音频文字.

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)

If you're still listening, then you must take technology seriously. So I'd like to introduce you to our case study database. Think of it as a roadmap that can help you understand which use cases and technologies are being deployed in the world today. We have catalogued more than 9,000 case studies and are adding 500 per month with detailed tagging by industry and function. Our goal is to help you make better investment decisions that are backed by data. Check it out at the link below. And please share your thoughts. We'd love to hear how we can improve it. Thank you, and back to the show.

(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.