines. So I think, you know, like Oliver said, we have a lot of tools and a lot of standards and practices. But when change accelerates, you know, if you only had one new product or you didn't have all the complexity, we wouldn't care as much about the handovers because they would only happen occasionally. But when they happen continuously, they expand. But I think there are a couple of other things. It's not just about the process. In my mind, we also need to treat the factory as a product. So what do I mean by that? If you look at a car today, we have a car and you want to crash test that car. You run tens if not hundreds of thousands of different variations of that car through a crash test virtually to figure out what's the best car. We do not do that with factories today. How many have hundreds of thousands of virtual different factory setups that they've simulated for optimal throughput given product changes? Very, very few. So getting that sort of breadth of treating the factory as a product as well on top of the process are two things I would point to.

M

Militia5:18:38

So what needs to change to make this work as one?

M

Mark5:18:41

Well, so one, we do need to have a very clear architecture. One of the things that we might be bad at the closer we get into production is we tend to say if it works don't touch it. What do I mean by that? When a factory goes into commission, whatever it came with of systems and machines, it stays that way, right? And then you build multiple factories and then you have a lot of different blueprints. You need a more clear architecture. Sure, the physical assets will not change as fast as the software, but there's no reason today to have seven different MES systems across your 35 different factories. So if you want to have these handoffs working, the more disparate systems of the same sort that you have across your blueprint, the harder it's going to be. So you need to keep not just the product definition up to speed all the time and in sync, you need to keep your factory blueprint and architecture up to speed as well.

M

Militia5:19:44

I heard something about this 80% come up somewhere. What does that actually mean? What is it and what does it mean for the manufacturer? Maybe all of you. I'll start with you.

O

Oliver5:19:58

Yeah, I mean it's important that we reduce the steps that we have to take, that we go faster through the process, and obviously with that we reduce the time. And we need to make sure in today's world where product cycles are faster and faster that we also reduce the cycle times in our operations, right? And this is the 80%. It will not happen in every industry in every setup, but up to 80% we will be able to reduce in time from when we have a change in a product until we get it produced. And this is where the different domains have to work together and then we will get to this.

M

Militia5:20:41

Wow, you heard that right? Different domains have to work together. Is that even possible? But anyway, Mark, what do you say about that 80%?

M

Mark5:20:49

So for me there are two 80%s if you want. One is when you are commissioning a new factory or new line. I talked about building a digital twin of that factory and simulating hundreds of thousands of different permutations. If you do that up front, we have proven that you can get up and running 80% faster with your virtual commissioning in factory. And by the way, you get fewer errors that you need to tweak afterwards after you've gone into production. So that's the first 80%. But there's also: I have a number of factories and I'm doing product changes in production. And if you've built a chain from your EBOM to your MBOM to your bill of processes and those things are cascaded together, back to what Oliver said, that you have workflows between those, then you can do new product changes 80% faster and get into production. So it's all about time to market, which is key for a lot of our customers.

M

Militia5:21:44

This vision doesn't sound new. So what is it actually? Is it actually achievable? What do you say about that?

M

Mark5:21:50

Very short. I'd say listen, none of this is new. We've always talked about time to market and so on. It's more that the things that yesterday took 10 years to make a reality because of technology can now be done in days or months. We've spent a lot of time building a software stack, building a blueprint together. So there is now a reference architecture that you can match up against and there are deployment methods that are orders of magnitude faster than they were before. These chains do exist. So I would say the big difference is you could always do this. It's a question of how much effort. You can now do it with orders of less effort.

M

Militia5:22:29

I'd like to hear Oliver's take on this.

O

Oliver5:22:30

I would like to add to this. No company can do this on their own, right? The tools are there. The domain knowledge is there in the production teams at site. But if anybody wants to do it on their own, it will not happen. So we have to work together and that's the reason why we partner with Siemens to say we have the technology, we have the domain know-how, and if we bring it together with the clients that are running the facilities, then we can make it happen. If we don't do this, it will take another decade and we will not get there.

M

Militia5:23:00

I love that you said the magic word. It's partnership and that is what this is all about. We wouldn't be here if it wasn't. But just coming to a final takeaway for our audience and the leaders in this room as well. Where do you start and what does success look like? I'll start with you, Oliver.

O

Oliver5:23:20

Success we have if the engineering and production teams work as one team. If they don't, they hand over things and data and we create that data again and again and create inconsistencies. But we have one data flow, then we have success. And from a business perspective, if we really are able to help our joint clients to reduce their production cycles so that they can come to market faster, because that brings them money.

M

Militia5:23:48

Sounds like a plan. If you would talk about what success or winning really looks like, Mark, what does that look like to you?

M

Mark5:23:56

Well, so for me winning would absolutely be that we are at that stage where we have an autonomous factory where I can make a process change or product change, sorry, and then I can get in real time all the cascading steps that need to change and the impact that goes back and forth. So sometimes you can choose to make a design change in your product and other times you might make a change in the factory and you can do those trade-offs back and forth. We will have new regulation that comes in, ESG regulation, supply chain. Unfortunately the world is an unsteady place today, right? So a place where we can adapt to those changes in real time. That's what success looks like for me.

M

Militia5:24:41

That sounds amazing. But you just tapped into something that I find is relevant. There's not just success lying out there. You do have some challenges. So maybe we can just talk about what are those big challenges from your point of view, Oliver?

O

Oliver5:24:58

Yeah, it's you very seldom have a green field approach where you can build everything from scratch, right? You have to integrate the systems that you have. You have to have a clear plan when you adjust and improve your systems. How do you do this? It's a long journey over years where you start with one domain and then you go to the next domain. And I think this is really the challenge. It's a change process for the next couple of years. It's not going to be 'I decide today and then I take out all of the systems and put in new systems.' And it's also not necessary. The systems are working. We just have to make them better and bring them to the next level where this consistency is better.

M

Militia5:25:44

Are you hearing that from Siemens too?

M

Mark5:25:46

Oh yeah. I couldn't have said it any better. You know, we should not go for wholesale change. Look at your biggest business pain points. Look at the reference architecture. Build a heat map of where are the biggest deltas, where do you get the biggest bang for the buck, and then start iteratively working towards that.

M

Militia5:26:10

If you would just very much round this whole thing up and tell us, looking at this partnership, looking at what's lying ahead for the next 5 years, what are you excited about, Oliver?

O

Oliver5:26:22

I think we're at a tipping point where we not only have the technology and the tools, we are at a point where we can really make that change, right? And automate the automation. We can go to smaller lot sizes. We can really revolutionize the way we produce and bring those teams together. We have had good tools for many years but now we have the opportunity to really have this one data layer to orchestrate it and we also I think have not only in our firms but also out there with the firms this understanding that a change is necessary and we have to get that further step. So I do believe everybody is ready to take that journey.

M

Militia5:27:08

I believe so too. What excites you in one sentence?

M

Mark5:27:10

What excites me is at the big picture level, to be honest, is I don't think that we can solve the world's biggest challenges if we don't get together, you know. So what do I mean? We know that we're all getting older, right? We need personalized medicine. We might need autonomous robots that take care of us. We will all demand personal products. We will demand them more circularly and more economically. We're not going to solve those problems and be able to deliver that if we don't digitize the entire value chain. And multiple areas have already fallen. It's just on the tipping point of all this coming together. So that's what I'm most excited about is I think we as manufacturers, as design engineers can really solve the problems of tomorrow and it's right there.

M

Militia5:28:04

Well, that sends goosebumps from top to bottom. I know you're here in the booth. We do have a slide where we can find you. Tell us where's the booth. Tell us where is it.

O

Oliver5:28:15

It is right over there.

M

Militia5:28:18

There we go. So you can find them over here. Please look out for these two gentlemen, Oliver and Mark. It was great to have you here. I'm looking forward especially to learning how tools — it's not about having many tools. It's about how we connect and how they work together. So for me that was a key takeaway. Thank you so much for your amazing time here and looking forward to all the other innovative topics coming up.

O

Oliver5:28:43

Thank you for the time.

M

Mark5:28:45

Great meeting.

C

Christine5:28:46

Thank you. And we have a present so don't run away. There's my wonderful colleague. Exactly. Thank you so much Melissa. Great hosting obviously and Oliver. Every guest speaker gets a little gift from Siemens which is brought to us from Brazil by our customer Natura. They produce great cosmetics with our technology and they sponsored this event. So, thank you so much. I hope you will be absolutely taking it or handing it over.

O

Oliver5:29:14

I share with Mark information. All right. Enjoy the rest of the fair and thanks for sharing your success stories here. Thank you so much.

C

Christine5:29:26

We'll move on with another success story where Siemens and partners come together and it's always a great time at Hanover Messe when we see the vast amount of delegations who come here from all over the world. This year, Brazil is partner country. And as a matter of fact, nice coincidence, 45 years ago, when Hanover Messe came up with the idea to have something like a partner country, it was Brazil who hosted for the first time the partner country activities. And I see a lot of fans from Norway over there as we are having the Norwegian colleagues and the delegation here on site. They have been partner country two years ago if I'm not mistaken. After you it was Canada also. Greetings to Canada. That was also very very cool. But Norway for sure you set benchmarks here. So thanks once more. Partner country heartbeat is for you. Hope you're enjoying and having a good time. And as we are going international, there is lots of customer success stories we are showcasing here at the booth. Lots of customers and mentioned often already today, it's on the right side, to your left that's where we have our digital enterprise. Last year it was for pharma industry, this year we are focusing on the consumer packaged goods. And when you think of how many products you're using from early morning until you come to work, maybe that all is somehow filled into tubes in cans. You get your coffee grains, you get your shampoo. You get your cream, you get your toothpaste. All of that is produced — not all of them with Siemens technology, that's what we're aiming for. But we have the right solutions to make that for our customers who are in the need of producing flexible, smart, adaptable, and of course sustainable, resilient. So lots of challenges our customers are facing where we're supporting. You're facing a tough time at the fair, I suppose, as you have been running around already a lot. You might wonder what those shoe sole cases are about. That's a 3D printed copy — or what do you call it? A 3D version of your foot sole. So it's very very easy for you and soft as it's totally made for your feet to fit. We're not going to offer this here, but you might think of the technology because that definitely is a no-brainer when going at a fair. That could be your next business project. And business project, this is what we're going to talk about now. From factory floors to smart ecosystems, this is what we will be focusing on and how the next manufacturing leap can be powered. And for this we have experts from Singapore joining us here on site. As mentioned, delegations are the source of the exchange and innovation at Hanover Messe. Please help me in welcoming them on stage. It's Isabel Chong, Cindy Ko, and Chin Chong Chu. Hello Isabel. Nice meeting you. Cindy, great having you on site. Welcome to the living room of innovation. This is the place where industry meets and this is always at Hanover Messe a great time when we have customers, partners, institute representatives joining us here. So, how are you doing?

I

Isabelle Chong5:33:10

Great, great, great.

C

Christine5:33:11

Great. Isabelle, you're always in the lead of bringing people, your customers and partners to the fair booth to exchange some stories. I'm doing a quick who's who. That's Isabelle Chong. She's the Senior Vice President for Siemens Digital Industries from Singapore, accompanied by Cindy Ko, Executive Vice President of the Singapore Economic Development Board. That is a lead government agency responsible for Singapore's manufacturing sector. So, we know here how much is on your plate, I suppose. And we got Chin Chong Chu. He is the CEO of Innovative Tech. Thank you so much for joining this round here. Let me just quickly jump into the question as we don't have that much time. Is manufacturing at an inflection point from your perspective? Cindy, I'm going to start with you. What defines advanced manufacturing today and where does the ASEAN region currently stand compared to global peers?

C

Cindy Ko5:34:15

I would say that advanced manufacturing is not — there is no narrow specific definition. I see it more from a perspective of a pyramid, whereby your company's looking at automation, robotics, digitization. But then with the emergence of AI, how do you move up the ladder in terms of capabilities to be AI-enabled and eventually AI-driven? So AI — so automation you can look at it from a production workflow process. The automation can happen at different work part steps of the workflow and fully integrated digitization. Same thing: AI can be applied to specific use cases. But at the top of the apex is where AI is actually used end to end. Now, where ASEAN is: I looked at some statistics. In terms of robot density, because I spoke about the level of maturity in Industry 4.0 adoption and how you are actually using AI, within the rankings of robot density, Singapore probably is the only country right now in the top 10 in terms of robot density, number two just behind South Korea. So there is a lot of opportunities in ASEAN for that transformation of the installed base when it comes to manufacturing. Now ASEAN as a whole today, manufacturing contributes about 22% of ASEAN's GDP, that is much higher compared to Germany or Europe. Europe is about 15%. North America is about 10% contribution. And in 2024 we see that FDI, that's fixed direct investments, going to ASEAN increased by 8%. And that's been probably one of the highest in terms of the regions. And I would say that manufacturing is the second largest contribution to that fixed direct investments. And this is in the high growth new emerging areas but also in the building of that smart factory ecosystem.

C

Christine5:36:04

That's a great overview. Thank you so much, Cindy. And I think we have to wrap up here. It's been an amazing session. Thank you all for being here. Please visit our booth for more.

U

Unknown5:36:22

Providers I spoke about, there's an opportunity for the transformation, and that's why we also see that companies in the ecosystem are enabling that transformation, IoT 4.0, to make plans more AI-driven.

C

Christine5:36:37

Okay, thank you so much. Very detailed. Thanks for the insights. Now, Chong Chang, maybe you explain first what Innovate Tech does and then come to the advanced manufacturing question from mine and where you see your peers today.

C

Chong Chang5:36:54

Okay. Innovate Tech is a company focused on using manufacturing technology innovation to help in advanced manufacturing, which is constantly evolving. They demand continuous innovation to stay competitive. We focus on agentic solutions that understand the manufacturing environment, especially dynamic environments, to help make data-based decisions. We serve semiconductor advanced manufacturing, packaging, and PCB, which is also evolving and requiring more advanced technologies.

C

Christine5:37:42

Okay, great. Now let's focus on how AI is playing a role in manufacturing in ASEAN. I'm going to start with you. What are the biggest barriers preventing companies, particularly small and medium enterprises, from scaling advanced manufacturing adoption?

Give it a go.

I

Izzy5:38:11

I think skilling is a global problem, not just in Germany but in ASEAN as well. We can't scale and deploy AI the way we want. It's not about awareness anymore, but scaling adoption. SMEs run on tight margins, so ROI is top of mind. If they don't understand the value AI can bring, scaling is always a problem. Also, many factories in ASEAN have been operating for decades with different brands and systems, making integration for AI very challenging. In Singapore, we have sandboxes like ARTC where industry players can roll out AI in a risk-free environment. We've developed more than 100 factory solutions there.

C

Christine5:39:52

Okay great. It needs a lot of trust. You were nodding, Chong Chang. Can you explain how Techwave prevents companies, especially SMEs, from scaling advanced manufacturing adoption?

C

Chong Chang5:40:11

Yes. First, we believe in building a strong ecosystem as a partner. This is a golden opportunity for Southeast Asia and Singapore. We tap into partnerships and focus on solving critical challenges like skill shortages. With agentic solutions, they understand dynamically what's happening in real time and make data-based decisions. Agents can talk to different agents, collaborate across the whole manufacturing floor, and make multi-dimensional judgments to solve complex problems and identify real bottlenecks.

C

Christine5:41:38

Mhm.

It seems like you solved the problem. You sit here so calm and relaxed. Now, how can governments, industry, and academia collaborate more effectively to build a future-ready manufacturing workforce? I'm going to face to you, Cindy.

C

Cindy5:42:02

Manufacturing contributes about 20% of GDP, and we've made a long-term decision to keep it at that level. For pre-employment, about 45% of our tertiary graduates earn STEM degrees. Industry participates on university advisory boards to keep education relevant. We also support continuous education and upskilling through aggressive grant schemes and career conversion programs. And we remain open to global talent to attract the best minds in advanced manufacturing and new areas like semiconductors and biopharmaceuticals.

C

Christine5:43:47

I think you face the other way around. A lot of talent from your side goes to Europe and the US. That's a bigger problem because you have the really big talents going elsewhere. But you still have enough. Isabelle, you want to sum it up?

I

Izzy5:44:11

Sure. Technology alone doesn't push AI adoption. We need a good ecosystem. We brought together our governments and themes to create a continuous pipeline for execution and scaling. At the end of the day, it's about how fast we can bring innovation to have industry impact. The most important message to customers is that Siemens has the muscles and experience to help clients deploy and scale at the speed they want. Thank you very much for having us here.

C

Christine5:45:27

That's not too far away. Don't miss the chance to see more developments. Thank you, Cindy and Isabelle. Big round of applause. We have gifts for our external speakers, cosmetics from Brazil produced with Siemens technology. For our external speaker Cindy V from Siemens.

Thank you so much. Big round of applause once more. With this, I'm leaving the stage and handing over to my wonderful colleague, Militia.

M

Militia5:46:10

Thank you. Thanks, Christine. Now that's what you call smart ecosystems. We just saw what factories can do. In every factory, decisions are made almost every hour on the shop floor. The challenge is not just the decisions, but how accurate and on time they are. In this session, we'll talk to our customer and partner Embra about connecting the shop floor with real-time data. Please welcome Gverson, head of Industry 4.0, and Renato, data scientist. The stage is yours.

G

Gverson5:47:08

Thank you so much.

Hello everyone. It's a great pleasure to be here at Hanover Messe 2026. We are here to talk about Embraer's digital transformation strategy for our shop floor. Embraer is the third largest aircraft manufacturer, with over 9,000 deliveries worldwide. Every 8 seconds, an Embraer aircraft takes off. Our success is strongly connected to innovation. 50% of our revenues come from innovations made in the past five years. We have innovation verticals covering zero emission, autonomous flight, AI, cyber security, and Industry 4.0. I lead the MAPA 4.0 process, addressing pain points and needs in different areas. We assess and recommend technologies for specific domains. Our business competences include asset management, augmented operator, digital industry, predictive quality, and industrial efficiency. I will now invite Renato, our AI specialist, to present the Gamma Connection project.

R

Renato5:52:45

Let's look at what really happens on the shop floor. Decisions are constant, but data is not always available with quality. Manual inputs are slow and unreliable. Why is real machine data important? Automatic status determines reliability. OEE is a key KPI. With a strong manufacturing platform like Gamma Connection, we connect existing machines and use the Casite hub to organize data. We build smart analytics and AI on top. Before Gamma Connection, we had manual inputs and assumptions; after, we have near real-time data, trusted insights, and proactive actions. For example, we can identify trends and process deviations across shifts or machines. By detecting bottlenecks early, we move from variation to standardization. This improves OEE, investment decisions, and reduces manual inputs. Gamma Connection is not just a dashboard; it's how manufacturing uses data for better decisions. Thank you.

G

Gverson5:56:46

Thank you so much. This is an important initiative for our shop floor because we have a huge machinery legacy. By integrating those machines and providing real-time visibility and data, we can go to the next level of productivity and achieve great results for Embraer in the future.

M

Militia5:57:31

All right. We won't let you go empty-handed. We have presents for you. Thank you for showing us how to enable better decisions. This shows that working together can achieve a lot.

If I would say something for these leaders over here and this audience, what key message would you have for them about the importance of what's happening here?

G

Gverson5:58:04

I think a good message is that it's not just about technology for its own sake. We need to go deep into our processes, understand the real pains, and deliver real value for the company, using technology as a means to achieve greater results.

R

Renato5:58:40

And real data changes manufacturing decisions. That's the most important for me.

M

Militia5:58:48

That was a strong message. Thank you very much.

And while we're at it, my colleague Christine will take it over. I'll lead you off the stage and hand it to my beautiful colleague in orange.

C

Christine5:59:06

Oh, wonderful. Thank you so much. Thanks for the presentation. Ladies and gentlemen, we're up for our next session. Take a seat if you'd like. Welcome to the LinkedIn Live community. After this session, there's much more on siemens.com/hm26. We've heard about data ecosystems, AI, scalability. Industrial AI can solve tough challenges, but scaling takes technology and trust. Our next panel is on how data ecosystems can create new value and shape the next era of industrial AI. Please welcome Rob Smith, Christina Vagner, and Peter Curt.

Rob, great having you here. Christina, great you're joining. Peter, welcome to Hanover Messe. Let's take a seat on the sofa of wisdom. It's always smart and clever people who know how to innovate. Why don't you quickly introduce yourselves?

R

Rob Smith6:01:34

Thanks, Christina. My name is Rob Smith, CEO at Keon, the supply chain solutions company.

C

Christina Vagner6:01:41

Hi, my name is Christina Vagner. I'm CTO and Chief Digital Officer at OB, a space company.

P

Peter6:01:51

And as you can see on the screen, my name is Peter and I'm looking after technology and strategy at Siemens and a lot more.

C

Christine6:02:05

You brought this round together, Peter. At first glance, it's an unusual combination: Siemens, Keon, and OB. What brings you together?

P

Peter6:02:31

Well, these are great partners. It's all about trust. We want to master big challenges like geopolitics, supply chain uncertainties, demographic change, skilled labor shortages, and sustainability. We face the same challenges and we need to solve them with technology, working smarter with data and knowledge. That's what unites us.

C

Christine6:03:44

That's a great approach, but easier said than done. Christina, where do you see industrial AI having the biggest impact in industry?

C

Christina Vagner6:04:00

For us at OB, industrial AI is a catalyst for two major areas: accelerating engineering lifecycle and strengthening mission operations. Satellite constellations are getting more complex, and we need to develop much faster. AI condenses engineering cycles from weeks to days or hours, improves traceability, reduces human error, and helps with technological sovereignty. Once in orbit, AI helps operate mission control autonomously and find anomalies. The future in space is AI-driven and software-defined. To lighten things up, if NASA had printed all Apollo documents, you could build stairs to the moon. With Artemis, you'd need high-speed escalators, and AI helps us move through that complexity.

C

Christine6:07:14

Nice example. There's a lot of AI involved in sending satellites up. But you need trust. Rob, where do you see the greatest challenges and opportunities for industrial AI at Keon?

R

Rob Smith6:07:48

Thank you, Christina. I'm excited to announce our strategic partnership with Siemens, connecting the real and digital worlds. The supply chain is always in motion and more complex than ever. To make it resilient and flexible, we need digital twins. A digital twin becomes the blueprint for the physical twin and operates it in real time, instructing humans, robots, and automation. Physical AI can do that for the supply chain. We have an autonomous truck running in a large facility. With our work on the solutioning suite, we can scale that across the supply chain.

C

Christine6:10:14

Wow. You're thinking of a fleet optimizing the entire supply chain with digital twins. Peter, what is still holding industrial AI back from scaling faster?

P

Peter6:10:30

We love partners with big challenges. To build something in space or revolutionize the supply chain, you need intelligence and data. The difference with consumer AI is that industrial data is proprietary—companies won't publish their designs. The challenge is building open data ecosystems to collect data from production, engineering, and design. By partnering and exchanging data with domain expertise, we can scale models universally. Siemens, Keon, and OB together have enough data to build industrial foundation models that encapsulate knowledge without violating IP. That's the only way to do it.

C

Christine6:13:00

Seems like you have a clear path. Rob, why did Keon decide to join this data partnership so early?

R

Rob Smith6:13:26

There's a lot of trust and a shared vision of connecting the digital and physical worlds with industrial AI. The industrial foundation model accelerates engineering and manufacturing. No single company has all the data, so partnering with trusted companies creates a first-mover advantage for Keon and our industry.

C

Christine6:14:24

Christina, aerospace data is highly sensitive. What made this data collaboration worth pursuing for OB?

C

Christina Vagner6:14:48

There's urgency because time to orbit is the new KPI. Space data is among the most sensitive—mission-critical, security-relevant, proprietary. A partnership must guarantee trust, protection, and governance from the beginning. We chose to join early to help shape the rules, not just follow them. Siemens offers a trusted platform, domain expertise, and a broad ecosystem. The cultural fit, with high expectations on quality and engineering excellence, makes the collaboration natural.

C

Christine6:17:42

That sounds great. Peter, what will the future look like with these collaborations?

P

Peter6:18:03

The future is already here, just unevenly distributed. Rob and Christina are bringing it here, giving them an unfair advantage. At Hanover Messe, we show the IGEN engineering agent that helps industrial automation scale faster. But we want to connect design, engineering, production, and operations through the digital thread. That's the industrial foundation model—optimizing end-to-end, considering material properties, layout, and manufacturing simultaneously. In Europe, we have the domain knowledge to do this. With partners like Keon and OB, we can build and scale the industrial foundation model faster than anyone else.

C

Christine6:20:30

Great vision. We heard a lot about the future. Rob, what are your expectations for the next few months?

R

Rob Smith6:20:56

The future is absolutely right now. The faster we create together, the faster we realize it. We've learned from our partnership with Nvidia to get on an innovation cycle every two or three months with shows like CES, Hanover, GTC. We're accelerating innovation to bring value to customers multiple times a year. It's a very rapid cycle.

C

Christine6:22:04

As long as we keep the fitness program, we'll be up for the challenges. Christina, any ideas you want to carry home from Hanover?

C

Christina Vagner6:22:38

Definitely. All of us have similar challenges. The level of industrialization needed for volume production, and systems that need to be innovated during lifetime, shows we need flexibility and software-defined, AI-capable systems from the start. That's the innovation leap space and aerospace companies must master.

C

Christine6:23:47

Thank you so much. Great insights. Peter, innovation is your topic. Thank you for sharing. We're proud and honored you teamed up. Big round of applause. We have gifts from Brazil, cosmetics made with Siemens technology. Enjoy the time at Hanover Messe.

Thank you, Christina and Rob. Dear LinkedIn community, thank you for joining. I'm handing over to Militia for the next session. Stay tuned on the Siemens digital industry channel or visit siemens.com/hm26.

M

Militia6:25:26

Welcome everybody to our live audience. We're streaming live on LinkedIn. We've spoken about data. The problem is data is stuck in silos. This session is about what happens when data becomes a living system. I'm happy to welcome Guiam, Director of Digital Manufacturing Innovations at Siemens, Mike, Omniverse Manufacturing Lead at NVIDIA, and Emad, Principal Partner at AWS.

We've been talking about partners. How does it feel to be here?

M

Mike6:26:48

Good. Fantastic. Thanks for having us. It's an awesome day.

M

Militia6:26:57

Let's get right into it. Manufacturers have been digitizing for 20 years. They have data. Why is it still so hard to see the factory at once?

G

Guiam6:27:08

You're right. Customers have data—3D layouts, point clouds, simulation models, IoT streams—but they are in silos. Logistics, production, facilities each have their own version. When trying to solve a problem, you spend more time getting the right context than working the problem. That's what Digital Twin Composer solves.

M

Militia6:27:56

Emad, is this just a Siemens customer problem?

E

Emad6:28:03

We speak to many customers across automotive, CPG, pharma. They have the same story. They invest heavily in digital transformation for individual processes, but when a plant manager asks, 'What happens if I change the line?' no one can give a real-time answer. Digital Twin Composer provides that solution on top of existing systems without requiring migration.

M

Militia6:29:03

You mentioned Digital Twin Composer. What is it?

G

Guiam6:29:13

We're seeing it live behind us. It streams multiple data sources—3D scans, simulation models, facilities, assets—into one scene. It's a SaaS cloud application accessible via web browser. It does more than build nice scenes; it governs data so everyone has the right version. Powered by Teamcenter for configuration management, the digital twin evolves over time.

M

Militia6:29:55

Mike, what makes this experience possible for factories to scale, and what does NVIDIA bring?

M

Mike6:30:04

Great question. Many things came together. I want to underscore what Siemens built with Digital Twin Composer—a comprehensive factory digital twin. NVIDIA provides the Omniverse platform for real-time collaboration and simulation. This combination enables scalable digital twins that can be used across the enterprise.

U

Unknown6:30:18

The cloud connected to all of Siemens' stack running on NVIDIA accelerated compute and AWS in the cloud. This is something that's never been done before. It's never been possible to even simulate an entire factory, much less do it in the cloud and connect it to all these amazing systems. So some of the things that make this possible like NVIDIA accelerated compute through AWS, as well as AI acceleration. So what we're building here with digital twin composer is the foundation upon which all of the future and now AI acceleration is going to be possible so that these facilities can become living, breathing, self-optimizing, and continuously improving.

H

Host6:31:02

And that's how you bring it to life. That's how you make it a living factory. I love that, Militia. So, PepsiCo, 20% throughput and 90% of issues caught before anyone touches the physical line. Now, I just want to know what these numbers are. You probably do as well. So, let me ask all three of you. I'll start with Yugam.

Y

Yugam6:31:24

Sure. Well, PepsiCo is an interesting story. By matter of fact, the video that you've been seeing behind us is taken directly from the collaboration we have with this great customer. And there are challenges that constantly have to reinvent themselves, constantly have to try to be more agile, produce closer to the consumer. And to do that, you really have to try out so many different options in a virtual world. The first example we saw was trying to repurpose an existing facility, but maybe introduce a new production line to be able to have a more resilient and agile supply chain. But I would also say it goes beyond just the visual layout aspect. It's really about predicting performance. And what we're seeing in this second example is how, because now all the data is coming together with a simulation model, you can actually try different parameters and with the power of AI, you can very quickly iterate through many different options, quickly see which ones will make the most sense, and then zero in on the optimum. All doing that in the virtual world. Maybe the key metric that really caught my attention is when they mentioned 90% of issues caught before implementation. This is really the key saver here. You have the freedom to explore in this comprehensive digital twin, catching 90% of issues or hopefully more before you implement.

Yeah.

H

Host6:32:47

How are you experiencing that?

A

Amad6:32:49

Yeah. You know, I think just to build on what Gom said, you see it in companies like PepsiCo that talk about tens of millions of dollars saved. Right? Because a lot of the problem solving that's done is done by trades during the time of installation. It's when the equipment's on the floor, the facility is down, sometimes up to a million dollars an hour in downtime. That's when these changes are really costly. And so what companies like PepsiCo were able to do is test different scenarios, test different options. And now we're able to have a time machine for those options. So we can not only go back in time and see what's happened, but we can look ahead. We can start to see what changes might be coming down from the supply chain from global supply disruption and how we can reconfigure our facilities on the fly to really realize this idea of an ideal factory of the future.

H

Host6:33:38

Well, this is such an exciting topic. Looking at the time, we always talk about, by the way, hashtag partnerships. So, this isn't a one-way thing. It's a three-way effort. What makes it work?

A

Amad6:33:50

Yeah, we have a strong partnership with Siemens and with NVIDIA for a number of years. The thing about it, you know, Siemens is a pioneer in manufacturing, understands very well customer problems in manufacturing. NVIDIA is a pioneer and actually creates all those kind of rendering at high frame rate, and we will develop the infrastructure for the customer. So the customer doesn't have to worry about any underlying infrastructure. They just focus on their solution from Siemens and NVIDIA to leverage their use cases.

H

Host6:34:27

Let's hear what Mike has to say about this partnership.

M

Mike6:34:29

Oh yeah. Yeah. I mean, really, Militia, what makes it work is early mornings, late nights, sometimes work on weekends, and fundamentally a passion for solving the problems of this industry. We've all been in this space a long time. We've all seen the challenges that customers face as they try to digitize and modernize, and now we have the tools that can solve these problems. And I think that's really what fires up Siemens, that's what fires up AWS, and definitely us from the NVIDIA side, being able to solve some of these problems now that we've all been wanting and dreaming about solving for so long.

M

Militia6:35:03

You made the right cue to the Siemens side. So now let's hear actually what are you talking about? What I would say is what I found really interesting and really fun, frankly, is how complementary our solutions are. And that's probably made it also very easy. Sure, we've had digital twin and simulation solutions. Now we can bring them to scale with the power of NVIDIA and Omniverse, and now anyone in the world with a web browser can experience them because we're on AWS compute. So it's absolutely a natural fit, and having our three technology stacks coming together in this way is fascinating.

H

Host6:35:40

Now here's a really intriguing question. A final thought from each of you, just to know if someone is intrigued but not really sure where to start. What do you tell them? I'll start with you, Gom.

Y

Yugam6:35:54

Well, I would say, you know, some of these topics may sound a bit futuristic, maybe hard to reach or complex. I would say you're probably closer than you think. If you're already using some of our tools, you know, process simulate, plant simulation, or things like this, you're actually a long way along that journey. And you can start simple with a simple use case and build on that.

H

Host6:36:17

Wow. What do you say, Mike?

M

Mike6:36:19

I, you know, to the question of where do you start? I think it's like anything else, right? You look to the leaders in the industry. You look to guys like Rob Smith at Keon standing right there that are really leading the transformation in supply chain. You look to leaders like AWS. You look to leaders like Siemens. And as well NVIDIA, with the Omniverse libraries and the three computers to really automate robotics and bring AI. But also you've got to start testing, you've got to start experimenting. We've experienced in the last few weeks a step change in computing and software development with these long-running autonomous agents, Nemoclaw, OpenClaw. It's going to fundamentally change our world. I think six months from now, we're going to have seen more change than we have in the last few years. And I just really encourage everybody to take this seriously, to harness the opportunity, and then go talk to Ashley and the team at the Siemens booth about digital twin composer.

H

Host6:37:12

Yeah. So I know when we were preparing for all of this, we were talking about a little bit of the technological thread. I do look at the time. So maybe we want to dig a little bit, but please, I know you're architects, engineers, and you name it. Let's not get too technical, but tell us a little bit more because looking ahead, what are one or two developments that will make the biggest difference in the next step?

M

Militia6:37:35

Yeah. Yeah. Thanks for the question because there are really three areas I would say where we're absolutely accelerating. Number one is of course AI. Now that you have a digital twin that has brought together all this great data, what do you make of it? And now we can unleash teams of AI agents that will help you converse with the digital twin, will help you optimize, take these optimum decisions constantly. Number two, absolutely critical for us is the connection with the physical world. You see in this booth a lot of physical assets, sensors of machines, and this is very important to us and it's part of Siemens' DNA. How do we connect these assets to the physical world to make sure we keep optimizing while the factories are in operation? And the third one I would say is with respect to autonomous factories and autonomous production. We see a lot of our customers now moving forward with humanoids with autonomous robots, and to train these types of robots today, you need the high fidelity environments that you saw in these videos. You need that level of fidelity, detail, and precision so that you can safely introduce these humanoids in the workplace.

H

Host6:38:40

I'd like to hear from you, Amad, what you think about this.

A

Amad6:38:43

Yeah, we're looking at AI in different kinds of solutions. First, AI can be used to accelerate simulation, which is important when you compose this kind of digital twin solutions. Another thing is AI could be used to reach the USD files, the Universal Scene Description files from NVIDIA. And in addition to that, we're looking into the future kind of agentic AI, like a proactive kind of agent that runs in the background that can actually discover some predictive problems before it happens. So those kinds of things are important to look into the future of agentic AI, not just as a chatbot or a conversation with natural language, but we're looking further to have those long-running proactive agents.

H

Host6:39:39

Cool. Mike's so excited about that. Mike, you got to sum it up for us. Come on.

M

Mike6:39:44

All right, Militia. I think to sum it up, the future is here. It's arrived with AWS and Siemens through digital twin composer and everybody can start to take advantage of this. And what it's going to look like is those of you who have ridden in a self-driving car for the first time, it's kind of a startling experience, but then two or three minutes in, you're cruising along and not even thinking about it. Very soon we're going to have factories that we can talk to that are self-driving, that are autonomous, that keep workers safe, that are more efficient, that can optimize supply chains both inside the factory wall and throughout the entire supply chain. So excited to be here. I think next year at Hannover Messe is going to be very different and very exciting.

H

Host6:40:24

Yep. So how excited are you about this whole thing? I mean we have so many opportunities, so many sessions that are streamed live as well. If you have the opportunity to also hear within the audience to run by, I think you're going to find us. Let's just give a shout out to your booth. We're out here. So, please don't fear. We are here for you. If you want to ask any technical deep dive questions, these three gentlemen offer themselves to answer those questions. Am I making fake promises? I sure hope not.

M

Militia6:40:56

We're generally pretty approachable.

H

Host6:40:58

Yes, that sounds like a plan. So maybe if we can just sum down the momentum and look at what we have here today. What is really the thing that you say wow, that really blew my mind away from this whole project?

M

Militia6:41:19

It's the energy at Hannover. It's always amazes me the amount of energy that is all in one place.

Y

Yugam6:41:29

For me, it's my absolute first time and I'm not trying to stag you here, but it's my absolute first time. And I was first challenged that there's so many cool AI solutions out there that most of us don't know of. But the fact that there's just too much data out there, it strikes me really. I think the journey has just begun and I'm really looking forward to what has to come. What are you looking for, Emard?

A

Amad6:41:55

Well, I like the new layout, the colors. Yeah, really nice, better than the last years. I see here all about innovation, about industrial AI, and we're looking forward to innovate with Siemens and Nvidia.

H

Host6:42:12

And as my colleague has been mentioning, before you go, we don't want to leave you empty-handed. We have two little presents for you showing our partnership and happiness for this great partnership. So there we go. One for you, Em. Thank you from our wonderful Brazilian partners. Some cosmetics I heard. Thank you. And if you don't like them, I'm just joking. But thank you so much. It was great to have you. Thank you. Thank you, Mike. Lovely to have you, Gome. See you later.

Thank you. Wow, that was an amazing interesting session for me because I just learned a lot. You know, we have that data. It's stuck in silos and it's up to us how we take the future and how we drive it. Thank you, dear LinkedIn live stream audience. Thank you so much for joining us. I hope you enjoyed the session and I'll be handing it over soon to my colleague. But do enjoy the rest of this fair because we have so many other cool topics that will be streamed live as well as on the industry channel. So don't forget to watch that. I'll see you in a few minutes.

Thanks, Melissa. There is a come and go here on our stage. This is the sofa of wisdom as we call it. And that's the living room of innovation. Innovation. This is what keeps us driving at Siemens. And a big shout out to all those colleagues who are working at the stations. They have been talking since 9:00 in the morning. This booth is packed. So a big thanks, virtual applause to all of you at the kiosks who have to bear with me yelling around. Well, ladies and gentlemen, great you're joining us. Take a seat if you want to take a chance. There are very few places left here. The next session is focusing on the topic of circularity. Now when you think of circularity, would you immediately think of an automotive company? Yes, they have big challenges in there. Driving the circular future, this is what we will be focusing on. And how Siemens and Volkswagen are scaling sustainable automotive transformation. Circularity definitely has become a defining transformation in automotive manufacturing as it is reshaping how the industry designs, produces, uses, and now reuses materials. Emissions can be reduced, resources conserved, and more resilient value chains built. Let's hear more now from our two experts in that field. Please welcome the head of sustainability strategy at Volkswagen Group, Guido Icon and Erin Dvola.

Wonderful. Hi Erin, great to have you. Welcome to our living room. Now let's take a seat. We're going to take the middle here as well. All the colorful ladies.

E

Erin Dvola6:45:35

Yeah, we tried to be as colorful as we can to give you the right framing.

H

Host6:45:40

Good. Did you have you been around at the fair booth already?

G

Guido Icon6:45:43

Yes, I did already. There was some walk around here and then visit some stands and had nice discussions and experienced what really is going on especially when we talk about circularity.

H

Host6:45:56

I mean, you're in good companionship with Erin, our head of sustainability at Siemens Digital Industries. She was on stage already this morning. So she kicked the program off here.

E

Erin Dvola6:46:09

We did. I gave Guido all my tips.

H

Host6:46:11

Okay. Wonderful. So let's deep dive into the topic of circularity. Now both Volkswagen and Siemens are investing in circularity. The importance of scalability was mentioned quite often in previous talks. So it seems besides AI, now scalability becomes really big. Why is now the moment to scale and go big?

G

Guido Icon6:46:40

From our perspective, we need to think circular economy much bigger than just getting materials back and recycling them and bringing back into the cars. We really need to talk about how we design cars, develop cars, produce cars, use them, and what we do during the use phase and afterwards. It's very important that we touch all our strategies in terms of circular economy, that we have an eye on the entire value chain. We have a circular economy strategy 'regenerate plus' with four dimensions: nature, society, people, and business. Circular economy is a very good example to touch all these streams and change. It's important now because the industry is forming new connections, companies are interacting differently. We work closer with recyclers than before. This is happening now, so if you pass the time, it's gone.

H

Host6:48:03

Okay. Fair point. Now Erin from Siemens perspective, is there that much to recycle and reuse as well?

E

Erin Dvola6:48:15

This morning Martin and I talked a little bit about disruption. That's really one of the big drivers for circularity. We see material scarcity, geopolitical tensions driving the need for more predictability in your material, which is one thing circularity can help with. The other thing that makes now the time for circularity is that the technology needed exists today. It can solve the problems that all automotive manufacturers are having with these really complex problems that are difficult to solve on your own. The future is partnership, an ecosystem driving forward, and circularity is where you see that the most because it's such a complex layered problem to solve.

H

Host6:49:08

When you say it, it already sounds complicated. But what does it mean in practice, guys?

G

Guido Icon6:49:15

It is complicated as well. Sorry to say that. It's not an easy story. What does it mean for us? We face a lot of challenges in terms of regulation, compliance regulations for fleet compliance, exhaust emissions, and circularity. There is a lot pushing us, making things complex. In practice, we have established near our factory in Warsok an open hybrid lab where we combine industry and R&D. We have strategic partnerships, like with Porsche and a battery recycler startup. On the operational side, we opened a facility in Sika in Germany where we do circular economy, dismantle cars, sell used parts. We touch the entire value stream from innovation drivers to getting it done.

H

Host6:51:11

It's quite surprising that you took that business opportunity back because it was seen by others but now there is much more value in those cars which we definitely see and want to use them wisely. Now coming to you Erin, what kind of technologies actually can help make circularity a success, profitable, and scalable?

E

Erin Dvola6:51:40

You know, here at Siemens, we talk a lot about bringing the real and digital world together. For a complex topic like circularity, where you have to think from this life to the product's next life, it's never more important to bring those two together end to end. You can make the best decisions at the design phase. We want to start with getting the best digital twin of that product, pulling requirements from later on into the design, thinking along the entire digital thread, and using AI tools to be more efficient. It's about bringing that digital enterprise to life and shifting left to make sure it's ready for the next life 15, 20, or 30 years from now.

H

Host6:52:37

Okay. Now I know you developed an impressive proof of concept. Can you tell us more about it?

E

Erin Dvola6:52:46

One of the challenges is that we need to design from the beginning with circularity in mind, but we weren't doing that 15 years ago. So now we apply technology like you see behind me. We use a vision system to understand what vehicle is coming into the disassembly cell. The second step is to understand what to do with that piece. We're working in a two-robot cell, and we need to plan for them not to interfere. We use virtual tools to plan the disassembly in the virtual world. The third step is executing in the real world. We have a test cell where we do that. This shows how AI can solve a problem we couldn't solve at the beginning because we didn't have those parameters then.

H

Host6:53:59

And how long did that take, that phrase of changing it from we didn't have it to having it?

E

Erin Dvola6:54:05

The team has been working on this particular solution for about a year, year and a half. But it's taking that advanced technology and finding the right partners. This is just one proof of concept; these ideas apply more broadly to disassembly.

H

Host6:54:25

So this is like the blueprint now for many other things to come. Okay. So things to come, Guido, when we talk about circularity, what would you say the future of automotive industry will look like in 5 to 10 years?

G

Guido Icon6:54:53

That's a good question. We have a much longer horizon. It takes 3 to 4 years to develop a car, and then it's on the road for over 20 years in Europe. So we need to think in long horizons. The challenge is e-mobility and getting cars back for recycling to make new electric cars. In 5 to 10 years, if we pass the switch point in the right way, we can scale this business. It needs to be scalable to be efficient. I think if we pass that point, we start scaling in the early 2030s.

H

Host6:56:11

Okay. Challenges and opportunities. Erin, from Siemens perspective, what do we see in 5 to 10 years?

E

Erin Dvola6:56:24

I've been working in sustainability for about four years and I look for inspiration from nature. I think about water, how it remains the same but changes state and location in an infinite cycle. That's the future. With the technology we have, 10 years isn't far away. We can quickly move toward having resources in an infinitely perfect cycle of use and reuse.

H

Host6:57:02

So there is big potential, definitely. Circularity is just the start. It's accelerating, turning opportunities into bigger chances. Scalability is something to go for. All right. Thank you for the insights. We have a gift from Brazil. Thank you both.

We remain on sustainability. Next we have four ambassadors of sustainability from different departments at Siemens. They will talk about how to maximize value and minimize waste, focusing on the Ecotech label. I am not the expert, so let's give them a big round of applause. Please welcome Cedric Banhogen, Johan Kick, Marcus G, and Christopher Varter.

Isn't that cool? These gentlemen, like my dream team of Siemens. You're all smiling. Have you had tough booth duty? Have you been talking to customers?

C

Cedric6:59:36

Well, I would say it's always a joy to talk to customers, figuring out what their needs are and then coming up with a sustainable solution.

H

Host6:59:44

Great answer. Before we deep dive, please introduce yourselves.

C

Cedric6:59:53

I'm Cedric. I'm a sustainability manager for motion control. We set things in motion and I'm passionate about sustainability.

J

Johan7:00:04

I'm Johan Kick. I work in factory automation, automation, and I'm here for the standardization team and the sustainability team in FA.

M

Marcus7:00:19

My name is Marcus G. I'm the sustainability manager of process automation and I have a side job as quality manager. Today I'm happy to show some nice features.

C

Christopher7:00:34

I'm Christopher Vatter. I'm with Siemens Smart Infrastructure Electrical Products and I'm a product manager for sustainable products.

H

Host7:00:43

Wonderful. Now that we know who's who, let's start the deep dives. Cedric, can you introduce our circularity framework with Siemens and the challenges around waste and circular economy?

C

Cedric7:01:27

It's a perfect segue. We need to ensure our scarce resources find a pathway for sustainable growth. We want to grow, but resources are limited. We need to keep resources in a loop as long as possible. With climate change, we must use resources efficiently and not extract new ones. Supply chain volatility is also a factor. We are driving circular transformation to use resources as long as possible. There are 9.2 billion tons of industrial waste annually, but $4.5 trillion in economic value is possible by 2030 if we utilize those resources. So doing more with less for customers, society, and the planet.

H

Host7:03:36

Sounds like you've been involved heavily. But there is more to it. Christopher, regarding circularity, what does it mean for Siemens?

C

Christopher7:03:53

Circularity is important for our customers, so it's important for us. Our customers have sustainability goals and want to fulfill them through circular products and business opportunities. We help them achieve that.

H

Host7:04:25

Cedric pointed out the challenges. Johan, how does the Siemens Ecotech label link these challenges to the circularity framework?

J

Johan7:04:58

I'd like to start with the Ecotech framework, particularly our program called Robust Eco Design. The label highlights products with good sustainability or circular performance. To get there, you improve them through ecodesign workshops. Product development teams, with material design experts and sustainability experts, use life cycle assessments to identify hotspots. Based on 13 ecodesign criteria, they improve aspects like less waste, less material use, recyclability, and minimum material use.

H

Host7:06:11

When the homework is done, the Ecotech label shows customers sustainability and circularity topics.

M

Marcus7:06:21

The label gives transparency for informed decisions. Criteria include compliance with substance norms (RoHS, REACH), production in factories running 100% on renewable electricity, an environmental product declaration, and fulfilling each of three criteria: outperform a relevant standard or be better than the predecessor in optimal material use, optimal use, and circularity.

H

Host7:07:40

These criteria are the same as targeted in workshops. Now Christopher, you have a highlight in circularity. What is that?

C

Christopher7:08:29

This is a soft starter, a refurbished product. We took back products, did a refurbishment process, and now customers can order this refurbished product. It has 50% CO2 reduction compared to a virgin product.

H

Host7:09:14

Okay. Shall we move on? Marcus, what have you brought along?

M

Marcus7:09:20

I brought two smaller power supplies. They are often overlooked but essential. Here on the left is the new generation, on the right is the lighter one. This one is about 20% bigger in volume and 23% lighter, showing material reduction. The idle time energy consumption was reduced by 80%. Over the years, 24/7, that has impact. Also, I brought a circularity example: if this product returns during the warranty period of 60 months, it will be checked, repaired if possible, and resold as a spare part with a 'Siemens tested' label.

H

Host7:10:13

Our technology saves energy and costs, and also saves the environment by using less material and reusing. Cedric, you also have two devices?

C

Cedric7:11:37

These are main parts of a servo motor – two rotors. They look identical but are different in carbon footprint. This rotor uses recycled magnets from our partner Hyperac, while this one uses virgin materials. The recycled magnets have up to 90% less carbon footprint.

H

Host7:13:23

Johan, you have an example for factory automation?

J

Johan7:13:35

This is a module from our ET200 MP series. The highlight is the secondary material aspect of the plastic. Two years ago, this product was one of the first to get the Ecotech label. Now, 50% of the housing material is from biocyclic feedstock from waste or residue materials from agriculture and forestry. This reduces the CO2 footprint by 50%.

H

Host7:14:56

These examples show how to keep stuff in a cycle: remanufacturing, reducing size, using old materials. Keep things in the cycle for sustainability.

Any other examples?

C

Christopher7:16:06

The most important thing is to show that these products work together in real applications. We demonstrate that at our sustainable systems hub at booth 180. Please visit.

H

Host7:16:42

We invite you to join us.

M

Moderator8:04:22

Must work hand in hand. Innovative projects like this secure Europe's energy demand, and Lionheart is one of those projects. Chris, let me start with you. Every innovation begins at a vision. What is Vulcan Energy's vision and mission?

C

Chris8:04:49

We were fortunate in 2018 to have the vision of two unique geologists who saw we were electrifying mobility, but if we continued producing lithium with high carbon and water use, what was the benefit? They thought how to create a sustainable product. They found locations globally with sufficient energy in the brine to drive the process, including the Upper Valley of Germany, which already produced geothermal energy and had lithium. The idea was to combine proven processes: geothermal, direct lithium extraction, and battery-grade material production via chloro-alkali. The innovation was combining these three steps to create a competitive, sustainable supply chain. We moved quickly to secure capital, take licenses, and build the project. It was hard work convincing Germans and Europe.

M

Moderator8:06:54

Especially when we talk about drilling, we are not that fond of that topic.

C

Chris8:06:57

And the fact that raw materials weren't being produced in Europe in 2018. To build a low-cost supply chain with stiff competition from China, we had to build credibility with partners. We understood the risks from lenders, EIB, and equity partners, and spent years demonstrating we could derisk the technology, permits, and financing before closing the deal in December. That is the vision: to produce a low-cost, highly competitive lithium supply chain in Germany. We are building it now with our partners on stage.

M

Moderator8:07:48

Wonderful. Convincing for the EIB to support this project because of sustainability and energy topics. Nicola, can you give details? Why is this essential for the EIB?

N

Nicola8:08:07

This project ticks many boxes for us. The EIB is an enabling innovation bank and the climate bank. We want to foster sovereignty in Europe, prove we can do green mining, and build a structure that is sustainable and profitable. This is a showcase that Europe can do green mining and export the technology to like-minded countries, making us sovereign both inside and outside.

M

Moderator8:09:54

Thanks Nicola. Veronica, regarding financing, how is Siemens Financial Services helping make Lionheart a reality?

V

Veronica8:10:15

We created an environment for Chris and his team to drive pioneering activities. Even in Germany, which has an established framework, this technology is first of its kind. We structured the project from early stage to attract equity investors, then debt financing. We integrated different Siemens businesses like DI and Smart Infrastructure. Our approach was to act as an integrator, ensure viability, and lay the foundation for scalability. The intent of the 'Made for Germany' initiative is to establish sustainable projects that create confidence and followers, and we will be a trusted partner for the long journey.

M

Moderator8:13:30

Wonderfully said. Axel, how did Siemens support the execution and integration of technology?

A

Axel8:13:48

First, I thank everyone in the panel. We are proud to be part of this project. Thanks to Siemens Financial Services for helping finance it. We will digitalize from the bottom: sensors, automation, communication, cybersecurity, fire alarms, building automation. We will have one project management team but work in parallel on different parts. We will create digital twins step by step to optimize through the lifecycle. We are partners for the lifetime of the project, not just installation. We want to learn and are proud to be part of this esteemed project in Germany for Europe.

M

Moderator8:15:21

Now Chris, choosing Siemens as both financial and technology provider: was that a walk in the park or did you face challenges?

C

Chris8:15:38

We set a strategy to work with Siemens. The idea of equity and project execution creates aligned interest and skin in the game, driving the right behaviors. We want win-wins. Whether through Veronica or Axel's organizations, we don't want to let them down. This aligned interest model with skin in the game is what we drive. We also did it with another German partner. A company of our size uses trusted partners. That trust will drive this. We look forward to working with Siemens on Lionheart and beyond, demonstrating this first-of-its-kind project.

M

Moderator8:16:51

Wonderful. We have seven minutes left. Everyone: what is the biggest reason this partnership will work? We have a setting where each brings the best to the table. Projects like this are complex and long-running. You need broad shoulders to stay when wind comes from the front, align on the pathway, overcome obstacles. We have patience, urgency, knowledge on the ground with different technical and financing partners, and policy framework help, including grants from German government and local community backing. Without that support, it wouldn't happen.

N

Nicola8:18:08

When I joined the bank two and a half years ago, the first thing I heard was 'no drilling, no mining, risk not mitigable.' Now we have two mines in Europe, this is the first 100% green mine globally. You can do it if you have leadership and courage on political, financing, and technical sides. I applaud Siemens for engaging to secure critical raw materials. Without sovereignty on critical raw materials, digitalization, green transition, and security defense cannot happen.

M

Moderator8:18:55

Thank you, Nicola. Veronica, what's the biggest reason this partnership works, also for future collaborations?

V

Veronica8:19:13

It's about creating resilient long-term supply chains for Germany and Europe. We saw setbacks in battery production. We created an environment for sustainable investments where confidence in viability increases. Chris asked what we expect from him. I want to see a first-of-its-kind approach that thinks about sustainable supply chains, scalable, creating confidence with a can-do attitude, attracting public and private capital. The 'Made for Germany' initiative is about attracting investments in Germany for sustainable, innovative activities to create a scalable environment on a long-term basis.

M

Moderator8:20:56

It's really trust, collaboration, and viability of new business models. Axel, new business models? Any other plans besides getting lithium out of the ground?

A

Axel8:21:14

If we execute this project in time and quality, I would be very happy. That's a challenge we need to do together. We have a good partnership. Beyond that, let's create transparency and efficiency. Help Vulcan get the data, use new technologies, become more efficient and resilient in production against any obstacles.

M

Moderator8:21:56

Great. I leave the last word for you Chris. Why will this be a success and what's the biggest reason this partnership is successful?

C

Chris8:22:08

We are confident as a company, young but experienced, humble. We look for complimentary skills. That's a sign of a mature organization knowing we can't do it alone. We chose the right partners complimentary to Vulcan. We will never have the mindset of a new-age digitalized company like Siemens; that's what they bring. We understand what's underground and how to produce and sell. We chose tier-one partners with experience in the same jurisdiction for 100 years. We are confident with the support of EIB and Siemens. That's why we'll be successful. I cannot wait to come back in three years with my first bottle of lithium hydroxide.

M

Moderator8:23:17

I take the challenge. Now it needs an Australian to come to Europe and look at the map for resources. Have you checked for other spots?

C

Chris8:23:37

We have, but I'm not going to reveal the entire hand. There's a lot of resource in the Upper Rhine Valley. We'll be there for decades. Our focus is the Upper Rhine Valley for many years.

M

Moderator8:24:05

Wonderful. Our backyard here is our booth. You'll find Siemens Financial Services colleagues to help with your projects. Thanks for sharing your expertise. Best of success. Let's have a round of applause.

We have presents here. Don't walk away, Chris and Nicola. That's from our customer in Brazil, Natura. They produce cosmetics with Siemens technology.

C

Chris8:25:22

That's a nice thing to remember from Hanover. Australia has never been partner country here. We should vote for that.

M

Moderator8:25:32

Yeah, let me see. Speak to somebody.

Okay. Definitely good people coming from there with good ideas and visions. Thank you so much. All the best. Bye-bye.

With this, we conclude our stage program. There will be a press conference later. Handing over to Izzy, our roving reporter at the fair booth.

I

Izzy8:26:03

Hello and welcome back. We're at the CPG station talking to Pringles. I'm with Ronnie Matz, plant director, and Andrew Manning, global account manager for Pringles. Andrew, what is the story here?

A

Andrew Manning8:26:31

We're talking about a three-year partnership, a journey of discovery understanding raw materials and processes, a data-driven approach solving productivity puzzles.

I

Izzy8:26:47

Ronnie, does it stay local or go beyond?

R

Ronnie Matz8:26:55

We are the pilot plant. Together with Siemens, we created a blueprint that can be used beyond the factory walls in other factories.

I

Izzy8:27:06

What's special about the Pringles and Siemens relationship?

A

Andrew Manning8:27:16

We have a process journey as partners. Over three years, we felt like one company. That's been an amazing strength.

I

Izzy8:27:33

Andrew, anything to add?

A

Andrew Manning8:27:35

Common purpose. Ronnie said from day one, you never felt someone was trying to sell something. It's problem first, solution second.

I

Izzy8:27:50

Great. Everyone loves Pringles. Thanks for being here.

R

Ronnie Matz8:27:57

Thank you.

I

Izzy8:27:58

We're passing over to Nura now. Mickey.

M

Miki8:28:05

Thank you Izzy. I'm here with Bourj who will tell us about what we can see in virtual reality. Bourj?

B

Bourj8:28:24

This project is a partnership with Natura, a major cosmetics company in Brazil. We support them to achieve better process control in an Amazon community. The process was manual, difficult to get traceability and viability. Here you can experience the Amazon forest and the extraction process, including smells.

M

Miki8:29:33

Can you dive deeper on what Siemens and Natura are doing?

B

Bourj8:30:04

We help Natura get precise control on processes. Their supply chain relies on small communities far in the forest. They need remote control and traceability. From 100 kilos of leaves we produce only 20 grams of oil.

M

Miki8:30:18

What's your first impression on the first day of the fair?

B

Bourj8:30:28

Amazing. Everyone needs to come to Hall 27 at Siemens and learn about our solutions with AI.

M

Miki8:30:45

Thank you. Now we head to the CPG area, PepsiCo.

I

Izzy8:30:59

Welcome to PepsiCo. I'm with Tyler Nukem, global industry developer. Tyler, high-level overview of what we're showcasing?

T

Tyler Nukem8:31:14

We're showcasing how to leverage a digital twin. For PepsiCo, we optimized a compounded issue across three facilities in northern Texas: warehousing and production throughput to meet local demand.

I

Izzy8:31:41

What excites you most about this partnership?

T

Tyler Nukem8:31:46

It's a true partnership, pushing and pulling each other, learning, challenging. We launched the digital twin composer at CES. We're seeing good partnership and working together really well.

I

Izzy8:32:21

Yes, we care. It's for the end consumers.

Any additional comments for other customers?

T

Tyler Nukem8:32:34

The industrial metaverse is excellent. Don't limit your thinking. We're talking about democratizing simulation models, for operator training, maintenance, etc.

I

Izzy8:33:03

Interesting. We have deep dive sessions tomorrow. Now passing over to Max and Militia.

M

Max8:33:21

This is the final interview. We're at the pop-up factory. Militia, do you know what this is?

M

Militia8:33:42

This is the factory of the future.

M

Max8:33:45

This is a pop-up factory, an industrial metaverse system that can be set up close to demand, like near a World Cup stadium. You customize, scale, and pack it up. Have you looked around?

M

Militia8:34:29

Not yet. My highlight today is the Pringles stand because my son loves Pringles. The main highlight was factory automation, seeing how digital twin and AI are moving on the shop floor. Everyone talked about letting data do the work, and we saw that.

M

Max8:35:28

Everything here is a taste of the entire fair. Tomorrow will be fun with many customers.

M

Militia8:35:32

We have partners and customers: PepsiCo, Tata Electronics, Natura, Aramco, Audi, and many more.

M

Max8:36:03

Have you seen the Audi outside?

M

Militia8:36:06

Not yet, but if I get a chance to drive...

M

Max8:36:16

It's worth a look. What partners are coming tomorrow?

M

Militia8:36:19

I heard about Spira, TCS, AIAN, AIA Brazil. Also a session with Pablo Favo, CEO of Brazil, signing an MOU with Jud Visa. Rhino Ram talking to Nvidia. Capgemini, Accenture, AWS, Deloitte. It's never-ending.

M

Max8:36:56

Our program is jam-packed. We have the main stage, Izzy and Mikael running around. Are you looking forward?

M

Militia8:37:24

Yes, I'm wearing comfortable shoes.

M

Max8:37:34

What's it like being here for the first time? Madness or structure?

M

Militia8:37:48

Standing here you feel the power, energy, excitement, curiosity. I'm learning every minute. This is magic.

M

Max8:38:19

It's packed. Should we take Yonatan on a short tour?

M

Militia8:38:44

Yes, let's go to the CPG area.

M

Max8:38:57

Any exhibits you've seen that are highlight?

M

Militia8:39:05

Anything that moves. Electrification and building optimization.

M

Max8:39:18

At SPS my favorite was the motorbike.

M

Militia8:39:23

The motorbike is in the electrification area, a predictive maintenance journey with VR goggles. You should try it.

M

Max8:39:39

Here's my highlight: the Pringles booth.

M

Militia8:39:42

Pringles. How are you?

M

Max8:39:45

Ronnie, how was the day?

R

Ronnie Matz8:39:52

The first day has been awesome. We shared our partnership experience. Can't wait for the next three days.

M

Max8:40:04

You sound excited. We're looking forward.

R

Ronnie Matz8:40:11

Thank you.

M

Max8:40:12

What are you looking forward to?

M

Militia8:40:15

The motorbike. Let's go there.

M

Max8:40:20

We could take a quick tour.

M

Militia8:40:26

All right.

M

Max8:40:27

Here we showcase a digital enterprise showcase along the CPG industry. We show how digital twin, software-defined systems, and industrial AI help companies go from product design to shelf. It starts with data, making it contextual and actionable.

M

Militia8:41:25

I'm shocked that Max knows everything. He can speak for hours.

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Max8:41:39

This is your second or third Hanover fair. What's the big difference in topics?

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Militia8:41:54

It's all AI. We're almost ready for the press conference. Handing over to highlights video. See you tomorrow.

See you.

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Narrator8:42:17

Welcome to Hanover Messe 2026.

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Patrick8:43:10

Good evening, Hanover. Welcome to this press conference. I'm Patrick, leading media relations at Siemens Digital Industries. We want to show how we bring AI from demo into the real world. Please welcome Cedrik Neike, CEO of Siemens Digital Industries.

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Cedrik Neike8:44:26

Thanks, Patrick. Welcome to the Hanover Fair. We are in the AI hall this year. The key story: when a general purpose technology like AI goes into industry, it becomes industrial purpose technology and adoption explodes. At Siemens, we have a 178-year history of making technologies effective. We connect AI to the physical world in four steps. First, integrate AI into as many products as possible. We have 150 AI-powered products. Second, innovate and push boundaries. Third, find best partners to change industries. Fourth, power AI factories. An example: we launched an IPC with GPU for inference on the edge, like Audi using it for welding spot detection. We also launched an AI tool chain for chip design called Fuse EDA AI agent, used by Nvidia and Samsung. For the second pillar, we use our own factories as customer zero. Our new factory in Alagan, designed with Nvidia, will be the most advanced. Rainer will explain more.

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Rhina Bra8:54:02

Thank you, Cedric. We are taking the next step in our factories, headed by the Alagan factory. We want to become the first AI-powered autonomous production. We are automating what has not been possible before.

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Ryan8:54:27

That means we automate the unknown. We automate tasks which have not been pre-thought when the automation system was put into place. How do you do that? You need AI but a special kind of AI which is connecting the brain of Cedric with the real physical world and that's called physical AI. And what exactly are we doing there in Alagen? We are already very automated. We were announced as a digital lighthouse of the World Economic Forum just one and a half years ago. But still there are manual tasks. One of the manual tasks is putting the accessory pack into the box at the end of manufacturing of our most sold inverters. It's a very hard task to automate because the box is flexible and unpredictable. Now using physical AI, we can tell the automation system what to do. We trained physical AI models with this task. It reliably takes the pack and places it precisely, correcting if something is out. This task has never been automated before with a good return on investment. This enables flexible automation and helps customers who lack skilled workers. Why is Siemens uniquely positioned? We have the industrial PC for inference, the software, the compute power, the data, and a reliable stack. We work with Nvidia for synthetic data. We are convinced Siemens is uniquely positioned to make physical AI real and automate the automation. Back to you, Cedric.

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Cedrik Neike9:00:24

Thank you, Ryan. Thank you, Rhina. Absolutely big thanks to Rhina. Very cool. So one thing is to use it on ourselves. A year or two ago, we were one of the first to launch the co-pilot with Microsoft. In the last two and a half years, we brought a luminary on board, Vazi, who has hired a big team to push AI further. Give a huge round of applause for our AI luminary Vazi, who will give us an overview.

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Vazi9:01:27

Thank you for having me, Cedric. I'm very excited to be here at this defining moment where Siemens is uniquely positioned to lead AI moving from the digital world to the physical world. We are launching the IEN engineering agent, a new class of AI that carries out engineering tasks end to end. This marks a fundamental shift from AI that just generates suggestions to AI that actually completes work.

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Cedrik Neike9:02:00

This is a new class of AI products. Vazi, please explain how it works and why it's so different.

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Vazi9:02:11

Unlike generic AI tools, the IEN engineering agent operates inside real engineering systems with full awareness of each project's context and constraints. It connects to the TI portal and enables outputs tailored to each project. We've piloted this with over 100 customers across 19 countries and it's now generally available. It can execute tasks like PLC coding, HMI visualization, device configuration while meeting industrial standards. It breaks down complex tasks, evaluates its own performance, and corrects mistakes. Let me give you a quick demo. We'll build control logic for a body and white welding station. We tell the agent to build the logic taking the full lifecycle into account. It breaks it down, pulls context, references documentation, and creates the full SCL function block, hundreds of lines, validated. Then we add quality checks to monitor weld current. It updates the code. Then we rename a switch across all files with a single command. Finally, it writes a summary and change log. Everything is documented.

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Cedrik Neike9:06:48

So this sounds great. Tell me tangibly what it means. I'm a customer—why is it special? What can I expect?

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Vazi9:07:01

Here are some numbers, Cedric. Automation engineers can execute their work two to three times faster, with up to 80% higher solution quality. With the IEN engineering agent, we deliver automation logic that meets each customer's standards, allowing engineers to take on more complex projects and complete them faster. Two to five times productivity gain is significant.

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Cedrik Neike9:07:43

Look at the CAMT demo—a Chinese manufacturer building new energy vehicle solutions. They built an electrical braking assembly and testing line in two months from scratch using the IEN agent. It has a disco ball that reflects light differently, and the agent still works in varied light environments. I recommend you check it out at the booth. Thank you, Vazi. But we should also ask: what's next?

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Vazi9:09:36

Two things. First, on the IEN agent itself, we'll broaden its scope to cover more of the lifecycle and remove the undifferentiated heavy lifting from automation engineers so they can focus on interesting work. Second, beyond the agent, we're working on making foundation models physics-aware. Most models today predict what's likely without respecting what's physically possible. You'll see something from us on that soon.

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Cedrik Neike9:10:36

Very cool, Vazi. We'll keep you for Q&A. So I told you: we integrate and launch as many AI products as possible, use them in our own factories, push boundaries. But we need partners. We have Accenture, Cap Gemini, PepsiCo. One area I'm particularly excited about is supply chains. Please welcome Rob Smith, CEO of Keon. Rob, great to have you.

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Rob Smith9:11:39

Great to be here, Cedric. Thank you.

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Cedrik Neike9:11:43

So, Siemens and Keon—how can you help customers make their supply chains more resilient, adaptive, and efficient? What's the big opportunity?

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Rob Smith9:11:58

Let me start by saying how much I appreciate announcing our strategic partnership. We're bringing the digital and physical world together at scale into the world's supply chains. Supply chains are more complex and vulnerable than ever. To future-proof them, you need resilience, flexibility, optionality, and agility. That requires digital twins and physical AI. We announced with Nvidia the digital twin composer, and our first customer was PepsiCo, second in Europe is Keon. Let me show you a fly-through of a digital twin of a high bay warehouse. You can see how racking transforms the warehouse, bring in large-scale automation, simulate, emulate, and prove the solution in the twin before building it. It compresses the solutioning phase from years to months.

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Cedrik Neike9:14:41

The amazing thing is you go to a brownfield, see the optimization, simulate, build, program, and run it in parallel. How quickly can we see this in action?

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Rob Smith9:15:00

It's there right now. We send an engineer with a lidar and camera to get millimeter-exact dimensions of a facility, use the digital composer to put in mechatronics and software, and test everything in the twin before physical reality. We're doing it now with GXO, a top 3PL. We have an autonomous truck running in their facility, making decisions, aware of surroundings, safely operating. The CEO of GXO came with me to Nvidia's GTC and talked about how it's transforming his operation. We'll have 100 trucks operating later this year, and he has 1,200 facilities to go.

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Cedrik Neike9:16:26

Thank you, Rob. Please stay for Q&A. We launched the digital twin composer at CS and already have over 350 customer engagements. So, first I define as many AI products as possible (over 150). Second, I push the boundaries. Third, I take the best partners. Fourth, I power the AI revolution. We announced a semiconductor circuit breaker that switches 1,000 times faster, uses 50% less copper, and can save up to 26 million euros per year for a gigawatt AI factory. That's our strategy: bring AI into industry and power AI factories. Back to Patrick.

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Patrick9:19:19

Thanks. Now the Q&A session. If you have a question, raise your hand or use the chat. Please introduce yourself. Let's start with the gentleman in the middle.

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Alex9:20:11

Thank you. My name is Alex from ARC Advisory Group. My question is about agent engineering agents. Do you think that with that tool a person with deep knowledge in engineering can handle complex engineering tasks?

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Vazi9:20:33

Yes. I'm a software engineer myself. AI is showing value in coding. Having an agent that handles the work you don't enjoy is huge. It allows you to focus on the domain problem.

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Patrick9:21:24

Thank you. There's a gentleman here in the front.

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Orhan Arinburg9:21:33

Can you hear me? Orhan Arinburg from Europe. Not about AI, but the semiconductor circuit breaker. How fast does it respond to short circuits compared to conventional systems?

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Cedrik Neike9:21:56

1,000 times faster. In microseconds. It also has intelligence to sense and can save energy because it can switch off LEDs when dark. And it doesn't have an arc.

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Patrick9:22:36

So I guess the task to the Siemens engineer was can you do it 100 times fast? And they did it a thousand times. They overdelivered. Next question to the left.

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Chowri9:22:49

Chowri, analyst at Arkansasite Partners based in Silicon Valley. Thank you for sharing the Alangan factory case. It seemed based on VLA models. How did you prepare to create this factory?

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Cedrik Neike9:23:26

We selected the task with the best return on investment. We did physical training, created a data set, and enhanced it with photorealistic data from Nvidia Omniverse. Then we fine-tuned existing VAS to run on our system.

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Patrick9:23:56

We have a gentleman here.

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Mohit Agraal9:24:01

Hi, Mohit Agraal from Counterpoint Research. I'm always interested in numbers. Can you share some data on ROI or interesting stats?

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Vazi9:24:29

Yes, I gave some stats earlier: 2 to 5 times faster execution, up to 80% higher solution quality, and up to 50% productivity gain on the engineering side. And we can share the IRR.

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Cedrik Neike9:25:15

Thanks, Vazi. If you can automate something at all, our solution is probably 10 times better, with a return on investment that other versions wouldn't have. Factor 10.

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Rob Smith9:25:18

When you put autonomous robotics in a supply chain facility, you're automating work done by people. The payback is that. But also, every robot is instructed by the digital twin brain, which tells each agent the next optimal step in real time. It's replacing humans with robotics making optimized decisions at every step.

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Patrick9:26:12

Okay, we have two questions. First to the lady on the left, then you next.

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Joanna Smith9:26:22

Hi, Joanna Smith, reporter for The Logic based in Canada. In Canada, engineers wear an iron ring as a symbol of humility. What is the safety feature for this AI agent?

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Patrick9:26:49

We also got a similar question: if the agent self-assesses, who assesses the agent?

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Vazi9:27:05

Ultimately, it's the job of the automation engineer to make sure everything works. The agent takes away the grunt work, allowing the engineer to focus on things that could go wrong. They now have quality time to focus on what matters.

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Cedrik Neike9:27:46

Safety is one of the highest priorities in our products. When you build a bridge, people can die. We are very aware and ensure safety is put in place.

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Vazi9:28:06

And on top of that, the agent does automatic testing. But we also have a virtual PLC or simulation to test before deployment. For mission-critical applications, you definitely simulate upfront.

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Patrick9:28:35

We have a lady here in the front.

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Soy Langanger9:28:36

Thank you. My name is Soy Langanger from Orchex and executive student. My question is about AI deployment strategy. Europe is often seen as fragile due to bureaucracy. How is Siemens collaborating with partners and startups to take care of people?

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Cedrik Neike9:30:02

AI will change jobs, but similar to robots in the 70s and 80s. Countries with the most robots are the most industrially competitive and have the most jobs. Europe must rewrite its economic playbook to use AI to be competitive and create jobs. We work with big, medium, and small companies and startups to make this technology available to everyone. Regarding regulation, personal data should be protected, but industrial data should not be overregulated or companies will move to the US or China. We need to be careful.

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Mauricio9:31:55

Working? My name is Mauricio, reporter for Brazilian Daily Foot. My question relates to China's lead in industrial AI. What is the remaining edge for European companies like Siemens?

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Cedrik Neike9:32:16

There are two industrial superpowers: China and the US. China has very efficient AI models and advanced factories. The US is strong on models and re-industrializing fast. Europe's only chance is fast adoption using our capability and knowledge. Three of the four biggest electrical and automation companies are European. We have to take that advantage. Every time we wait, we lose. We must move in all three regions to be competitive.

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Vazi9:33:28

I want to add one thing. The real value will be created at the application layer, not the model layer. Models will commoditize. Europe has the opportunity to build those applications.

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Cedrik Neike9:33:47

Yeah. And in Brazil too, of course. Brazil is the industrial hub of the southern hemisphere with 350,000 industrial companies. Brazil needs to work with partners like Siemens to keep competitive using physical AI.

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Patrick9:34:25

Okay, then I think we have a lady here in the middle.

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Marilyn Martin9:34:28

Yeah, Marilyn Martin from Bloomberg News. On the IEN engineering agent, do you see a specific market or vertical where it will be adopted fastest?

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Vazi9:34:39

Currently we're targeting automation engineers, factory automation engineers. It will find rapid adoption there. It's a horizontal capability applicable to many verticals, but over time it can expand to other parts of our portfolio.

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Patrick9:35:20

Another gentleman here in the middle.

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David Humphrey9:35:23

Yes. Hi. David Humphrey from ARC Advisory Group. I see a disconnect in how AI is perceived in industry versus the rest of the world. In industry, AI is a timesaver, not scary. Why do we do things better in industry?

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Cedrik Neike9:36:27

Maybe I'll start. Automation engineers look for 1%, 2%, 3% improvements. We push boundaries on automation. It's less scary because in our most automated factories, we still employ the same number of people as in the 80s, just more highly qualified. We've trained them. There aren't enough people capable of automation, so anything that helps is welcome.

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Rob Smith9:37:34

Maybe the difference is the objectivity in industry. It's a fact- and data-based assessment. We automate to make every job better. Jobs in factories with AI and robotics are better jobs. It's perceived as value added. And adopting AI helps companies become very competitive.

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Cedrik Neike9:38:48

I'll give you a specific example. Our electronic factories have been running efficiencies of 5-7%, mechanical 7-9%. We've used lean and kaizen, but they reached their maximum. We then digitalized and now use AI. In Alangan, we started with one robot arm and two AGVs. Now we have over 100 AGVs, over 100 robots, and 50-60 AI use cases. The team continuously pushed the envelope. As lean ends, you need digitalization and AI to go forward.

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Vazi9:39:55

Can I add one more thing? Don't believe everything you hear from the digital colleagues. That's another reason for the discrepancy.

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Patrick9:40:10

Always take it with a grain of salt. So do we have a last question from the audience? We are coming to an end. It's been a great pleasure. Thank you to our panelists and speakers. Now we move to the networking area. Thank you.

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Rob Smith9:40:43

Thank you for including me. What a pleasure.

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Cedrik Neike9:40:44

It was absolutely...

Siemens | Live from Hannover Messe 2026

About Cedrik Neike