How Removing Digitalization Barriers Increases Industrial Value – Podcast Transcript
Digitalization, and the Digital Twin specifically, have arisen as a means of helping companies accelerate processes, lower costs and reduce risk in an increasingly dynamic industrial environment.
In this episode of the Future Ready Podcast from Siemens, we are joined by one of our resident experts on the Digital Twin, Dominik Zettler, Vice President of Simulation for Industrial Systems at Siemens. The conversation explores how companies can alleviate obstacles to starting or scaling digital transformation. The discussion will also examine how artificial intelligence and the Digital Twin will synergize and evolve in the future.
You can listen to the podcast and read the transcript below!
00:00:13 | Conor Peick
Hey there, and welcome to the Future Ready Podcast from Siemens. My name is Conor Peick, and I am a marketing writer at Siemens, as well as one of a few hosts that you will find on the Future Ready Podcast feed. In this episode, we have more from my conversation with Dominik Zettler, Vice President of Simulation for Industrial Systems at Siemens, and one of our resident digital twin experts here on the Future Ready Podcast. As we continue, Dominic and I explore some challenges companies tend to encounter as they embark on a digital transformation and why companies should adopt A think big, start small mindset to alleviate these challenges. We then lightly touch on the synergies between the digital twin and industrial AI, which we will explore more deeply in the next episode. Thanks for joining us and please enjoy the conversation. You know, talking about companies maybe that we’ve talked about companies why some companies are maybe hesitant to start. But obviously some companies have started. They have begun that journey. So for those companies who are on the journey, who have started their digital transformation, what are some of the challenges that you see them tend to encounter, things that maybe slow them down? And then looking ahead to the future, yeah, what benefits do they have to look forward to?
00:01:25 | Dominik Zettler
In general, the first companies we know since years already, in the normal market behavior, early adopters, they are eager, they want to be market leader, they invest, it’s very clear, they see it as an invest for their future. They want to be future ready, future proof. So they have a high internal motivation to be thought leader, leader in the industry, leader in digitalization. So from those We are quite far already. These are all in the same category from the mindset. We want to be leader. We want to be in the pole position. We want to lead the digital transformation also in our company. This is the general mindset for the companies and customers we have. They are a little bit ahead of others and further in this progress. What they tackled in their journey is a little bit what I mentioned already. They also needed to figure out what are the right steps, but they manage it quite well. They learned also that they need to change processes. It’s not only that I buy maybe a new tool, maybe a new simulation tool, or maybe a new other engineering tool or whatever. Now you need to learn how to use it. And this is really Sometimes they come back again and ask for additional trainings and so on. It’s not about the tool training, it’s more how we bring then the tools together and so on. So this is one topic. And then for sure, in different industries, you have different capabilities which are necessary. And this could also depends a little bit if they struggle in example with norms or rules or whatever that they say, okay, in the old world, I know how to do it, but I need to have a test. So how do I do a virtual test? So they sometimes needed to rethink how they apply this new technology in their known world. And this was also learning process for them, how to manage it, that it is in the end successful. And also the old world rules could apply in a new world. if there are really laws behind they need to fulfill or something like this. And there’s one obstacle, some also struggled a little bit in the beginning or maybe in the future as well here and there. If we talk about digital twin and digital models and digital representation of whatever, it’s technology. And if we talk about technology, even if it is only a 3D file of something, we need to fulfill export declarations. because it’s technology. And so they also need to think how and where they use it and how they ship it maybe to their customers or if they use it only internal or not, because it’s technology. It’s not only something in 3D like a 3D movie, it’s a technology in 3D or in a digital twin. And so they need to learn that also this is technology, we need even if it’s digital, have applied the same laws for exporting them.
00:04:41 | Conor Peick
That’s fascinating. I don’t think I’ve ever thought of that aspect of doing this adoption. So then, as companies grapple with some of these challenges, what do you see them doing to develop and evolve their digital twin step by step? You know, you talked about dream big, start small. What do you see them doing to move that initial implementation into bigger and bigger scale?
00:05:11 | Dominik Zettler
Exactly. What if I talk to customers and where they start, start with a valuable use case. And this is something, let’s start quite close to production and quite close to automation in this time. It’s an example validating code. in a virtual world. That means start very simple code validation instead of on a real machine, I do it now against a virtual instance. I check my IOs, my inputs, and so on. I don’t have a real machine, I don’t have a real PLC, but I can check my code already. And so this is a small, simple use case, simple setup, also of tools, and we have maybe then a digital twin of a PLC and the PLC code. And then we need to develop it further. And the companies also developed it further because then they figure out, oh, look, I can go maybe for virtual pre-acceptance test, next use case, virtual commissioning, maybe next use case. Oh, I can maybe train my customers for the machine already, even the machine is still in assembly or maybe on a ship to the facility. So they discover them by themselves, mostly the next ideas, how they can apply it to further steps in their machine building, operating, line building, or whatever they do. They normally, if they started in, I would say, nearly every case I know, they figure out for themselves left and right further things where digital twins or simulation capabilities and everything could help. They start somewhere and maybe upstream or downstream, there are different possibilities where I could utilize the technology stack I’m having, but I need to have a use case which is valuable for the customer or for the company itself.
00:07:05 | Conor Peick
It’s kind of a growth model in a way where you start somewhere with a small seed. And just as that evolves, as you continue to add capabilities, you see where else can we apply this technology to kind of reap some value for us?
00:07:21 | Dominik Zettler
Absolutely. And if you go such a journey, a step-by-step approach, then in, I would say nearly every case, it is then also successful because it’s digestible, it’s holistic steps, it’s not too much for the whole company or for the outside world. So if they do it iteration by iteration. It is super smooth for them. The whole colleagues and so on can learn and develop by themselves into this new world. They are getting into the digitalization topic in general, more and more insight. And then they like it. And then they figure out and then it’s not, oh, they are afraid of it. No, they like it. after two or three years, if you talk again, maybe to some colleagues which have been skeptical in the beginning, they are then the biggest supporter of all.
00:08:12 | Conor Peick
You know, we’ve been talking about how doing this digital transformation is obviously a big investment. You mentioned how the early adopters, they have viewed it as an investment in their future, which is, I think, the correct perspective, you know, whatever my opinion’s worth, but there it is. One thing we have been thinking about, though, is as we continue to develop the technology and look towards the future, how do we lower the barrier to entry for companies so we can maybe help more companies get on board to either start or expand their digital transformation and just make it more accessible to realize that first implementation?
00:08:54 | Dominik Zettler
Now, I think what we do already, but for sure there’s always room for improvement, even for us. is for sure to exactly explain and give real examples for different industries. Because some say, oh, I’m in a special industry. What is this? For this industry, I can’t apply. So sometimes we need to translate to the right industry or to the right customer size. We also have a different customer. We’re talking about small, medium businesses. We have large enterprise accounts. So we have also huge variety on different customer types. And I think if we can translate it quite well into the customer voice, into the customer language, into their use cases into their domain, I think it’s more easy for them to then also because today, many of those customers or potential customers, they need to make their translation of agnostic things, what we say, what our capabilities into their specific domain or into their specific field or environment and so on. And I think here we can really help and grow over the years and exchange a lot of learnings how it is the best way to apply maybe in small businesses, maybe in large businesses, maybe in discrete industries or in process industries. So there I think is room for improvement and it would make it much easier for the customer to better understand how it can help them in their industry, in their market. before to translate it by themselves, what we have on an agnostic way or in a general approach, we need to bring it down for the customers that it is easy to start and have the right tools to start with.
00:10:50 | Conor Peick
Yeah, make it more tangible as the way we started the discussion, it seems.
00:10:55 | Dominik Zettler
Make it more tangible. Yeah, absolutely. Then let’s repeat it. Yeah, make it more tangible, make it doable, and For me, we come often to the same points now in this conversation. Realistic goals. I can also repeat the classical change management. Realistic goals, capabilities are there. No problem. We can do it. Think in a journey. And do not go for boiling the ocean with day one. It will not work.
00:11:27 | Conor Peick
I think it’s great that we’ve got this maybe the more practical, just how do you get started sort of perspective. But now as we look again, as we look more towards the future, I would love to know what you see are maybe some key technological trends, some key developments in the next three to five years that, yeah, things that are happening with comprehensive digital twin that will that will maybe come along and make it even more powerful, even more capable.
00:11:55 | Dominik Zettler
There is one topic nobody could avoid to not say these two letters in a row. One letter is A and the other letter is I. Maybe we should talk about artificial intelligence. For sure, this is really the next… Digital twin is fueled basically by AI. It brings so many different aspects to the next level that what I said, we have maybe come from 5 to 3 approximately. But maybe then if we look exactly into these topics and developments with authentic AI, physical AI and all those things, it will become or will be possible to be even much, much more faster. And then also a second topic where we didn’t talk too much in the upfront, we talk mainly how we come to this digital twin and how we set it up and how we could utilize it. is also the life cycle of the digital twin. I can then also go really longer, doesn’t matter on the product side, we do it longer, but also in the production side, bring this digital twin into the life cycle of the production and get use out of it and not only in the early phases.
00:13:09 | Conor Peick
Yeah, okay, that’s perfect. So let’s talk more about AI and the digital twin. And I think you’re absolutely right, there’s no way we can have this conversation without those two key letters, right? So maybe you can just tell us a bit more how you expect the digital twin will evolve in capability, maybe even in the way that it is used. How do you think those things will change in this new world of AI? And do you think that AI could play a role in speeding up or accelerating the actual creation of a digital twin, especially for a company that’s just getting started?
00:13:46 | Dominik Zettler
Yeah, Really go back to my own experience. What I said from my history, I was basically a mechanical engineer. I was addicted to 3D and I was in automotive. I got task here. We need a welding chick for this and this operation. And then I was the engineer, I was thinking about how I’m doing it, and I was thinking about how I sequential the part, how I automate, how much speed I need for the drives, how much stroke I have in the cylinders or whatever. And everything was in my mind, but I was a mechanical engineer. I had no clue about automation. I had no clue about any other further step. I was not able to operate it. But the logic and how it worked and what this station should do, I knew everything. But I was forced and… 25 years back, I need to write it down, put it into PowerPoint or in Word, describe what this chick is doing, transfer to a totally different person. He then tried to understand what I have written to then make the automation of this chick and so on. And this you can imagine now for multi disciplines with physical simulation, liquid simulation or whatever. And we have Different things what we need to overcome and where AI will help us really is to make it accessible for more people and also extend that, like I was a mechanical engineer, maybe a mechanical engineer in the future or an automation engineer can perform tasks of different roles. can then with agents and so on, enable different tasks because their agents could help me to operate the simulation tools or the complexer things and so on. So if I bring in AI into this whole workflow and we have in the tools itself, we have a lot of AI stuff already implemented. But if we look also in the tool combinations, then we can really level also the skills and the knowledge of the people. And a friend of mine have the sentence, AI will not replace you, a person using AI will. And this is also so super true for digital twins. If we go to the next step, AI will bring the next switch or the next lever in this whole digital twin journey because we will have here total new capabilities for different personas. on the customer side or on the engineering side or whatever in various ways. So this is one example. This is only AI in a few of enabling more personas to interact with digital twins and different engineering and simulation tools. We utilize also digital twins and digital representations of the real world to train AI algorithms upfront which are later running in production or whatever. That means here we have AI not helping us using those tools. So it’s we’re using digital twins to bring AI into the production quicker, faster, better. So that means we can, even the machine is not ready. In the today’s world, in the old world, today’s world, we do it also a while already. But in the classical way, you collect data in the real world, then based on this data, you train an algorithm, then you apply this algorithm, and then you maybe go for reinforcement learning of this algorithm and deploy it again. Now with the digital twin and the digital twin capabilities, we can do it upfront already. That means we do not have even real data. We do not have anything running. but we have a virtual instance of the machine or of the production or of a process, doesn’t matter what it is. So we can train an algorithm already upfront until a certain level and we reduce the time until this algorithm brings really value in the production by factor, it depends on the topic, 10 or whatever, but much, much, much shorter because we train already in the virtual world. And the second topic is I also want to have confidence that my algorithm, which I apply to my production, and maybe they are sensitive goods, if we look to industries like consumer products, goods, food and beverage or whatever, they need to ensure that everything is running, fulfilling their quality. So they need to have also the capabilities to test maybe an algorithm in a digital twin or in a digital representation before they bring it to the real production. So that means digital twins and AI have many, many different spots where it’s a perfect match. And that’s why I always say I’m really looking forward to next years, what’s even possible with AI and digital twins together?
00:18:50 | Conor Peick
Thanks again for joining us on the Future Ready Podcast. There’s more to come from my conversation with Dominic, and not to mention future episodes featuring Dominic in conversation with other experts from Siemens and Beyond. exploring the future of comprehensive digital twin and other technologies. If you enjoyed today’s conversation, I encourage you to subscribe to the feed, as we will have many more conversations with experts from various backgrounds, diving into the most important trends and technologies in industry today. So thanks once again for listening, and we hope that you’ll join us again soon.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
