Sarah welcomes Ivo Siebers, Sr. Vice President of Global Logistics at TKElevator, to discuss the company’s journey to removing as much uncertainty as possible from its spare parts operations and the impressive results that they’ve achieved.

Sarah Nicastro: Welcome to the Future of Field Service podcast, I'm your host, Sarah Nicastro. Today we are going to be talking to Ivo Siebers, who is the Senior Vice President of Global Logistics at TKElevator, about the company's path to predictive logistics. Ivo is the senior vice president of Global Logistics at TKElevator, and he and I met at Field Service Europe late last year and had a conversation about the journey the company is on to really modernize and transform its logistics operation, and he is here today to share some of that insight and perspective with you all. So Ivo, thank you so much for being here.

Ivo Siebers: Thank you.

Sarah Nicastro: So before we get into the predictive logistics topic, can you just tell our listeners a little bit about yourself, your role, and maybe just give a brief overview of TK Elevator in case anyone isn't familiar?

Ivo Siebers: Okay. Thank you Sarah. Let me start to introduce myself. I'm 30 years in construction supply industry. I worked in R&D in product management, product line management, branch manager. I was service manager so I occupied a lot of different position in this area. Since more than 20 years, I'm IN the elevator industry and since 13 years with TKElevator, formerly known as ThyssenKrupp Elevator.

So TKElevator is an elevator and escalator company. We are producing elevators and escalators and we also offer after-sales services. My current position within TKE is I'm head of global logistics and this means I'm responsible for the service supply chain or to be a little bit more concrete, the spare parts business around the world.

Sarah Nicastro: Which you described to me when we spoke is, the overall goal of the journey is really taking something very unpredictable and making it predictable. So when we think about logistics and spare parts and supply chain, particularly over the last few years, right, it's been very unpredictable. So to really accomplish that objective, to put as much predictability into this ecosystem as possible, what does that mean in terms of some of the ways that TKE needs to modernize? So what is the importance of embarking on this journey and looking to reduce as much as you can of that unpredictability?

Ivo Siebers: Well, that's a very broad question. So my understanding of our business and here I'm concentrating mainly on the after-sales part, which is about 50% of our revenue stream, we have a common interest with our customer, which is we want to avoid breakdowns or down times of the equipment that are under our maintenance. And everything unpredicted means there is a breakdown, there is something where we have to react instead of act and that has the potential of increasing the number of breakdowns but also the time that the breakdown takes to be fixed.

I think it's in the utmost interest of everybody that we reduce that to the minimum. To doing so, we must understand better the systematics patterns, how it occurs, and that's exactly what we try to do. We try to better understand our portfolio, we try to understand better each and every type of equipment that is under maintenance with us and try to use this knowledge, this big data, to make predictions in order to avoid that.

Sarah Nicastro: Okay, that makes sense. What it makes me think about, Ivo, is when we think about the overall landscape of logistics and particularly spare parts, inventory and things like that, over the last couple of years there's been a ton of headlines about the volatility and having trouble getting inventory in certain places or having delays in getting things that you need, et cetera.

What you are saying, what it makes me think about is any company cannot remove all unpredictability, but you can focus on, internally. So this is kind of what I think you're saying, is there might be things outside of TKE that you can't control, but the focus is to make sure that your own operations, you are looking at how to remove as much of that unpredictability as possible. Does that make sense?

Ivo Siebers: It makes sense, but I would phrase it a little bit broader, and we had the experience of the last two and a half, three years with a lot of interruptions of the supply chains globally. And it's more, as you know, what you will need in the future that better you can tackle that by building up stocks upfront by being the first of ordering critical stocks and by rebalancing the stocks around your own network where it's really needed.

Sarah Nicastro: So basically, having visibility into what you have currently, having the intelligence based on that visibility into what you will need allows you to do more forward-thinking, forecasting and planning so that you get ahead of some of the external challenges. Is that what you're saying?

Ivo Siebers: Exactly. What I'm saying is I think when we are talking about prediction, we are usually talking about digitization, about condition monitoring, so trying to figure out when which equipment breaks and try to avoid that. What we are doing, it's a little bit the opposite. What we are doing is we are using all the data points that we have anyway, so we are using the data points of the consumption of material, of the equipment in order to predict for the future what is needed and put it into the cars of the technicians upfront and direct them also beforehand to take a closer look into certain components before they break.

Sarah Nicastro: Yeah, okay, that makes sense. So let's kind of rewind a step and talk about the environment that you've moved away from. So if we think about what the before looked like versus the ideal state that you're working toward, what were some of the biggest challenges, and I guess the indicators that you needed to modernize and take a different approach?

Ivo Siebers: I think before we can discuss that, I have to give you a short journey how the typical workplace of the technician with TKE, a service technician of TKE, looks like. So usually, each technician maintains the number of equipment over a longer period of time, so we are talking about years. TKE is a multi-brand service company, so we are servicing not only the equipment that we produce ourselves, but also equipment from our competition. So we are maintaining each and every brand, which makes it even more complex.

But in essence, the technician has a number of equipment that he maintains that he's responsible for. Most of the technicians go to the sites by car when they're doing maintenance and they try to fix everything that they can during their regular maintenance visits. Nevertheless, there are incidents, there are call-outs where the system breaks down. And when he goes there, often he can only fix it with the help of exchanging spare parts. That makes a huge difference, whether his car is equipped already with the right equipment, or whether he has to order it and come back to fix it.

So typically, how the process works is if the technician sees that something is broken or will break, he will try to identify the part and get the part ordered and collects it somewhere, and brings it back to site and fixes it. That is the normal procedure. What we are trying to imply with the system or what we try to do with our system is to bring the parts already to the technician before he needs it, so that he do not have to order it. He has it in his car already, he consumes it, and he can fix the elevator in one stop.

Sarah Nicastro: Okay. So this is a journey that you are still in the midst of. Can you talk a little bit about where you've gotten to today and then what the complete ideal state looks like, so where you're ultimately heading?

Ivo Siebers: When you look at TKE, TKE is built up of a lot of different country organizations with different setups, and what we have done is we defined target states that fits for all that can be applied to each and every country organizations of ourselves. But obviously, we couldn't roll it out in one big leap, but we tested and piloted it in different countries and we are now in the rollout state, country by country.

So the current status is that we have rolled it out already in some countries, and there, we have the feedback of the improvements so we can prove what you can gain from implementing it, and we're currently rolling it out further to other countries.

Sarah Nicastro: So just to understand a little bit more, if you go back to the initial process, so you mentioned that the technician would need to identify what part is needed, order the part, pick up the part somewhere and then go and make the repair. Are they determining what's needed with a visit on-site?

Ivo Siebers: That's the normal process. Yeah, it could be during a regular maintenance visit, so they do maintenance and see that a part could break and they order the part before it breaks. Or it's really a call-out where the elevator is already, where the part is already broke, where failure happens, and then he's going out not really knowing what to fix, identifies the root cause for the failure, identifies whether you need a part, identifies the part, orders the part and comes back when the part is arriving to him.

Sarah Nicastro: And in that situation, I don't know if you know this, but how often, percentage-wise, do you think they had the part needed versus needing to order it?

Ivo Siebers: Before or after?

Sarah Nicastro: Before.

Ivo Siebers: Before we started the initiative, and we call it spare part business excellence, so that's the overall name that we are running it under, we had an investigation in two bigger established service countries, country organizations. And we figured out that in the car, the technician, in 10% of our cases, the technician can find the part he needs for a fix, then he would think, "Okay, we have branch around the country, "so you would find it in the branch inventories, but that only fixed it within 20% of our cases. With the new system, we are now at 80%, so it's a magnitude.

Sarah Nicastro: Yeah, absolutely. So 80% before they had to order that part, and now 80% of the time they have the part. Okay?

Ivo Siebers: Correct.

Sarah Nicastro: But just going back to the process itself, if I remember this correctly from our initial conversation, the other change is that before, when they would order that part, they then had to pick it up from the branch location. And now, rather than going through that longer process of identifying, ordering, waiting, picking up, going back, you are looking to forecast what will be needed, have it in advance, and proactively stock into their vehicles so that they have those things that first visit. Correct?

Ivo Siebers: Correct. So let me explain the entire process, how it's designed. So we really tried to get a seamless digital solution end-to-end, from the technician to the technician. It starts with a technician who is onsite, who needs a part. He has a catalog, a digital catalog in which he can identify the parts. That's extremely important for us because we have a variety of approximately 100,000 different parts, or per country organization maybe 20 or 30,000 different parts that might be broken.

So it's important to give him a good tool in the sense that he can do an accurate identification, meaning he can identify the article number. With that, he can then set a request to a central warehouse which fulfills the request. And instead of sending it to the branch as formerly done, they would send it directly to him. That might be a pick point, that might be the car trunk, that might be a PUDO, that might be a location close to his workplace. Wherever it suits, it'll be sent as close as possible to him in order to save his travel time.

Further on, we are using the data of consumption of his route and also routes around in order to understand which equipment has which need at what time for spare parts, and try to configure his car stock in the way that it fits best to his specific route, so to the elevators that he maintains regularly. That is a kick that allows us to go away from this one-to-one order and fulfillment to replenishment, replenishing of his car and then reduce the orders that he has to place to a minimum.

Sarah Nicastro: That makes sense. So going back to thinking about the progress that you've made and the benefits of doing this from the before to today. So just to clarify for listeners, we said that before 10% of the time he had the part needed another 10% of the time, so 20% of the time he could find the part nearby and get it from one of the local branches, 80% of the time he had to order, wait, pick up and go back. Okay?

Now in the updated system, 80% of the time he already has the part he needs, and the other 20% is fulfilling that request. Let me also ask though, with the change in how those orders are fulfilled, how much faster does the technician get the part needed when he does need to place an order? So I'm just thinking about the difference between ordering, it goes to the branch, picking up, versus it going directly to the technician.

Ivo Siebers: Yes, it's a full digital process. Each request that the technician sets to the central warehouse is immediately in the warehouse, and there's a cutoff time in the afternoon until if he places the request until then, he will have it in his car or at his pick-up point early in the morning, before he starts, so that's what we are fulfilling today.

Sarah Nicastro: Okay.

Ivo Siebers: Obviously, there are exceptions of where you need the part immediately, let's say an elevator who is going directly into an emergency room in a hospital. There, we would find a solution to bring it directly there and install it during nighttime, if necessary, but that are really exceptions.

Sarah Nicastro: So it's less than 24 hours from the time that the technician submits the request until they have the part in their hands, for the most part.

Ivo Siebers: Actually, we designed three types of service level and the technician can choose from them. One is next morning, one is immediately, and one is planned, then he gets the part when he wants the part to have, because it doesn't make sense to bring out the parts to him if he needs it only next month.

Sarah Nicastro: Right, okay. That makes sense. So the other question, I'm just thinking about how to phrase this in a way that is clear, so bear with me. The other question I have is thinking about the predictability of parts, and so how much you've been able to evolve to using all of the data that you have access to on usage, typical product-related insights on the average duration of how long a certain part lasts, et cetera, plus the technician's individual portfolio of what they're servicing.

How much have you been able to move to this world of looking ahead and planning in advance? So what I'm thinking is yes, you still have this 20% of time that they're ordering a part, 80% of the time they already have it in their car. But overall, if you think about how you're getting ahead of, like we talked about at the beginning, what you need to order from different suppliers and doing that in a way that allows you to meet those SLAs, right?

I mean, the only way it's possible to meet those three SLAs you spoke about is if you are already doing a good job of stocking parts in your distribution centers, because otherwise, you would be running into situations where the next morning isn't happening because you're not doing a good job of evaluating the demand you need for parts to have them already available in the distribution centers.

So how has that process changed in terms of the forecasting and planning so that you are kind of getting ahead of the curve to make sure you're doing a good job of stocking things at these distribution centers to make this possible on the sort of last mile?

Ivo Siebers: That's actually a very good question. Actually, we learned ourselves a lot during this process, during our first piloting phase. And one of the things that we learned was that all the biases that we had about our business being already quite okay or quite optimized were totally wrong. So as I said, 10% of everybody, the former procedure was that the technician together with his field supervisor determined what parts he puts into his car. Everybody was thinking as they are so close to the customer, so close to the equipment, that they know what they're doing. But they are no specialists and they do seldom really put a lot of time into it. So this bias, we broke through, and it actually showed how seldom really they got a hit.

The second bias is that you need more stocks in order to increase your service level and what we are seeing now is that we have already reduced at the warehouse 30% of our Stocks. And if we can trust the predictions, we can go another 50% down with higher SLAs than before. So there's the takeaways, there's a lot of unused stock lying around that is there because we don't know what stock we need in which location. And what we are doing, we have a quite well-working inventory planning system that is fed by different systems, our ERP system, our field operation system. It has some preconditions set in into, and this plans each and every location instantaneously. So it plans which part should be positioned best in which location, and by that, you can optimize extremely well your inventory.

Sarah Nicastro: Yeah, that's really interesting because we talk a lot about this idea that in the last, let's say, 10 years, we've talked so much about how important... Data, data, data, the importance of data. Right? But now, we talk about the fact that data in and of itself really doesn't do much, right? It's the intelligence you're able to turn it into that is impactful. And a lot of companies struggle with that, especially the more data they have, the harder it is to identify what data is useful.

And this is a really good example of taking data from, like you said, different systems, consolidating it, and gleaning that insight from it that is helping you make a significant impact on the business, both in fulfilling those spare part SLAs for the technicians, which is helping them to improve first-time fix and mean time to repair, I mean I have to assume.

And then also, reducing the inventory stock that you have while improving the inventory availability that the technicians have of what they need, when they need it. So it's a really good case study in terms of taking a bunch of data that's being gathered from all of these different places, and making a really good use of that data converting it into intelligence that's helping you make really specific positive impact on the business.

Ivo Siebers: Let me answer with my takeaways from my experiences here. I think we have a lot of data already in our companies and we talk about data, not information. So data in the company are of different qualities, that was also something we learned. And I think you can't make compromises in the quality of the data, that is already one of the big hurdles in order to realize something as we have done it.

So you have to be extremely strict, garbage in, garbage out. You have to be strict at the point of entrance that you get correct data in the system. And think about, we have 25,000 technicians, 25,000 technicians have to be disciplined, trained to do it correctly. It's not an easy task, it's really not an easy task. They have never done it, they are not IT specialists, but you rely on the quality of their input. And that makes it so difficult to implement.

Yes, I think we have a lot of data but of different qualities. So first you must ask yourself, "Is what I'm seeing really what is a representation of the reality, or is this corrupt?" And then you have to clean it up. A big hurdle, really one of the very big hurdles. Then the second thing is you have to make sure that if you design an end-to-end process, you must be aware that you are crossing border lines between different functional silos.

And usually, at the end of each silo, things fall down and at the beginning of the next silo, they are picked up and worked on again. And that, you have to avoid. So you really have to design a process that seamlessly works end-to-end, and where everybody relies on the quality of the work that has been done before. And this is also not in our genes, so this trust is also not in our genes. So it's a huge transformation process to design such a system in an existing elevator company.

Sarah Nicastro: Those are excellent points, and I think that the point about data cannot be overemphasized. I think that that's something that a lot of organizations, when they embark on a journey like this, they don't account for enough time, they don't understand what the work might look like. To your point, when you talked about the biases earlier, they may overestimate the quality of the data they have and then not be prepared to face the reality of what it actually is. And so then, companies can get really frustrated but there's no way to avoid that bulk of work if the goal is to achieve these outcomes.

And I think the point you made about this end-to-end process is something that in service is increasingly important, because we see more and more sort of focus on the overall customer experience or customer journey, which means that internally we have to look at more of those end-to-end systems and processes. We can't be good in one silo, poor in another, and expect the overall experience to be a positive one. And so this idea of going through that change, of those functions really working together and being able to depend on one another is a really important point as well.

Ivo Siebers: And it also, I think it's as a side effect that we see, you not only increase the competency on all levels about digitalization or you make it to something that they start believing in, but also it's something about the self-esteem going into the next project. If you have such an experience done already, you are open for the next one.

Sarah Nicastro: Yeah, absolutely. There was a podcast that I had early on when we started doing these, and it was a gentleman named Greg Lush. And he spoke about this concept of digital reputation and this idea of how important that digital reputation is when you have any sort of digital transformation initiative within the business. When you don't handle it well, the ripple effect that causes in the distrust your employees have of your ability to execute change makes everyone after that much harder.

And on the flip side, when you do this hard work that you're speaking of, you really confront those biases, you do the hard work of the data integrity and getting everything the way it should be, you really work on breaking down those silos and getting everyone to work together, the outcome of that is not only visible in the amazing results you've achieved of really flipping from 80% unavailability to 80% availability, but also in how your employees feel about your ability as a company to introduce tools and change that actually help them. Because to your point, when it's time for the next thing, they have a trust and a belief in what TKE can do that makes them a lot more open minded, and makes that next change just a little bit easier to accomplish.

Ivo Siebers: That's another point that I think is important. When we talk about digitalization, often it's connected to job reduction, cutting down costs and so on. And I would like to give it a little bit different perspective. So as you see with the examples that I made, it's not really, we are still growing, so what we achieve with the efficiency gains we are putting into more workload for the people. So we use the time with the same people, we are not reducing headcounts there.

In addition, I think we should have to look a little bit ahead we have some baby boomer generation which goes out of business during the next years. Everybody is talking about lack of talent, especially for the technical jobs. Here's a possibility, digitalization gives us a possibility not only to fill in the gap, but also, and we have to do it now, to codify the knowledge of the people who are going. And I think that are also two aspects that we have to think when we talk about digitalization that are important.

Sarah Nicastro: Absolutely. Yeah, I think that's a great point. It's about working smarter, right? I think with the point you made about the talent gap and all of the struggles, companies across the world are having to bring people into these roles at the pace they need to. Technicians should not be concerned for their jobs, that's not what this is about. It's about efficiency for the sake of not wasting people's time and not just burning through money just unnecessarily and not delivering the customer experience that you are quite capable of delivering if you just use these tools.

That's what it's about, it's not about getting rid of anyone. It's just about working smarter and allowing you then to serve more need with either the same amount of people or being able to grow and expand with the same amount of people. So I think there's this unnecessary fear of, "Automation is here to take my job." It's an often an outdated perception. I think it's really, there's plenty of work to go around, it's just about doing it in a way that is smart and allows them to shine in a way.

Instead of showing up and assessing the situation and saying, "Okay, customer, now I need to order this part, wait for it to come in, pick it up, I'll be back in however long," they're showing up, and 80% of the time they're getting the job done and they're fixing what needs to be fixed and they're achieving that higher customer satisfaction. I mean, that has to make them feel better about what they're doing day to day. So there's a lot of really positive points that come out of this for the technician itself as well.

Sorry, I was just going to say one last thing, which is I think when digital transformation is done well, what's really interesting is it's mutually beneficial to the customer, the company, and the frontline worker. There's things to gain for all of those stakeholders. It's just a matter of making sure you're looking at that change from the context of how it benefits all parties, not just one party.

Ivo Siebers: I just wanted to expand and give an example for something which hit us unexpectedly. When we first discussed this quite broad new concept, there were a lot of discussions about, "Oh, we are giving the technician quite a bit of authority. He's ordering the parts, he's warehousing, in a way, his car, he's consuming the parts, he's receiving the parts and all these data points he has to do accurately. So we are expanding his area of responsibility quite a bit." And we thought, "Okay, then we go into discussions about increasing salary. Will he ask for more? Will he accept this responsibility? Will he fill his car unnecessarily with material because he can now?" That was one of the questions.

Another question was if we are cutting out the branch out of the entire process, because it's an end-to-end process between the central organization that warehouses the parts and the technician and nobody is anymore in between. We thought that the field supervisor who formerly did all this work would protest because they can't see any more really what his technician is doing when he's doing repairs. And we did a pilot in Brazil. Brazil is a big and very experienced service organization for TKE, and we did a pilot there. And guess what? It was exactly the opposite.

So we started the program and I talked myself with quite a few of the technicians and they said, "Great that somebody is doing it. We always have to telephone for the parts, we're in the front of the customer and we have to rectify this part not coming. Now we are in the driver's seat and we know when the part is coming, we can directly give the information to the customer." So they feel really empowered and they took it really positive. And from the field supervisor side, also the opposite, they said, "Nobody likes this task, so taking it away from us, it's great." So sometimes you are a victim to your own biases, when you discuss things.

Sarah Nicastro: Yeah, that's a great point. And I think you want that frontline worker to be someone who is capable of portraying your brand really well with customers. And if you just think about, as human beings, are they better equipped to do that when they're this resource that the company feels it needs to micromanage, it always has to be watching, it's questioning what they do, versus this resource that's empowered and trusted?

I'm sure there's always anomalies. Maybe you'll come into a situation where there is this one individual employee that doesn't handle that responsibility well, but generally speaking, I think that employee feels more valued and more appreciated because they are in the driver's seat and that is reflected in how they interact with customers then. I mean, there's just a real difference of someone, I think the energy they have showing up to a customer site when they have this feeling of being trusted and being empowered. I just think that's a different energy that they'll give off to the customers they're interacting with. So I think that's a really good point, a lot of good lessons learned.

Ivo Siebers: Actually, want to add, where we put a lot of emphasis on and put also a lot of time into was to think about each and every stakeholder. So first of all, the technician, if he don't likes it, he will give corrupt data. You can't run such a system without the help of the technician and full acceptance of him. So what really is in for him?

So really sitting down, building a pitch for him, really trying to address all the uncertainties, but also what is in for him, and really get a good pitch together for him and for the branch, for the field supervisor, for the warehouse. So for all different stakeholders to understand really what drives them and what can you achieve for them. This is really an important step that we took and it proved to be extremely important,

Sarah Nicastro: I think it's such an important step, because a lot of times, companies either, they go out communicating the benefit of the change to the company, which some people are going to care about but a lot aren't. And then other times, they come up with this generic why, right? Something that's just, "Well, you should care because X," without really, what you're talking about is personalizing that change management message for every role that the solution will impact.

And that's when taking the time to put yourself in their shoes and think about what does it really mean for them, I think is such an important aspect of managing change well. Because number one, it shows them you took the time to understand them as an individual function instead of just throwing out this, "Here's why we're doing this and you should comply." I always say there's a difference between commitment and compliance, and that difference a lot of times is how well you manage that change.

You don't want employees that are just complying, because at some point, the pressure will be off to comply and then they might not care if they put in the data the right way or they do the steps they need to do. You want them to be committed to the new process because they see the value in it. And then, that's when you have technicians that are always following what they need to do or using the tool the way it's intended to be used because they want to. That should be the goal, not just force or getting them to do what you're saying to do just because, so I think that's a really good point.

Ivo Siebers: And I think you also have to, during the implementation process and afterwards, you really have to give them a word. So you have to go there and really ask them honestly about the experience and take it with you. You might not change everything that they want to be changed, but at least you have to take it seriously and answer it. And the good thing with technicians is, at least with the technicians I met during that, is that they usually are quite honest. They are seeing everything from their own perspective, that's clear, but usually, once you have some warm-up behind you, then you get quite qualified answers to questions.

Sarah Nicastro: And if you're open to really listening in that implementation phase, you may pick up on some points that make a significant difference in how the solution is adopted in the wider rollout. Right? I mean, to your point, not every piece of feedback will be relevant or addressable, but there may be some that if you are listening or points that you hadn't considered or will make the end product a lot better.

This is, again, where I think some of the aspects of change management that are tough are coming back to, "Are you willing to put the time in, to personalize the message, to actually listen?" Employees are smart enough to know, "Are you asking me my feedback just because you're trying to pacify me and make me feel like you care enough to listen, or are you asking me because you genuinely care and if I have something important to say, you may actually take action on it?" I think people can tell the difference, and if you are willing to really listen, there could be some very helpful things that come out of that, so that's a good point.

So Ivo, last question is really just around, you've made significant progress here, but if you think about the future, I'm talking three to five years from now, something like that, and you think about what else may change in the logistics landscape, what do you see coming along? What further refinements or changes, what do you think will be sort of the next version of the ideal state that you're working toward?

Ivo Siebers: I think in the nearer future we are planning to do something quite obvious, actually, we are working already on it. So the system that we are using at the moment is working with data from the past, so we are using consumption data plus something else, mix it together, do some big data analytics and then come out with a prediction. But in essence, we are looking back to predict the future.

The next step will be, you might have heard about our condition monitoring system, we call it Max, and Max is throwing out condition monitoring data and we are working on algorithms that give us hints of future events, future failures. And obviously, that would be helpful for us to integrate that also into our prediction methods, so that we not only look to the past but also into the future where the condition monitoring, that's, I think, the nearer future that we are working on at the moment for the concept.

Obviously, as byproduct of everything we are doing, we get a lot of data and a lot of insights about equipment, about failure, pattern of equipment. And when you think that further ahead, it'll give you also very good ideas for the route planning, for time, assumptions for certain tasks. You might vary your work plan depending on the necessities of the equipment because of your experiences on that, you might have different contract models for different equipment. I can see a lot of use cases for those data.

What you also can do is you can use the data in order to re-engineer or synthesize some sort of service problem. So as you know what was consumed, you could also take a bunch of the consumed parts and say, "Okay, this is the equipment I'm maintaining," without really looking into it. This is also important for old equipment, where you might not have the data available. So there are a lot of things that I can see in the future, without talking about 3D printing and about picture recognition and about artificial realities and so on.

I think one point I see definitely for the future too is, as I said, we are looking into the future where talents are scarce and where a lot of knowledge get lost, where a huge amount of people will go out of business. So I think we have to find ways now to conserve this knowledge, and digitalization, digital tools, virtual reality gives us also a huge opportunity to do so and we should start using it.

Sarah Nicastro: Yeah, very good points. Well, I mean, kudos to you for everything you've accomplished so far. It's really impressive to hear how far you've come from the before to today, and then to also think about some of the ways you can continue to refine what you're doing to make further improvements, so I really appreciate you coming and sharing and it was a great conversation.

Ivo Siebers: Thank you for having me.

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