Episode 3: Gunter Beitinger

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Welcome to the Factory Office Exchange podcast, hosted by the US center for Advanced Manufacturing and by Guide Wheel. I'm Lauren Dunford, CEO of Guidewheel and executive committee member for the US center for Advanced Manufacturing. Today, I'm absolutely thrilled to be joined by Gunter Beitinger, a leader with 25 years at Siemens who has helped shape the future of manufacturing and inspired teams across the globe. In this episode, Gunter shares his guiding principles for leadership. Lessons from how best to successfully roll out AI on the shop floor and how to turn challenges into opportunities that strengthen teams and deliver impact. Let's dive.

So, Gunter, I feel so lucky to get to be here with you today because you are one of the people where whenever I mention your name to anybody within Siemens, they glow. And what they say is, Gunter is such a fantastic leader. Everyone I've met who's been managed by you has said you're just one of the most amazing managers they've ever had. Everyone within your teams has talked about what a leader you are and that ability to bring both amazing leadership and some of the most advanced thoughtfulness around all of the topics shaping manufacturing today. You just released this new book, Crisp Manufacturing.

Leading all of Siemens efforts. I have my personal favorite Sea Green sweatshirt. From the work you've done with Sea Green, it seems like you are just doing everything and managing to do it in a really thoughtful, wonderful way and be a great leader along the way. So I want to approach it by maybe first just asking that approach that you have of thinking about how we solve challenges, how we turn them into opportunities. Talk to me a little bit about. About that and where it came from and your thoughts on. On the how.

On. Yeah. Okay. Nice. First of all, thank you very much, Lauren, for these words. And I'm happy that people feel, say, inspired when they are working with me because I get also inspired by all my people, which I'm working around. I take so much out of conversations with you. Also when we met, it was immediately inspirational for me what you are doing. And I incorporated also it in my work. So I think this is always this taking and giving which is so important when we are working together with people. People.

Yeah.

Over time, I think.

Now I'm 25 years with Siemens, by the way, and I have a long history now I gained a deeper understanding from my point of view how leadership, organizational culture and job environment are inseparably connected. Yeah. How they are directly shape the job satisfaction, performance, and also creativity and inspiration. That's something which I always take into account from the perspective of leadership, responsibility, yeah.

And you've had the opportunity to lead on the shop floor, lead digital transformation across many factories.

I love this concept that you mentioned right before we started of when there's a problem, does it have to be a problem? It could be a challenge that we turn into an opportunity. One of my personal favorite quotes is, can all people and circumstances be our allies in helping us learn the best next thing for our growth? Any examples or stories that come to mind that really reinforced that approach of, you know, turning challenge into opportunity? Talk to me about that.

Yeah.

Or how you do that when you're coming into, to work with a new team.

When, when I come into, into a new team, of course the first thing is what I'm doing is observing.

Yeah.

So.

I have realized over this excessive time on the shop floor and different cultures that relying on past experiences and established programs is not enough when we face disruptive shifts and rapid technological transformations. So when I go into an organization, and especially if it is with an intention to.

To do, to adopt or to.

Adapt to a new situation or to bring in new programs, is that I definitely observe, observe openly. And I try to become one with the situation and to understand why are the people and all my team, my new team is doing it that way that they are doing it at the moment. Yeah. And.

And this helps me then to, to step back to reflect and, and, and I try to, to understand it very deeply before I put any, any fast decisions out or something. I slow down, observe deeply and allow new insights more to emerge and then I can come out and act with more clarity and more natural flow. And of course when I go there and I start to ask questions, most time then.

They are sometimes even asking why are you asking this question?

Because one example was I come into a new factory and then the people were really keen to, to present all the improvements, what they have done. So I went from one team to another, small teams on the shop floor, and every team was telling me we improved that or here and there. And then I always asked why did you improved it? And I said.

Yeah, because.

It was not working well. So yeah, okay, but the question was more what was the problem? Why you changed this, what was the problem? And how this problem affect your daily work? And when you removed it, what was the improvement later and to which bigger improvement or performance increase has this.

Accomplished? Or yeah, which bigger problem you have, which part of a bigger problem you have learned? So then they feel sometimes really upset and said what are these stupid questions? The idea was behind of these questions is that I wanted to slowly teach or to.

Tell the team if we are improving something and we want to have a greater impact, we need to align all our improvements so that we are working towards an overall goal and not just.

I see there is an opportunity to improve or to change something so I do it. But then I missed the, the impact. So this something and this realizing is then. So after sensing the observing openly and then these present things or this stepping back and reflecting is then realizing which helps then the people to act swiftly and effectively, guided by a deeper purpose and by a deeper awareness where we have then a, a bigger impact. And that's how I normally start to work with new teams.

Yeah, love it. Listening, observing, immersing and feeling yourself in that situation and then asking the right questions that help folks think for themselves. It's funny, I was so everything is quite similar when you're running a process. And so for us it's about releasing new engineering features. And I was lucky to get to sit in on the engineering team demos yesterday where all the different engineers were presenting the features that they just built. And the question our head of engineering would ask if it wasn't already covered was what value does this drive for the customer? And I think it's the same type of thing. Yeah, that's wonderful.

Yeah. This value for the customer is the overall, let's say purpose. So it's exactly the same, Lauren. So when I ask which overall problem you have solved at the end for the customer.

To which overall problem you have contributed a part of the solution. So it's exactly how we can serve better our customer, how we can improve our, for example.

Delivery performance or quality, performance or reliability and so on.

Yeah, love it. You are kind of at the forefront of all of the different things. Well, I don't know about all. There are lots of things happening in manufacturing. You're at the forefront of a lot of the kind of exciting next waves. You also are very grounded in the reality of the plant floor, the history of manufacturing. What are some of the things that get you excited right now about where the world is? What are some of the things that you think are a little overhyped? How are you thinking about parsing what's important and what's not right now?

That's a multi part question.

That's a big question, Lauren. Yeah, of course. I'm totally.

Overwhelmed by the possibilities of AI, of artificial intelligence and of the opportunities, what we can achieve by swiftly implementing it into our shop floor.

And here an AI at the moment, how we are using it. Is that many companies and also in our factories at Siemens we have established processes so many times what I see is that we are setting up our processes, whichever processes or production lines that quality circles more or less a traditional way.

Yes.

And then later we are applying AI to improve it. So here I see a big opportunity that we are thinking AI.

Right from the beginning and integrated into the process development. And the process design, from my point of view requires a completely different thinking. Yeah. One example maybe if I'm allowed to is when we are thinking about a quality process. Quality process by tradition are based on.

Historic data and maybe also with.

With an analyze what possibly can go wrong. And then you are implementing your quality gates and then you have.

A process which is then always interrupted or where quality is integrated accordingly to the value add. If we would this in electronic production. This means sometimes 40 to 50% of all process steps are quality steps. Because what you don't want is that your problem are traveling down the line at the end or.

Maybe ultimately turn to the customer and then he sends it back. That would be worst case, you don't want this. So you have this quality steps. But what if. If you use AI predictive and prescriptive during the process.

By identifying patterns.

Where they tell you right from the beginning which part, which product is good. So you don't need to check it. Because if you have a process where. Which. Where the outcome is defined by how you are.

Manufacture, how good you can control and how robust is your process, then there must be a pattern which tells you this is a good process step. You don't have to check it because you see it of course in the range of possible process parameters.

Yes.

So why you check it? So that's an example. If you would AI right from the beginning integrate in the process design, you could without any.

Possible quality risks or without any.

Quality.

Let'S say reductions.

Produce, but with less efforts.

Yes.

And that's something we on. I don't see too much at the moment in our manufacturing or in our production process. So it's always AI based assistance put on an existing process to improve it.

Fascinating. I that. Yeah, that makes so much sense. And also of course I'm thinking of inside our business. We have the same thing of do we start by thinking about how we do this with AI in mind or do we take how we do this and layer AI on top? I'm curious because there's that. That people element of how you lead people through that change and how you in your role ask the right questions like you Said where you're not necessarily giving the answers, you're asking the right questions to help people think through that new challenge in an AI first way, what have you learned or what are you thinking about for you as a leader in terms of the questions you ask to whom and how to shift from the layer AI on top mindset to the embed AI in. From the start.

When we are started to introduce AI this was really something which put us really on a.

Really on. On a challenging journey with our people. As there was so much fear that AI will replace jobs or they come obsolete or sometimes I'm not any more relevant because then all the knowledge I have which makes me important, I mean that's, that's natural which makes me valuable is then in a bot and.

Why should I now support this transition? So when we were introducing AI, what we have learned is that first of all we developed a kind of a language which the people were used to communicate in their shop floor. So we avoided all the.

Let'S say AI IT terms. So we translated all the terms into their lean language. Yes, that was very important to find.

A level of trustful communication. So we explained what we were aiming to that the implementation of AI in X ray in our X ray quality gates will help us to reduce efforts which was at this time so important as we needed to produce more in a shorter time as we had a huge demand and we were not able just to purchase machines because of course these are assets, but also they are lead time. So we needed really to improve.

And we knew that this will be a critical step. So then when we were.

Really trustful communicating what is all about what we are really trying to do is that people when they are having their X ray observation then they get of course stressed. Then they get tired as they always have to look on a screen and to evaluate if this is.

What we could say a real error or just pseudo error. We say or so was it.

False positive or not? And. And then that this is also for them.

That it has a great value for them as it takes responsibility.

And I will help them to.

Decide. Yes.

And, and still in the first step that I will not replacement. It makes. It makes something, it makes more. It is an support for them who gives them the still the opportunity to overrule the system. That was very important. So when we were introducing introducing them then we in parallel we also made the results that the system always explained them how they came to this.

Yeah result how the system. So the explainable AI was very, very important at the Beginning. Yes. So they, they were step by step they were accepting the way how the decision was made by the, by the AI system. So they said yes, I would have made it the same. So the system is in a certain way acting and coming to the same conclusion is I would have done it. Or sometimes they even said wow, I would not have seen this. But now I see that's good. So then this, this surprising effect was also there. So time by time to just implementing explainable AI was then gaining trust.

Yeah.

Then the next step was we switched from just proposing a solution to them. We switched and said you can inter, there will be a proposal. So the system will make a proposal but you, and if you're not react then he, then it will be accepted as a system but you can still interfere. So it was the next step.

Yes.

So he must stop. As he said, I'm not, I don't agree. In the, in the, in the first step he needed to agree and now he did not agree. And then we found out that they time by time accepted more and more the solutions the system were proposing.

Yes.

And they felt very, very satisfied with the results. And naturally their AI became.

Kind of a co worker for them. Yeah, it was a co worker who supported them in addition to their other duties that they had.

Yes.

So these steps were for us very important to, for the accepting new technology. First of all agreeing on a common language, how to communicate, translating this into their language, how they were using it in their day to day the new technology, then having it very clear and explainable. Then the first step they could, they just had to accept it. So they, they, they actively had to accept the proposal what the system was giving. And then the other step they had actively to interfere with the proposal what the system was giving us. So over these steps we were very successful implementing AI into a running system and had a, an 80%.

Exception of the people. There are always people who are, let's say a little bit more skeptic. But then they will come because they see their co workers are accepting it also. So there is no.

Need for pressure. I mean when we look at modern employees, just as a side.

Comment, when we look at modern employees, I mean they are living already very complex lives. When we Compare it to 50 now, maybe 50, 60 years ago. I mean they have, they have technology on their hand, they making decisions which are, is a multiple of their yearly income. When they are providing the university for their kids or schools they had to finance or they're making an investment in their home. So when you all realize that of course you can give them responsibility when you. During their work. Sometimes we treat them if they were not able to make.

Really.

Impactful decisions. But on their daily life they do this all the time. So they live in complex lives in a globalized digital world already. And then they, you can expect much more from them when it comes to this. So people nowadays, what we have in our operation, they seek autonomy, they seek purpose and recognition. So this is what we see when we introduce. Sometimes we really underestimate our people and we, we are back in times where, where Taylor maybe was writing scientific management or something. Yeah.

Yeah. It's funny, there's a coaching principle of creative, resourceful and whole. There's this assumption that every person is creative, resourceful and whole. And so it's about figuring out how to interact in a way that draws out the creativity. And I, I love how you're putting it. People all the time are making very big decisions. They're very much capable of great responsibility.

Yeah. And another thing what I realized is especially when it comes to our managers in our shop floor, they rather accept a problem.

Yeah.

They cannot, they cannot really solve than a solution they don't understand. So they have problems, they know their problems on the shop floor, they know.

I cannot solve it. But they have found routines to work around.

So they feel comfortable because they are still in a driver's seat. So now you come up with a solution and they don't understand it, they don't accept it. Even if you say it's a solution, all your problems now they don't. If they don't understand it, they are not accepting this solution. So this was one thing, why it was so important to implement the AI routine with an explainable and written down path. What was happening in the background.

Yes. Yes. Yeah. Yeah. Our AI team always talks about interpretability of someone being able to look at and understand the why. And I had never had seemed important. But I love that phased rollout that has folks not just understanding but being able to take control over the degree to which the system can first be approved and then be slowed down. Yeah. Oh, that's awesome. Oh my gosh. I wish we had all the time in the world. I will just ask one last question which is if you were sharing your thoughts for leaders navigating these challenges today, anything you want to make sure people have in mind as they figure out how to, how to navigate this world?

I mean.

If you want me to give a little bit my, my guiding principles for leadership.

Yes.

It's. Then I Would say.

To use the continuous stress constructively. That's it. As a driver for creativity and change rather than only as a risk. The most of my creative thoughts and inventions like Sea Green or the book, I mean they came out of very challenging times. Sea Green was due to all these.

Challenges we faced due to the climate change and the need for carbon free supply chains or let's say carbon efficient supply chains and the book Crisp. I mean we are dealing with uncertain times. So how should I plan.

And really prepare myself for the future if everything is not anymore.

In balance? So I think this is very important. So this is. These times are also driver for creativity. The other thing is for once by some foster reflection and ownership. So encourage people to see how their own.

Behavior and reactions shapes the outcome and to a good so, so reflection and ownership. There's one, one thing as an example. When, when, when you have really a challenge then of course you can think okay, what is the symptom? I remove the symptoms like the aspirin. No, I have a headache. And then. But then what. What was leading to the headache is the next step. And then this is. So you're coming down a little bit the root cause. But the most of the leaders are stopping at the root. Caus for me is what I have with my decisions and with my let's say interferences may be also added to that problem or to this situation. So also reflecting on their leadership how maybe these shaped behavior and the behavior shaped a pattern and then this ultimately was then one of the root causes. So this foster reflection for our management and build a culture through communication and coaching. So open communication coaching means for me asking of course.

And discovering with your team together solutions and then let them and of course.

They learn that they can learn by implementing their solutions and then of course asking what you have learned from this and what still hinders you. So these coaching routines. So making vision and values present in daily routines when you are doing this. And last but not least is this balance of satisfaction and motivation. Removing frustrations is not enough. People also need recognition, responsibility and opportunities to grow and foster an environment.

Which also allows creativity. I think that's the things I would put together for leaders in our times.

Gunther, thank you.

It's really fun to get a taste of the way that you lead as a coach and empower and just it is such an honor to have gotten to dive in on a couple of the many topics that I know you could talk about.

A huge thank you to Gunter for such an inspiring conversation. His journey reminds us that the most meaningful changes in manufacturing happen when we bring together cutting edge technology with people centered leadership, reflection and a culture of trust. If you enjoyed this episode, please share it with a colleague and follow the Factory Apps exchange for more conversations with leaders building the future of manufacturing. Thanks for listening.