Q&A Summary.

What major trends do you foresee shaping the future of manufacturing over the next decades, especially in terms of sustainability?

Looking ahead, the essence of manufacturing might not change drastically if we maintain the current approach to product creation. Cars, for instance, will continue to have four wheels, glass windshields, and metal bodies. However, significant transformation will occur in how these products are customized and produced. Products will increasingly become software-defined, driving the industry towards greater customization. This shift, combined with global workforce shortages, particularly in skilled labor, suggests a significant increase in automation combined with artificial intelligence (AI). These intelligent automated processes are essential, especially as production shifts back from overseas to regions like Europe or the U.S., to maintain commercial viability.

However, to leverage these advanced technologies, a robust data foundation is critical. Modern factories need to build comprehensive data architectures before implementing sophisticated use cases like AI-driven automation. This intersection of automation, AI, and data infrastructure defines the trajectory of manufacturing in the coming years.

Given the rise of automation and AI, do you believe humans will remain central to manufacturing processes?

While many emphasize keeping humans at the center, the rapid evolution of AI suggests their role will diminish, particularly in repetitive or highly standardized processes.

Creative stages, such as research and development, might retain strong human involvement. Yet, it's unrealistic to assume complete automation everywhere due to cost considerations.

Regions where labor costs remain relatively low might still see factories filled with human workers decades from now. Nevertheless, industries focusing on software-defined products are bound to experience substantial growth in automated and autonomous production environments.

You highlighted the role of materials in revolutionizing manufacturing processes. Can you explain why material engineering is critical?

Material engineering fundamentally shapes manufacturing by determining production processes and the nature of final products. Advancements in materials directly influence a company's and even a country's competitive edge. For instance, semiconductor materials significantly shape technology sectors, with notable progress driving profound changes in product capabilities.

Moreover, innovations in materials related to sustainability—like lightweight materials or highly recyclable components—are pivotal, affecting both product design and life-cycle management.

Could you give specific examples of how innovative materials are transforming industries today?

Lightweight materials, for example, play a crucial role in the automotive and aerospace industries by reducing vehicle weight, leading to increased fuel efficiency and decreased environmental impact.

Additionally, the semiconductor industry sees rapid advancements, directly influencing electronics and computing capabilities, pushing technological boundaries significantly forward. Materials with enhanced recyclability are also pivotal, affecting not just sustainability but the broader production processes and business models.

How can manufacturers balance profitability with increasing sustainability pressures, especially in highly regulated regions like Europe?

Balancing profitability and sustainability is particularly challenging in regions like Europe due to stringent regulatory frameworks and limited natural resources. Manufacturers must be innovative in optimizing available materials and enhancing production efficiency. Smart leveraging of local resources, combined with innovative material usage and recycling, can help address these dual pressures effectively.

Businesses must integrate sustainability into their strategic planning to remain profitable and compliant, given Europe's tight regulations on environmental and resource management.

You recently discussed the concept of hyper-automation in manufacturing. Could you explain what hyper-automation entails and its implications?

Hyper-automation describes a highly automated, end-to-end manufacturing process primarily managed by algorithms rather than humans. This integration includes physical production lines, robotic systems, and a sophisticated digital layer driven by AI. The key benefit is unprecedented efficiency and precision across manufacturing processes, significantly reducing human error and downtime. However, achieving hyper-automation requires sophisticated virtual infrastructure, including robust digital twins and real-time data analytics, making the transition complex but transformative.

You’ve also mentioned physical limitations in manufacturing processes. How do these constraints impact innovation?

Physical constraints are inherent in every manufacturing process and are closely linked to the materials and products involved. For example, there's limited scope for exponential efficiency improvements in producing a well-defined product. While simulations and incremental technological improvements are possible, they eventually hit natural limits unless the fundamental approach to production or product design changes significantly. Modern engineering now integrates simulations deeply into design processes to navigate these constraints more effectively. Thus, product and process design increasingly consider not just traditional manufacturing methods but also digital simulations and AI-driven optimizations.

Looking forward, what practical steps should manufacturers take to thrive in this evolving landscape?

Manufacturers should begin by reinforcing their data infrastructure, establishing robust data foundations that can support advanced automation and AI systems. Investing in material innovation will also provide competitive advantages and help address sustainability goals.

Additionally, embracing hyper-automation—while recognizing and strategically planning around physical limits—can ensure manufacturers stay ahead of industry disruptions. Ultimately, a balanced focus on innovative technology, sustainable practices, and strategic adaptability will be key for future-proofing manufacturing operations.

3D Printing has often been touted as revolutionary. Yet, its practical adoption seems to lag behind expectations. Which industries are seeing substantial benefits from this technology right now?

Certainly, 3D printing didn't quite fulfill the exponential growth expectations from about a decade ago. Initially, many anticipated rapid and widespread adoption, especially due to its promise for remote customization.

However, the real-world scenario has shown that its greatest practical impact is currently seen in rapid prototyping and engineering development rather than broad industrial deployment. For instance, startups or smaller enterprises significantly benefit from online "quote-to-order" platforms where they can submit design specifications and receive parts without needing their own supply chains. This effectively accelerates their product development cycles, allowing them to focus primarily on design, testing, and iteration rather than logistics. Additionally, industries like aerospace and automotive are increasingly utilizing 3D printing to produce complex, lightweight parts, drastically reducing lead times and material waste.

In healthcare, customized prosthetics and implants have seen remarkable improvements due to additive manufacturing technologies, underscoring the personalized approach that 3D printing can offer.

Such streamlined prototyping capabilities are critical in today’s fast-paced engineering environments and contribute significantly to industry advancements.

You emphasized the importance of accelerating innovation. How is 3D printing contributing specifically to the innovation landscape, particularly in regions like Europe?

3D printing is a pivotal element in enhancing the innovation speed within Europe, especially in manufacturing and engineering.

Historically, European engineering sectors have struggled somewhat to keep pace with rapid global developments, especially compared to China or the U.S. 3D printing, coupled with digital technologies, empowers engineers to iterate more quickly on designs, substantially shortening the development timelines for new products.

The availability of these rapid prototyping tools enables a continuous learning and development cycle, crucial for European industries striving to maintain competitiveness in the global market. Moreover, initiatives and partnerships among research institutions, tech companies, and governments in Europe are increasingly focused on leveraging 3D printing for advanced manufacturing solutions, significantly fostering innovation and cross-sector collaboration.

Transitioning from individual technologies to a broader industrial context, Industry 4.0 has become a buzzword globally, starting in Germany and now prominently featuring in Chinese strategies as well. In your view, what exactly does Industry 4.0 signify today, and how has its perception evolved over the years?

Initially, Industry 4.0 represented a symbolic push, particularly in Germany, to rejuvenate local manufacturing industries. Its foundational principle revolved around integrating cyber and physical systems—creating a seamless intersection between digital data and physical machinery. Initially, the concept focused heavily on big data, evolving progressively to incorporate artificial intelligence and machine learning. Technologies like 3D printing and robotics embody this concept vividly, transitioning digital designs directly into tangible products.

However, early optimism met reality as companies confronted significant hurdles, primarily around unrealistic management expectations regarding immediate benefits. Many businesses invested heavily in digitization and technology implementation but found that expected returns did not reflect swiftly in financial results. Over time, these organizations realized that foundational work—such as comprehensive data governance and integration—was crucial before tackling advanced, isolated use-cases. Industry 4.0 has gradually matured into a more pragmatic approach where incremental yet strategic implementation has become the norm rather than immediate, widespread digital overhauls.

Speaking of integration, it seems this has emerged as a substantial challenge within Industry 4.0 implementations. How are companies now approaching this issue, given past setbacks?

Indeed, integration has proven to be a critical challenge, often underestimated initially. In many companies, digital transformation paradoxically resulted in increased complexity and fragmented data silos, with different systems providing various pieces of data without effective coordination. Companies now recognize the necessity of returning to foundational strategies, prioritizing robust data governance and seamless system integration.

A practical illustration is evident in the automotive industry, where a worker might encounter data drawn from multiple independent IT systems simultaneously. Addressing this complexity demands focused efforts by IT integrators to harmonize data sources. Thus, many companies are now revisiting their strategies, emphasizing the importance of accessible, unified data as the bedrock for successful Industry 4.0 deployments. Additionally, there is increased investment in middleware and standardized communication protocols, facilitating smoother interoperability among diverse technological platforms.

Looking ahead, what role do you envision emerging technologies, like digital twins and industrial metaverse concepts, playing in enhancing sustainable manufacturing practices?

Emerging technologies, notably digital twins and the broader concept of an industrial metaverse, hold enormous potential across manufacturing processes. A digital twin captures comprehensive, real-time digital representations of a physical product’s lifecycle, encompassing design, manufacturing, and even post-sale usage. This technology profoundly enhances sustainability by enabling detailed simulations, predictive maintenance, and real-time optimization, leading to better resource efficiency and reduced waste.

Additionally, while technologies such as quantum computing, artificial general intelligence, and humanoid robotics are still developing and subject to skepticism among factory operators, their potential to revolutionize industrial efficiency and sustainability remains high. For instance, humanoid robots and physical AI could significantly automate processes, reducing human error and improving precision in manufacturing, thereby directly contributing to sustainability objectives. Furthermore, the industrial metaverse offers a collaborative virtual environment, promoting global cooperation in innovation and sustainability efforts, reducing the need for travel, and supporting remote collaboration, thus positively impacting environmental sustainability.

Finally, how crucial do you believe continuous learning and education are in realizing the full potential of these transformative technologies in manufacturing?

Continuous learning and education are absolutely critical in manufacturing today. Traditionally, manufacturing environments were highly operational, leaving little room for professional development. However, the rapid evolution of digital tools demands that manufacturing professionals constantly update their knowledge to leverage these innovations effectively.

Organizations must foster a culture of learning to overcome biases and skepticism, particularly regarding technologies like artificial intelligence and advanced robotics. This involves comprehensive training programs, workshops, and hands-on experience with new tools and platforms. Promoting this educational approach is vital for enabling employees to fully utilize new technologies that can greatly simplify complex processes, enhance productivity, and drive industrial efficiency forward. Ultimately, organizations that prioritize continuous education will lead in successfully adopting transformative manufacturing technologies.

Transcript.

Peter: Okay. Do you want to quickly introduce yourself? Is there anything you would like to add? Here is your chance.

Jan: All right. Thank you. Hi, everyone. Hi, Peter. Thank you for having me. Happy New Year, actually. Right? We're still in January, so I think it's a proper greeting. So Jan Burian here. I'm actually an analyst by heart, yes. So what I do, I provide research on the technology being developed, used, deployed in manufacturing environment for, I don't know, 15, 20 years. So all my career, I spent in manufacturing, supply chain environment, straight on a, let's say, factory shop floor then as a consultant, business consultant, with the big four. And last five years, with IDC as manufacturing insight. Now I'm with Trask Solution, which is actually an IT service provider. Funny fact is that my life partner, she told me, she was like, "Okay. Now you transition again from being a pure theorist to hands-on business, so now you deal with the real problems of the real people." That's actually very true. So I'm very grateful for this chance, and I'm ready to share some of my experience with the audience here.

Peter: Thank you. So what do you see in terms of future trends? What significant trends are shaping the future of manufacturing in the next decades, and how do you see these trends relate to sustainability?

Jan: That's actually a tricky question, right? I would like also to avoid kind of like a buzz word. Looking into the future, I'm always thinking in certain perspectives, right? One perspective is, if we keep the status quo of the products we produce in a way we're producing them, in a way we're using them, I don't think that there's going to be that much change in manufacturing. It'll still be something like material processing, there will be some machining, whatever, assembly. The car still looks as a car, right? So it's not a disruptive change. It's not like a floating bubble or something, right? It's still like four wheels, rubber tires. It's still like windshield made of glass. So it's still like the steel aluminum, whatever. Then, of course, it also determines the way how the product is being produced. Actually, here, if we go towards the more, I'd say, customization, the products are being even more customized. They are more software-defined. Whatever it is, car, but it could be something else as well. Then, of course, the automation and sort of like intelligence being added on top of the automation, that's where the trend goes. Right? Also, if you can look around the world, especially in the Western world, there's the lack of the workforce, especially the skilled one. Again, there's a trend to shift certain type of production back in Europe or in Asia, whatever, to the US. Then to make the production, let's say to make sense out of it from a commercial perspective, again, you need to automize and you need to blend that automation with the power of the automation with artificial intelligence. That's the way it is, right? But before you do that, actually, what do you need? What you need too is data foundation, right? Even a factor of today, the factor of the future, they need to have robust data foundation, like that. And on that, you can build all those super cool new use cases.

Peter: Of course. So you just mentioned that it would probably be automation coupled with AI. It would be the dominating technology for the coming decade.

Jan: Yeah, that's what I believe. I mean, I hear a lot of talks about being a human at the center of those, the processes. But personally, I believe, nowadays, of course, human still should be somewhere in the process. Right? But if you just look at AI, how it's been developed, it's like exponential growth, exponential development. Right? So there will be less and less space for the human brain in this manufacturing process. Maybe at the beginning where you have an R&D part, it could be more creative. But when it comes to the production, I'll see. On the other hand, it's all made of cost, right? So you still have countries. So production environments. I mean, in 10, 20 years from now, let's don't be naive. So there will be still factories or production environment full of people as it is, right? Because that commercial aspect matters a lot. But when it comes to the products, especially software-defined, that I believe is going to be really the level of automation and autonomous production environment is going to grow.

Peter: Making a little jump to material innovation. In your articles, you actually brought the importance of material engineering. Can you elaborate how materials can revolutionize the manufacturing processes?

Jan: Yeah, I mean from a perspective, right? Because I'm actually a big believer in the power of primary research. Because this is where I can see there is a real value that is happening. So the entire enterprise, even countries, are let's say based on the smart leveraging of the primary research output, such as the materials. So this is very important for any organization or a country's competitiveness, right? Because the material itself, it determines the way how it's going to be processed and what the output or what products could be produced out of the material.

Peter: Do you have some examples maybe? Anything tangible?

Jan: I think it's a good point. So it could be different types of fabric, or it could be even in silicon industry, right? So in semiconductor industry, that's actually the area where this development is being quite prominent. Of course, it could be also the materials which are related somehow to sustainability and to be more environment-friendly. But my point here was really to have materials like lightweight materials. Because lightweight is also the trend in your engineering. This also has the impact actually on the sustainability efforts in a company. Also, the materials to recyclability is also very important here. So again, that actually determines the way how we produce, how we reproduce, even the entire life cycle of the product.

Peter: Yeah, I understand. Okay. So this also plays into sustainability then, this whole material topic, right, how can manufacturers balance actually the profitability and the sustainability goals, especially in the light of increasing pressure from authorities, how do you think they can actually balance between sustainability and profitability. Must be a big challenge depending on where manufacturers are. Then obviously, materials will play a role in technology.

Jan: Especially if you do your business in Europe, it's kind of like over-regulated environment. So you need to be, I would say, twice as smart when it comes to the production. Because also, the sustainability here is, again, determined or is related to the material regulations but also to the availability of the materials. Just because here in Europe, there's not much left. The coal or the iron ore, whatever minerals, there's nothing here. So it's also the way how to smartly using or leveraging the materials or the chemicals, whatever is available here.

Peter: Okay. All right. And then I wanted to focus on those two articles you wrote. The first one is what's really shaping the future of manufacturing. There were some interesting things you mentioned, the first one being hyper automation. So you discussed hyper automation in manufacturing, right? Can you explain what that entails, what it means? What's the benefits and the challenges for manufacturers?

Jan: Yeah, of course, there might be different understandings of what hyper automation means. For me, it's really like the end-to-end process, which runs pretty much in an autonomous way and which is also managed by, added much by the people themselves but really by the algorithms, by the, actually, ones and zeros. That's why it's like that. That's why it is. It's also like a confluence of a shop floor automation, production lines combined with the robots, but also combined with the or by the intelligence which is behind it or above it, whatever, over there. So that's how I see it. To enable that hyper automation, of course, there's a virtual world which is also actually enabling this as being up and running. So that's much more complex from what you see when you go to the shop floor, the physical world, but also the world of the digital data and data layer.

Peter: Okay. Then you mentioned also physical limits. What are these implications? There are physical limits of products that are produced. That's what you mentioned in that article as well. How does this fit together? Why are there limitations?

Jan: I would say that it's very — again, the production process is determined to the product itself, to the material which you need to process, and so on. So there are some limits to command this thing. So you can be like — let me select an example. On a factory floor, probably there's no exponential growth in efficiency when it comes to product, which is pretty well-defined. So we need to change something, right? So you can do the simulations, whatever you want. But when it comes to the production process, then to a certain level, you can change the technology. But again, there's limits. So you can go beyond that until you change the entire logic of production, for example. Or you need to also be thinking about — but that's actually a classic technique of engineering where you were thinking about the design to manufacture, actually. But nowadays, you need to be thinking about design, simulate, to manufacture, but also design and simulate to manufacture with having the data and the AI layer above it. It's just something I think and I believe for many engineers or people designing the production process.

Peter: Okay. Then another topic I found here is about 3D printing. You said there are quite some limitations when it comes to 3D printing. So what specific industries do you think will be the ones benefiting most from 3D printing nowadays and in the future?

Jan: I mean, this is a really good point. Because when I started looking at the 3D printing — it's like 10, 12 years ago — it looked like there would be really like, again, an exponential growth in the performance of this technology. Because it's super useful, especially for the remote areas, to customize the product, right? It's still waiting for how we're ready for it, water deployment, for many reasons, of course. But I see that, on the other hand, especially in the area of rapid prototyping and engineering, I would say that this is something we should speed up the process. Of course, we can talk about the industrial use. But anyone can, let's say, Google those use cases. It's nothing that much new. But where I see a real power race in this, in engineering development part, of course. Also, it leads to another thought.

Peter: In product design, you mean?

Jan: Yeah, because the point is that, two situations may be. Right? There are some, let's say, like a quote to order platforms, for example, where you can just if you're like a small company or also a startup and you don't have your own supply chain, you can order. I mean, you just send over your drawing specifications, and those companies or providers, they are able to provide you with the part to the certain level of quantity from defined production places. Right? So like India, China, Mexico, for example. And you don't need to have your own supply chain, right? So you can really focus on development of the things, right? Testing, digital testing. You can just order your parts for this development part, for the process, I mean. There are those platforms, right? This is where the 3D printing has, it really has impact. By the way, that also — sorry, I bridge to my favorite topic. Let's say, speed of innovation. Especially, here in Europe, the speed of innovation is something which is really crucial for the future success of the local manufacturing or engineering, to this area. So those engineers, they need to really learn how to speed up the software development but also the development of the physical parts, and how to use the digital tools to speed up that, to really be able to compete with the rest of the world. That's what we need to learn. I mean, a little learning around.

Peter: Okay. I guess that's also a big topic. It's the education around the whole topic on what technologies are available nowadays to get your job done when you work in design, product development or wherever in manufacturing.

(break)

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(interview)

Peter: Actually, to this article in general, Industry 4.0, a very big topic from Germany and around the world. Now it's a big topic in China as well. So how do you envision the principles of Industry 4.0 to evolve in the next few years? Obviously, everything is going to be data-driven, I would guess. But where do you see Industry 4.0 taking its course, to which direction? Where will it lead us? 5.0? What is your opinion here in this broad field?

Jan: That's actually a good question. I remember when it all started. At the very beginning, for me, the industry 4.0 was more sort of like a light motif. It means like, now let's do something. Let's make the German industry great again. So that was the initial thought at the beginning. But it became sort of like a symbol of everything new. Or the foundation principle of Industry 4.0 was that merge of the cyber world and the physical world, like cyber physical systems. At the very beginning, AI was not there really. It was more about like a big data. Then it turned, this narrative turned into, let's use AI or machine learning at the very beginning and so on. So it developed, right? I mean, from having data, big data, data analysis. And actually, even 3D printing is actually, I would say it's really like literally the example of how the cyber world turns into the physical world. Right? A robot is the same thing, right? So you have ones and zeros at the beginning and makes the robot turn or to do something. That's the thing. So that was really the initial thought, cyber physical systems. But I think that the people were maybe, of course, there were a lot of skepticism. But the world's leaders, they were maybe too optimistic. A lot of even companies, even enterprises, they jumped on that wave to try to be really the leaders in these areas. But then, after several years, I could see that there were many — they were like, "Hey, let's stop. Because we invested a lot of energy in that, in the entire strategies and deployment and testing new technologies, and we don't see the results in our PNLs or at least other results which were expected or promised." Right? Because the enemy of those disruptive activities are, I would say — I would phrase it like that. The enemy of these disruptions are the wrong expectations of the management. So the managers are convinced that it's going to be super success. Then they see that during the entire journey, that it's really quite a rocky thing. Then, of course, they started thinking how to cut this, cut that, and how to redefine the whole thing. Of course, if you would like to really succeed in implementing and deployment or in benefiting from the industry 4.0, you need to start with a foundation. Right? You need to understand your data, and then you can build it like that.

Peter: Integration, right?

Jan: Yeah, it was something that a lot of companies underestimated. And they were like, let's try product maintenance here, product quality there. Let's try to order almost over there, right? The more digitized they were, actually, the more siloed they were. Because for every single use case, there were different application, different set of data or whatever. They're just like, they start to be super complex and over complicated. So what we can see now and I can see now with my current employer, that organizations are getting back to basics. They are like, "Hey, we need to really have that. We need to start from the data governance, data format, and then we can build the use cases." In many companies, especially global ones, typically in automotive, you can see this. If you work on a line and you look at the screen, like a monitor, there are some working instructions and some other information. The data source could be like five, maybe eight different systems, different IT systems, providing information for the shop floor worker, right? So it's really a good job for IT integrators, right? But in reality, it's super tough to manage all that complex environment.

Peter: Yeah, the integration is a very big challenge. I'm an old SAP project guy, you know. Yeah, integration is the key, integration between top floor and shop floor. And then making data in silos accessible is really a topic that was totally underestimated. Yeah, I agree. So you think actually people are going back to the drawing board, or companies are going back to the drawing board saying, okay, we'll do it again now, collecting data. But we'll make sure they are also accessible in order to make sense of it in an easy way and draw conclusions, really.

Jan: What I also see, there are so many really cool tools or solutions. Somehow, it's related to the part of the education or the perspective of how to educate the people. Because also, I mean, speaking for myself, I used to start my career working in factories, especially quality management and project management. It wasn't that part of my daily routine to absorb new information. I worked like, I don't know, like nine hours a day. And it was it, right? So I was very, very focused on the daily operations. I didn't have any capacity to develop myself or to educate myself. Maybe 20 years ago, it didn't really matter. But now, with all those possibilities, the tools which could really make your life much more easier on the industrial level, that's unbelievable. So the organizations also need to implement not just a strategy but the entire learning culture. It's not like a buzzword or a cheap recommendation, right? I wrote several articles about this actually, because I can see how biased some people are working in environments. Like you can talk to the — I don't know if you speak or talk to the head of IT of some. Even a big company is like, "Yeah, AI. Why bother AI?" I mean, it's been here for 15 years, for example, right? So I'm like, all right. Actually, in principle, you might be right. But now you can see, with generative AI, this could help you. Like if you have a international footprint, I mean, the possibilities are almost endless, right? So that's what I see, sometimes what I'm missing, is that sort of education among the people.

Peter: Yeah, that's of course a very big topic, but I think the whole education topic might be a bit too much for today. But it's still a topic we should talk about at some point in time, which I believe is a very, very big factor decoupling the thought of manufacturing with unskilled and dirty or something, that has to be overcome nowadays, especially in the West. I would say that we nearly hit the 30 minutes, so let's close with a final question about the future outlook. So, looking ahead, what do you see in terms of advancements in using emerging technologies to improve sustainable manufacturing? If you want, within the automotive industry, I think this is mostly your playing field.

Jan: Yeah, I'd say there's one tech that actually goes across the entire product process. That's actually digital twin. Or sometimes we go even further. We call it like the digital twin or digital twins, or even like industrial metaverse, right? Something which is capturing the entire R&D, product, process, and the way it's being used, after sales as well, right? So the entire lifecycle of the product and make it like a — of course, there's a digital way. There should be also the physical and digital way of blending both together. So this is where I see that's really a tech which is going to help to optimize not just a process and also not just to simulate but also to have a real impact on the way the product is being designed, manufactured, and being used, right? So that's I would say number one, let's call it, technology which I see. Right?

I would like to talk about quantum computing and the other cool stuff or artificial general intelligence. But it's still all the humanoid robots actually, right? So this is like a hot topic, especially in China. By the way, just maybe a comment on this. Because I find a person really cool because it's something really like a new one, I really enjoy looking at those developments. But of course, if you talk to the people in the factories, especially the ones who are responsible for the fleets of the robots, they are still kind of skeptical, right? So there's still some times, some years ahead to see where this actually goes. But I can also see this is going to be the future, this physical AI combined with the automation.

Peter: Yeah. Okay. Great. Well, Jan Burian, thanks a lot. I think that was very valuable information you conveyed here for us today. So thanks everybody for listening in and hear you at the next podcast.