Hexagon AB (publ) (STO:HEXA.B)

CMD 2019

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Dec 11, 2019

So very welcome to Cobham and this is actually a bit of a test for us because we've renovated these premises over the summer. So it's the first big event we host here, but this is supposed to be a customer interaction facility. Today, we're going to talk about empowering an autonomous future because that is what Hexagon is all about, and that's what we've built this company to do. And if we look at a brief history of Hexagon, you could say the 1st 10 years to be fair was really about collecting technologies. And we learned more and more how they interacted and how software interacts with hardware sensors and basically creating 3 d images of the real world. But around 10 years ago, we started to realize that you do something with all this data that we collect, and you could put it to work to create a better world, more efficient world. And we started focusing on customer centric solutions, I. E, targeting an industry and solve real workflow problems or maintenance problems for that industry. And it's really about now we can see this taking off. So changing the world or changing technology is not a fast process. It's taken 20 years to get to this position. And I think now over the next 10 years, we are ready to harvest what we've planted. But it's a small revolution autonomy. You've heard automation over several years, and automation is about industrial robots, things that go into autopilot and so on, but it still requires human intervention. When we discuss autonomy, we talk about systems, designs that don't need human input. And that's interesting because in certain areas, computers are actually better than humans. We have a fantastic brain, but let's face it, sometimes it goes wrong. So humans in construction are not necessarily a good thing. Sometimes we simply don't see the problems. And sometimes we got brilliant engineers. I mean we got 4,000 of them in Hexagon and they design fantastic ideas or technologies that are going to help us in our everyday life. And that might not work either. So yes, it was supposed to break, but it didn't and they did that in front of the world media. And we take traffic decisions that sometimes if you look in hindsight weren't the most clever ideas you had. We text whilst driving down the motorway and so on, and that's not good either. And sometimes we just want to get from A to B and you tried it this morning if you were on the M25. And yes, this is an increasingly efficiency problem in the large cities that we're all live in today. I mean, more than 50 percent of mankind live in cities, and this problem is going to grow exponentially. But if you do simulations, and we're pretty good at simulations, you can simulate traffic models. And if you disconnect the human interaction to traffic, you can create all sorts of traffic patterns that you can't do when humans drive the cars. So to design these autonomous systems, you need a set of core capabilities and that's what we're going to focus on today. And Hexagon's core capabilities on the road to autonomy are, for example, reality capture, design and simulation and location intelligence. So we look at them 1 by 1. I think you're going to acquaint yourself with BLK2Go and you see it here on this video. It's our latest development and we're launching it about now into the market. It's a fantastic device where the device itself keeps track of where it is, geo referencing itself, so that you can stitch together 3 d realities like this. And this is very useful if you want to design autonomous solutions. Now another core capability is positioning. Not only do you need to know what you see, you need to know where you are. That is crucial. It's crucial for traffic. It's crucial for autonomous agriculture. It's crucial for aeronautics and construction to know exactly where you are when you develop something. And positioning is one of our core capabilities. We happen to be a world leader in positioning objects. The 3rd core capability is design and simulation. Design is what we call CAD, computer aided design, using powerful computers to make designs, digital designs. Simulation is all about simulating sequences, events, doing shock absorption and things like that when you design new products. We simply don't have the time to do it in the real world anymore. So we need to simulate events in the digital world to develop autonomous systems. Another key core capability is location intelligence. What's going on in my vicinity? What do I need to keep track on? Well, first of all, you need a 3 d representation of that reality and you need to be able to move about in that 3 d representation. Then you need the ability to overlay that 3 d representation with events. It might be crime, it might be people, it might be traffic, it might be outages or whatever it is you want to follow-up. And finally, autonomous technologies themselves being able to stitch products, systems and software into a system that actually works in the real world, so that you can have an autonomous car, for example. That's one of our core capabilities as well. So these are individual technologies, individual capabilities that in their own right are fantastic, but it's really when you stitch it together using a platform that can import and export data, send commands or receive information when the real thing is starting to happen. And that happens to be Xalt in Hexagon. Xalt is our own developed IoT platform. And it's highly compatible with everything we do. It's designed to be able to take information from our sensors and send information to software and so on. And now we have an ecosystem that can create smart solutions. So where are we going to go from here? Where are our target markets? Well, our smart solution portfolios are, 1st of all, smart factories. And what we try to achieve in smart factories is, it's autonomy on the shop floor. So we've started to develop cells, production cells, where we use our coordinate measurement machines in harmony with manufacturing machines of different brands. And our software is telling that manufacturing machine, when you're out of tolerance, when you're not producing according to spec and so on. And at the end of the day, if you can do this over and over and over again, you have less man hours. Actually, you might not even need people on the shop floor when this is working. You're going to use less material because your yields are going to go up and you're going to have less waste and you're going to create a better quality, a quality according to your original specification. Now if we think beyond this cell, let's say you have 1,000 of these cells, the next product that we're going to launch is virtual assembly, where you can take a product, measure it out of 1 cell cell and then compare it to a product 100 meters away and see do they fit together. And even further beyond that vision is then we link it back to computer aided simulation and we can help developing the next generation products based on real live data feeds from our factory. And that is really what Smart Factory is about for Hexagon. We're talking about smart industrial facilities, and we're going to hear more about that today as well. It's about designing, planning, building, operating and overall maintaining an asset for over 40 years maybe. And what we want to do there is to link the real world through scans, 3 d representations of the real brownfield plant back to your original drawings, so that you can identify components and know when it's time to replace them, doing simulations, see if you can enhance your kit and so on. Another interesting vertical is Smart Mines and this is probably the area where Hexagon is most advanced when it comes to selling solutions. We're selling comprehensive solutions, helping large miners planning their operation, following up the operation with fleet management systems controlling hundreds of vehicles in the mine, helping them with safety solutions inside the mine and then surveying the mine once you've blasted. So it's a full suite of product from planning, drilling, blasting and then basically excavating, refining and so on and creating iron out of an iron ore mine. Finally, smart autonomous mobility, what is it all about? Well, it's about anything that moves and we saw in the introduction that we do need help and assistance driving vehicles. And it's about creating an ecosystem where you do need a very precise map. This map needs to be the same in all costs. You can't have specific maps for 1 brand and then another brand is using another map. It's about controlling and optimizing traffic. So you need car to car communication and you need car to infrastructure communication. And Hexagon is active in making eventually, we eventually, we might even be involved in the mapping of roads and so the things we've encountered when it comes to construction. And it's fairly simple actually. It's probably the least automated industry on the planet. So our simple idea is, if you have good digital plan on what you want to build, You should be able to share that plan with the people that are actually doing the construction. But you should also be able to follow-up and this is what never happens in the construction industry. You've heard about BIM and all these very nice pieces of software, but no one on a construction job site will ever see them. And therefore, we need to sync our sensors so that we can make a representation live on what's going on, on a construction job site and feed that back into the plan, so that you actually get to know as a project manager, what is going on in the real world. Finally, smart sites, large industrial sites, citizen nations needs to coordinate information databases. And we talk about digital cities. We are targeting Hexagon is targeting public safety, utilities and telecom operators. That's our three priorities. So what we can do is, we can provide a 3 d map of a city. We can show where the cables are underground or above ground. We can basically make an inventory of a utilities infrastructure and we can pinpoint where outages or leakage is happening. And then we can coordinate that to a planner or a dispatch authority somewhere. So what does it mean for Hexagon? Well, today we're organized in 6 divisions and over the 10 years, I would say, we've started projects, intra divisional collaboration projects. And this is now accelerating into a web of collaboration where the current divisions might not exist 10 years from now, because it's so integrated the technologies. And this is sort of a glimpse of what's going on inside Hexagon today. These are the projects that we're actually running. But we believe that once this is done and we formulated solutions and sellable products out of this web of projects, we're going to see an increased growth from these solutions because the solutions are so much more valuable for our end customers than the core capabilities, the core products that we've been selling over the past 20 years. So we believe that we have a great future and maybe it starts now and we can feel the acceleration of this uptake. Thank you all. Hi everyone. It's a pleasure to be here today this morning. I see some new faces. So my name is Claudio Simao, CTO and I'm talking today about some elements of our strategy and how the outcomes of these elements of our strategy is going to produce future proven technology advantage that bring it to us and to our customers different portfolios as Ole mentioned, our smart solutions. As CTO of Hexagon, I'm responsible for the Innovation Hub. And Innovation Hub is a central R and D that is designed to evaluate, to develop and to transfer technology to the divisions and addressing very well targeted innovations in upcoming products. Talking about these key elements of our strategy, I will try, I will do my best to avoid technology parlance and technicities. But if there is any doubt, we can talk afterwards. So as you probably know, to develop all these transformational smart solutions, as Ola mentioned, we have a strong R and D that commits resources and investments. And our focus in the central R and D is basically to your right, is basically on not only legacy technology like scanning, laser, optomechatronics or photonics, but also in the modern enabling technology or digital technology less for example edge computing or cloud and mobile techniques, artificial intelligence model and algorithms for artificial intelligence and so on. We count with a strong groups of experts with skilled in specific technology that can place out across excellent divisions and we work with excellent divisions to generate know how and IP with proven track record on groundbreaking initiatives. So we have a long history starting from our origins of this group in Leica Geosystems in Herr Brook. But now we have teams across the globe in many areas. Another interesting thing to notice is our the way we leverage synergies. As Ulla mentioned, we are now in a phase that divisions future, but we have these verticals, applications that are our SmartX. But we have many initiatives at the center of R and D, for example, the expert groups. So we have experts in specific technologies in different divisions. They come together. They analyze commonalities. They analyze new products that they can develop together, sometimes optimizing, avoiding duplication and so on and so forth. Technology harmonization, we have a very strong group for harmonization. That means how we can design our architectures in a more user friendly way to our divisions or using, for example, common libraries and common procedures, common tools and so on and so forth. So a lot of cross divisional, inter divisional initiatives. But coming to the topic itself, so everybody knows that we are in an extremely fast changing technology world, very overcrowded with technology blocks, technology elements, different narratives that's confusing sometimes. So in this situation, it's very important to have clarity to understanding to understand the tech environment, to keep up with new technologies and to separate the trios and buses from the real stuff, the things that we can really leverage. In this context, I would like to bring to attention a major element of differentiation of Hexagon. That is the tight integration between harder and softer components and the data flows among them. This is in fact a major hurdle for a scale for scaling up of IoT solutions, for example. Nowadays, I guess everybody is reading the tech news. So we see how difficult companies are facing to develop with plans on IoT, vertical IoT applications or advanced edge computing or AI projects. And this is all about data as we will see in and the reason is simple. The order is getting complex. The solutions in fact, the order is complex. We have to embrace complexity to deliver an abstract complexity for our customers. So the solutions are present in the real world are getting more and more complex. So we need to prepare our solutions, the design of our solutions for sustainable scalability and to be effectively future proven. So and if we don't do this now, we will hit the wall in the future. And this is the big hurdle for IoT and AI at the moment. If you look to our capabilities, right, our core capabilities, as Ulla describes. We have seen lately a lot of cool and powerful innovative technologies that are applied to our products and solutions. We will see Ola shows some of them. You'll see in the next presentations more of them. But today my focus will be much more in the underlying underpinning technologies, the so called unsung heroes, the hidden stuff that make our solutions nice, very easy to use. And this is all about seamless integration, efficiency and user friendliness with good user interface and user experience. So our enablers, our digital enablers together with our connectors represented by Exalt, that's our IoT platform, our digital enabling framework can deliver solutions that are differentiated, but not only for today, prepared for because the stress is coming with all as I will mention in a minute. So the fact is that if the architecture is not designed for a smooth interoperability of components both harder and softer that then being very tightly integrated, we will have a problem. Why? Because digital evolution is making systems more complex. So and this in the professional market, this will bring the need of high functional integrity and ultra reliability of the systems. And this will be tremendously stressed in the close future because the humongous, the enormous volume of data that are playing out, the more we connect it, the more we have flows of data. The ultra low latencies that are coming to place when we have 4 gs LTE or 5 gs coming and other technologies for more connectivity and fast connectivity. And last but not least, the massive machine to machine communication, distributed architectures, machines talking to machines and learning in a mesh, not in a central repository. For sure, cloud is important, but this trend to distribute detectors tethers is enormous. So if you look to ultra reliability, very, very low latency, fast real time and volume of data, we can think about autonomous processes in general, autonomous cars, traffic management systems. You cannot wait 1 minute more, right? You cannot have a flaw. So systems that are have to be really, really reliable and intrinsically or functionally integral. Drones and robots, smart grid, air, augmented reality, virtual reality applications and so on. When we think in distributed architectures and massive machine to machine communication, it's all about industrial automation, smart factories, smart cities, plants, construction. So we have many flavors of these trends, let's say. So Hexagon's core capability in sensor, software and autonomous give us a unique point of leverage that is the convergence between the physical world and the virtual world. So this is a very strong footprint of Hexagon. So when we think about the convergence, it's all about smart digital realities. That means a digital replica, complete digital replica of a systems that can be represented essentially high level by the digital twin objects and digital twin of workflows, functional digital twins that are interacting in real time or in some cases in a static model. But if you look a little bit deeper and you look to our SmartX solutions, it's all about data. It's how we generate data, how we curate data, how we stream data. That means we have to capture data. We have to prepare data for streaming. We have to connect edge points or endpoints or data points. We have to feed process with data, model, simulate and feedback corrective actions to make the real world better, to optimize the real world. So it's all about how you move data in optimized way. So we can see the quantity and diversity of iterations that we have with data. And in this context come our platform and our solutions, systems architecture. That's no longer a software architecture. It's a system architecture end to end from the sensor to the endpoints that could be a feedback, could be a person visualizing thing and take decision, supporting a decision process or could be simply a feedback. So we develop Exalt a framework or a platform of digital enablers and connectors. And as I mentioned, it has a vital play allowing for allowing our solution, allowing Exxon for native integration of our core technologies and data flows, bringing together seamlessly different technologies into a robust systems or ecosystems architecture, always with a vision of sustainability and scalability of value creation. So this is totally opposite to use fragmented digital enablers or generic connectors, even APIs that would create architecture that maybe works today. But as data our volumes of data kick in that means more volumes of data, low latencies, machine to machine iterations, more fast real time processes, the systems can present flaws. I would like to make an observation now that I'm not saying that our solution architecture is a closed architecture. But the backbone of our solutions architecture is natively integrated. Why? Because it will not survive to a stress test with what is coming technology. It's like hitting a moving target, right? You have to look a little bit in front to hit the moving target. So this is how we are planning our future. So we will still we have open architectures. Most of Exelon's platforms are have our open architecture, natively integrated in the backbone but open for accessory or supporting processes or components. But if we look in the market, if you look to to what is happening today with many companies are combining different technology, integrating midware and data platforms using patchwork at digital enablers or generic APIs. So we don't know our connector or generic connector. So we don't know the quality of code. You don't know the road map. You don't know the lifespan. Maybe different road maps are prepared for future technology or not. So this is one key element of what we are doing for solutions architecture. So we are creating solutions architectures that are stable, that will not crash, that will not present hidden limitations when dynamic flows scale with data and incremental connectivity. So the next step then in our smart solutions is this one. So close integration of building blocks. And why we can do this? Because Exagon has extensive portfolio of sensors, technology, domain expertise and horizontal end to end enterprise software platforms. So it's a big differentiation where we can put together using our XL platforms and order to be frank, order enablers. We have other digital platform, the digital reality platform is another one, the smart map could be considered another important component for putting together smart solutions. So our multilayered data information, let's say, enabler called Exalt permit us a native integration that is a major differentiation. So let's see some of the aspects of the outcomes of this strategy. The first one would be basically balance of data, balance of load balance of data. So if you have a modern ecosystem architecture, consider the increasing connectivity, data volume, real time application and so on, is fundamental to balance data and the data loads. And when I say balance, I mean in processing data, in storing data and in streaming data. And all of this dynamically in all instances can be at the edge, can be at the network, can be in the load balance is fundamental. And the load balance is fundamental. The correct balance of data and orchestration I don't want again to go to technology parlance but the orchestration how you sync the data and how you choreograph different end to end points It's fundamental. And if you don't have this, even if the solution is stable today, maybe with 2 updates, it will be no longer stable. Controlling the integrating natively, controlling the enablers and the connectors plays, as I said, a vital function on that. So not doing this can alter profoundly the solution's efficiency and effectiveness. So a simple example, a very straightforward example is a control room. So today we have a lot of cameras streaming all data to a command center. Sometimes we have some supporting functions at the command center, but this is not really effective. And if you scale up, we will find a limitation for that. So it's much more sensible to mine the data, to extract what is important, what is relevant at the edge, at the camera, at the sensor. Could be a simple camera, even if multispectral or could be a more elaborate sensor like our BLK-two forty seven that scans, that collect data with much more accuracy, much more information. If we extract the anomalies or patterns at the edge and then stream only what is important to the command center, we eliminate a lot of traffic. We make the solution much more efficient. We could even have at the edge some algorithms, learning algorithms that will take action, send a notification or send dispatch a guard or a policeman to take to change something. So for example, somebody left a backpack in Waterloo and somebody has to be dispatched to see if this is something that should be avoided Or somebody moved into a geo fence into an area that's not permitted, so some action should be done. This could be even a situation or when a machine is located in an area that could cause an accident. So you have to you could automatically, autonomously dispatch somebody to move this. This could be even more critical. If we have a highway and there is an accident, you have cameras, you could close the highway autonomously or you could send notification, but this could be even made autonomously. So this is the trend of world. Decisions we take at the edge and even actions we will take at the edge. A second important aspect related to data is, in fact, twofolded. The first is discoverability or the ability to discover the data sources, being the data sources dynamic feeds or historians. And the second flavor, let's say, of this aspect is related to the feasibility of multi source data fusion, how you fuse data. So let's go to the first one, how you find data and why use it. In manufacturing for example there is a report from HB Harvard Business Review HBR from maybe 2 months ago or 3 months ago showing that in manufacturing sites, we use less than 50% of the structured data and less than 1% of the unstructured data. So that and we are talking about manufacturing site that's much more well behaved, let's say, it's much more contained. If you go to more open environment like agriculture, construction or mining, this should be even less, right? And why people don't use data? Because they cannot find the data. They cannot make sense of the data. Data is not contextualized for the application. There is no correlation in place. Or if there is some structural data, you don't have easy access in a IT environment. So a solution that is natively integrated where you know all pieces, bits and bytes of code, you can instruct a very easier way to fetch data to do queries in a contextualized, in a correlated way. The second aspect that is the feasibility to fuse data is very simple also to infer. Think about how many different sensors we have today. We have many different workflows. We have many different data forms and data types. And to make sense of this multi flavored data, we have to fuse them. You have to combine them and to do correlations. A good example is in our digital reality platform. So we combine data from different sensors, someone some of them airborne, others are manual transported as BOK RealityCapture, for example, or to go or they can beat mobile mapping. So we have different sensors, different workflows, sometimes different data formats. But because we have a very well integrated architecture, we can combine the data in one what we call super mesh. That's a very, very precise representation of reality. And we can, for example, visualize as one component, one single representation of reality. We can extract portions for licensing. We can apply to real time applications and so on and so forth. This is a super mesh of Paris just to show how nicely it's possible to fuse data. I don't go to the end of this video, but you can even navigate inside the buildings or you can go underground. So is that, let's say, a fusion of multi sensor architecture that is very effective. Let's think about if you have a platform with a lot of different connectors and neighbors. A third element is the ability to update our entire solution. This is very important, the update ability, let's say. This I guess everybody face when we in the consumer market. In fact, the professional market is looking a lot to consumer market, but in the consumer market you don't need high integrity. If something fails it's not so dramatic as in a factory or in a plant or in a safe environment like public safety and so on. But anyway everybody face these. You have to connect device or you update a device and it loses connectivity. So the possibility to update natively is fundamental. And the simple reason is because technology is changing very fast. So we have to update all the time. So a good example in the consumer markets is I guess everybody probably set up already some cameras at home and we see the cameras lose connectivity or your network lose connectivity simply because somebody updated the system. So this is a very, very simple example, but shows how sensitive is the updatability in systems, in architectures. So let alone in professional markets when you have hundreds of endpoints and etcetera. And for sure this solution here don't need to be ultra safe or present high integrity. But if you are driving a car and your sensor don't realize that there is somebody crossing the street in front of the car, the consequences are different. That means in professional markets, the consequences are pretty different. This is why we need this high integrity, ultra reliability are fundamental in many examples. Last but not least is in fact our holy grail or our vision. We do believe in Hexagon that the next super cycle in technology is autonomy. So autonomy is coming to optimize the process and to use less and make more, let's say, to simplify. And to have really autonomous systems is fundamental that the architecture to be integrated. That means autonomous systems have to generate optimum output for fast conversions. An adaptive algorithm, an algorithm that adapted the system, consider the last cycle to produce the next cycle. That means if the architecture produce any flaw because update or because some change, this flaw will be considered to project in that next cycle in adaptive, in AI baked learning system. So the systems can get unstable and crash, sometimes not in the first moment, as I said. So it's very, very important to have a solid architected solution. And this is how we are doing. Last but not least, this is my last slide. I would like to show one example. There are many examples in that you will see today about applications, how our smart solutions have elements of our natively integrated architecture. But I would like here to bring the open environment application like cities, mining, transportation or agriculture. In open environment applications, geo referenced workflows are fundamental. For example, in smart cities where Hexagon has a vast has a very comprehensive portfolio from the 3 d representation of a city to real time geo data and location intelligence system as in smart map, 3 d representation as smart digital reality platform. Smart Map 5 d is our product and LUCIA platform for geo data real time geo data location. For dispatching or managing workflows in a city, we have our smart command center on call launched in our conference in Vegas this year. So by integrating all these different geohive for instance work process, fusing different data and types and formats that we have in this situation, we can contextualize different semantic scenario, We can apply other digital enablers to produce a very effective solution. And when we design solutions, natively integrating them, we can bring augmented situational awareness and collaboration of the different bits and bytes. And this can be scaled up when incidents turns into major events. The same we could say about other applications, but in smart cities, it's very easy to see, for example, AI baked dispatching systems where the system can collect information, structure information from police, fire and ambulance, for example, or from social media and could make sense of all this data and autonomously is where you have an event, for example, let's say there is a toxic spill or leakage and you have you need different first responders to face that specific problem. So in a matrix of competence, it's very difficult in a command center for you to select everything. So AI baked system could notify and bring together, group together the first responders to face that specific situation. So all our smart if you look to our smart X solution or smart solutions, you can see that we have rich data environments that could be leveraged if we have a solid or robust architecture, if we have enablers that will scale up, that will be updatable in an easy fashion. So with this, I would like to say that the opportunity in front of us is real. So we can create scalable, sustainable solutions, our Smart X. We have all the technologies bits and bytes, the technology components and domain knowledge for our digitalization capabilities. So converting the digital and real world and physical world composing data using our software and hardware is really, really a major opportunity in front of us. So thank you very much. Good morning, everyone. I would like to take the opportunity to talk to you about how we digitalize the infrastructure and mining industry. The Geosystems team is a leader in Geospatial Information Technologies. We provide digital solutions and services to basically empower the professionals in these industries to increase productivity and quality. That is in one €1,000,000,000 We will cross the first time the 1,300,000,000,000 €1,000,000,000 We will cross the first time the €1,300,000,000 this year. And 35% of our The markets which we are mainly exposed is roughly 70% to building and infrastructure market, roughly 15% to the mining market. And other industries is another 15% that that consists out of forensic forestry applications, media entertainment and so forth where our technology solutions are applied to. We are active in over 180 countries every year. So that shows you kind of the widespread of GEO systems. We are nearly everywhere probably on every second, third construction place where accuracy matters. You will see one of our solutions if you keep your eyes open for yellow and green systems in construction sites. The exposure in the world is a a little bit more, close to 40%, 50% in Europe, Middle East and Africa. Roughly onethree of our business is in the Americas and 20% in the Asia regions. These are the latest quarters distribution, which more or less represents the annual exposure of our business. When GEO Systems thinks about digital solutions, we think about how to make the world machine readable, machine readable and subject to analytics. This is about digitalization. If you don't digitize the world or your place where you work you cannot digitalize it, you cannot automate process. One example, imagine you digitalize your world. In this case, a city. It could be a building. It could be a mine, it could be a smart factory or smart plant or whatever. We have sensing solutions that basically create super measures, as Claudio said. It's the best digital representation of the real world, and what we need for that is high performing sensing solutions that fuse different data. In this case, our core competences is in imaging and laser technology, where we fly such cities and then have an automated workflow that basically creates these 3 d models. So there is no manual interaction anymore. So we have optimized the workflow in a way that we create affordable 3 d replicas of the world, whether it's a city, a building, a street, inside, outside and so forth. Having these digital representation of the world is not the end of the story. We call that digital reality, not virtual reality. We call it the real world in the digital world, not the virtual world. But the digital reality is not the end of the story. It only helps us if it's smart, if we connect it with information. And if we have a digital base of our world, then we basically can convert it to information. And here we experience now the adoption of artificial intelligence to create information. In terms of a city model, you could basically extract and can fully autonomously extract kind of perfed areas. In some states, no, here, this is the buildings that we can extract. We can extract kind of the biomass in the city automatically. We can extract cars and parking places on the cities. We can extract perf areas, perf areas where no water goes through the ground. In some countries you get taxed on that. We can determine the streets, the automotive industry is about that, about the assets the streets. And we can see, for example, extract automatically roofs and form the building, which might be interesting for the insurance companies. So there is and this is all through artificial intelligence, where you train certain algorithms to detect certain features in the data. So this is now applied here to our data from the content program, where we expand the portfolio of our offerings to the market. When we come to an overview what GEO systems core capabilities are, then we have a 200 years of history to create sensors. At the beginning was optical mechanical instruments. Today we have digital solutions. You could say it's a sensor, but as Claudio already mentioned, it is not a sensor by a hardware. Way. 50% of our software developer, of our R and D people are basically in software developing, because we converge the software with the sensors to put all the processes that we imagine and we can do into the sensor. So basically to make edge processing kind of a normal thing in our industry, really kind of to get the workflow to the simplicity that we need. We have also then applications or kind of digital services, digital services because we believe with the rich data sets that we create, there's another opportunity to basically participate in a sharing economy or let our customers participate in a sharing economy. Data sets that are once captured can be shared with others. Airborne image is relatively clear. You have governments that are interested. You have construction companies that are interested and so forth. So the Hexagon imagery program is running. Our correction services that we have help not only kind of surveyors to be more precise, but also help tractors in agriculture to drive straight or we have projects with our positioning intelligence group, where we support the autonomous mobility in the automotive industry. The goal is that we take these digital services and our disruptive sensing solutions to empower these industry specific solutions that we can develop in conjunctions with other divisions, whether it's the smart factory with manufacturing intelligence or it's smart plants with our PPNM group or into construction, intelligent construction where we develop smart build. We basically connect our sensing solutions with these solutions to create complete industry specific solutions. Geosystems is focusing their strategy on 5 verticals, which is mining, heavy construction and building. They're really focusing on the end user on these industry workflows and we still continue with innovating for the survey industry. Surveyors are a profession that take instruments and live from measurements and serve all the industries. And geospatial content is basically sharing data. What I would like today to focus is on the mining and the building section. Mining is probably one of the most advanced solution that we have put together with general hex technologies from different divisions. And mining is an important market because mining is resourcing our future. What would you do if you would not have your notebooks to make your notes that they are in real time out there? What would you do without the cell phones? But think about the low carbon emission world around this. How would we get the minerals to create the technology for renewable energy or to host battery to develop batteries for electronic movements. So you can think about mining the way you want, but mining is needed. The challenge in the mining industry is that these resources are scarce, right? And we need to have the utmost productivity to basically help the industry with technology to basically use less resources to get resources, to have a lower impact of the environment by basically being more efficient. And the other element in the mining is, and you see this in most mining industry is the number one priority, is about safety. There is an increasing demand for social responsibility to increase the safety not only for the employees in the mine, but also for those who live in the neighborhood. So these elements are things that we serve with our portfolio. Our portfolio that we have in mining is basically distributable 3 elements that matter for the mine. In the design area, we connect the physical world with the digital world. In the operation, we're increasing the operation, we're increasing the productivity and the safety and in the with the enterprise solutions we connect the data to create information through analytics and then help to improve all the information that we used, the information that we got through the design and through the production to improve the productivity cycle, to continuously have a continuously improvement process. Let me give you an example on this design. When you start a mine, you have boreholes. Out of these boreholes you get the samples of the minerals that are underground. With our design software, we are basically modeling the underground to know exactly where the auras are. Then when we have those informations, then we model the optimal design for the mine to have the least impact on the environment and the highest efficiency to basically get those resources out of the ground. We do this for open pit mine, but we do this also for underground mining, because underground mining becomes also more important that because some of the minerals you don't find in open pit mines and you don't need to go underground. With our design software, this is now the design of an underground mine. So it's fully in 3 d designed and this is the base for all the operations going out, where we basically use this data model to basically help the organization in operation with gun charts types of scheduling to basically exactly simulate how you basically process the mine and grow the mine in the underground, and connect that in 3 d, so that through simulation technology, you will see through simulation technology, you can exactly see in 3 d and the progress over time how the mind would be developed. So this is basically creating the connection between the real world and the digital world as a fundamental base to go into the operations. In the operations, you would have then quite a number of assets like such whole trucks, where we have fleet management solutions with the goal that these fleet management helping to schedule these whole trucks in the right sequence to use less fuel and basically be more, let's say, more careful with your trucks, don't over speed and those kind of things. Elements that the information of the trucks give us through the fleet management system. One important part is the safety element. If you go on a larger mine, then in the larger mine you would have 200 of these whole trucks. These are the yellow ones. This little one which you see to the left, that is not a small car. That's the normal you will see here on the roads. You see kind of what the size differences are. And where you have these 200 haul trucks, you have roughly 800 light vehicles as you would call it. And where you have that you have quite a high risk of accidents. What we do here with our GPS technology, with our fleet management solution and communication technology, is that we basically created a solution that basically creates a digital fence around these cars, which are seen in red, yellow and green. And whenever 2 cars are coming to close it basically sends warnings and then later we autonomously basically make vehicle intervention to pull the brake. So that means that we tremendously increase here the productivity the safety for these operations. So 2 whole trucks crash with this other, that's not kind of 10,000 damage, that's 800,000 damage. So I think that you get the dimension on it. What we also know through the statistics that 65 percent of the excellence in the mine is coming through fatigue. So if a whole truck driver goes down the mine for hours and hours in slow speed and then comes back in hours and hours and has multiple shifts, then it's not surprising that they get fatigue. What we do is that we create a software solution that basically converts the face as of the real world into the digital world and makes it subject to analytics. So we basically monitor and digitize the face and can see whether the driver is getting fatigued or is distracted. And if you detect that we basically send him a warning, it's like a vibrator on the seat, so he feels that, or a beep. In addition, what we do is we know exactly which car has fatigue events, where has it, where could be accidents there and we send that to our enterprise solution. Our enterprise solution that basically connects all this data for further analytics. One representation here in our enterprise solution is that we have a digital model, the digital reality of the mine and overlay it with the information that we gather from our radar system. So, here is now the other connectivity, so our geosystems division where we have monitoring solutions. So there's basically a radar there that monitors the entire mine and basically colors the mine in 3 d, where it is safe, where we don't have any risk for slope stabilities, kind of where the earth is moving. And if it would color in red that would be the signal that you have to evacuate people and assets out of the mine to be safe. This is one part of the data, then it's clear that we need to foster with the enterprise solutions the connectivity, not only in control rooms where management can sit and see all the data, but also outside to the people in the field to make them aware about things that need to be done. And then the third is then the data analytics, where we basically connect the data. Typically all the data which we achieve, it's kind of an IoT enabled environment with XaL technology. We basically connect here all the information here. It's a heat map from our safety solutions, where we can basically look into the data set and see where is over speeding, where is near misses of cars and then basically feed that information back into the operations to make it safer or more productive. What you see here is that Hexagon has a wide range of solutions that basically create a total vertical application for the mines. It creates kind of a certain dedicated solutions from mine plan to mine enterprise, where we have surveying equipment, we have fleet management solutions. We touch with applications out of our PPNM sister division, where we have common customers. We use technology from precision intelligent positioning intelligence to prove the autonomous driving in mining. We have first applications where we automatically steer whole trucks in the mine to make this even more productive, a wide range of applications. The second part vertical I would like to speak is about building. Buildings, are millions of buildings in it. I'm not sure whether there are statistics on how many buildings are in the world, but there are a lot. And the nice thing for us for us is that the digitalization of the construction and with that also facility management is accelerating. It is a nice growth area for us and there's 2 elements in it. 1, the digital construction is in every man's mouth. BIM and so forth is playing a big role in the building industry. And BIM regulations accelerate the adoption of the digital construction because there are some places and some buildings where BIM is mandated and when you mandate BIM then you have no other chance to use digital solutions to basically work on those projects. However, the construction industry is well aware that they need to digitalize because they are hoping or want more at a higher margin from these kind of buildings, from building operations. But it's also relatively clear that these margin improvements will only start after they have to invest. And there is a kind of, let's say, challenge for some of these industries, because typically the construction industry is operating at lower margins and then investing in IT infrastructure, new processes and so forth is taking some time to adopt. And for us it is here kind of the challenge to take those kind of different sites, top down digitalization, bottom up kind of lots of trades on a construction site that has not yet digitalized to bring them basically on this journey together. Having then BIM made or compliant buildings means there is 3 d models available, there is IoT devices available to make those buildings more efficient is also in the challenge for the facility management they must digitize as well. In order to reduce energy consumptions of the buildings and to increase the utilization of this building, especially commercial buildings, When you have kind of like we have hundreds of operations around the world, you want to know which site is optimized in the utilization. The nice thing here for us, or let's say the challenge for the industry is most buildings are already built and not according to BIM, but they need also those technologies. So what there is creating a need to basically digitalize those buildings in the most efficient way. Our portfolio is approaching or touching this industry in 3 areas: in the design, in the construction and in the operation. For the design, and I think here is a business that we are serving since 200 years, for the design we are providing with our sensing technology digital twins for better designs. It's topographic service and so forth. I'm speaking about that later. In the construction area, we provide constant feedback loops between the digital plan and what happens on the site. And for operations, we're entering kind of the operational part of the business through a couple of disruptive technologies, enabling the digitalization of the operations of the buildings. Let me start with something that we do basically since 200 years. As we are serving the survey around the world, as I said, if precision matters, every second construction site has an instrument from us. We're taking here our surveying equipment represented here by one total station, but we have GPS and so forth and different versions, so there's quite some product lines behind that. But connecting them through our software to these design packages, because we are making these digital twins with our serving equipment, convert these data in these CAT and BIM readable data to basically form the right assumptions for the building applications. We are basically then, since a couple of years, since 2014, we're offering off the shelf topographic data as a service to construction companies, especially for large construction sites through our hexagon imagery program that basically gives topographic information as a service and can be licensed as data. And then we have disruptive technology to make things visible that are not visible. And that shows you the span where we are. This is kind of RADA technology that we have on board and we are creating quite some innovative software solution to that to make those data visible. And it's basically creating tomographies of the ground. And the radar looks kind of up to 6 meters into the ground to basically map the underground for pipes and also for cables. The key here is to simplify the interpretation of data and then to connect this data again into design packages that help to basically then construct the re world around it. The construction company with design packages is a bit fragmented. There are some big players in there and there's also small, but local players there. And since we have such a long history in that business, we are basically having through open standards and connectivity kind of a high tide or kind of significant tight connections to cut packages around the world. If we then go into the construction phase, then you have to transfer what is in the design to the field. And therefore, we are basically having these integrated software solutions in our sensing technology that basically converts the digital plan into the reality, where surveyors, construction formats, help basically to receive the data and know exactly where to place what. There are with BIM and the need of documentation, a need for construction progress documentation, which at the end is a collaboration service. We are experiencing quite some nice growth in this service, where we basically offer large construction sites the documentation services of their sites. So at this point, we started that with 2 d images, but we are bringing that over into 3 d scanning technology, that whenever a certain stage of a construction site, d that. So the challenge is, if you have it in design, it doesn't mean that it has been done the way. Only by documenting, only by proving and only by commissioning that everything was built in the right place and there, we can do this. So if you would have built this building, we could basically look through such a software behind that wall and see exactly where the cables are, where the installations are and so forth. It's a huge value for collaboration on the site between the trades, between insurances and the construction company and so forth. In this last example, I show you kind of the broad portfolio. We're making the survey autonomous also with UAV technology that you give your life update of your construction site. You create a 3 d model and then add to this the CAD model and then compare whether cut is equal to what was designed. You can use that for volumetrics on the side to basically automatically steer this data, the excavators. And then typically our serving instruments are daily on the job to basically commissioning the surveying to understand is was it built like it was planned to build. The right portfolio, as I said, 180 countries in the world, we are represented with our own distributions, but also through dealer networks, widespread around the world. A new era that we enter with disruptive innovation technology is the operations of buildings. This trend making buildings smarter, because now BIM is there and new buildings have all this information, creates this need also for the existing buildings. And there is a need for making these services to document your existing building more affordable. One approach here is the democratizing our 3 d asset documentation through our BLK3D that helps you to make pictures where you can measure, dimensioning it of all your assets you have in the building. We have a software solution that helps to share those data, to help to share this data into BIM solutions. So there is connectivity into BIM solutions, in progress documentation solutions, in project management solutions, so that you have kind of the real world there. So it is one part to do it. The other part is, as we have seen already in the 2 presentations, the BLK to go. The BLK2Go is democratizing the way you can achieve digital twins. What it is, is basically a hand scanner, imaging scanner and you will have the opportunity to see it later outside, which is edge computing enabled. We put all the intelligence into that system that you can walk kind of a floor like we have here in 5 to 10 minutes and capture it in a 3 d world. You get visual impacts, you can have a camera inside to make additional imaging information. But while you walk in a normal speed through a building, you basically create these 3 d models. It's then interfaced to CAD models, to BIM models, where you basically extract the data and then kind of reconstruct the building in a very efficient way. This is something which we see tremendous growth right now because of the digitalization in the construction industry. And there is a need of copying the real world and the digital world, so you get basically spatial awareness of all your assets. And you understand and run simulations for energy consumption, safety scenarios and so forth. So and having it in a simple form like we have here is something that we're very excited about. Another part, and we're taking that one step further, it's the real time constant feedback loop that we have developed based on different visualization and technologies together also with our central development hub to basically create here a sensor that creates spatial awareness. The sensor basically tracks movements of people since we do this LiDAR, it's GDPR conforms, so privacy things are given. We don't know who is going there. We could do this with cameras, but here we have versions that have no cameras and just give us information where flows are of people to help to optimize buildings, but also can create fences around things that should be not touched. And the exciting part is, since we have announced this product, there is kind of quite an enormous interesting applications out of this. And I'm pretty sure next in the next year, you will hear from the one or the other press release where this enters the markets in the operation of facilities. It's a new market for us. We are developing this market. We are connecting to existing solutions, but it's a really kind of a nice fascinating area. When we speak about the portfolio that we have for the building, you see that our sensor portfolio is touching every step in this. You see this kind of the field to BIM, that's the beginning. Before even design starts, our sensors feed into CAT BIM solutions. Then here we connect, obviously with our technology that is provided from Hexagon PPNM with BricsCAD, the design software. We have integrated our scanning solutions into this. We are connecting our service surveying solutions into SmartBuild, another solution that and Matthias will talk about this later, to basically optimize the construction progress management process to be more efficient. And then kind of going out when we have those data sets, we're going into integrated workplace management solutions or computer added facility management solutions that need also basic. They are right now underserved by these technologies and that shows a nice growth potential for us. Good. These were the two examples I wanted to show and I would like to end here with a quote. The only way to discover the limits of the possible is to go beyond them into the impossible. And I have say, I'm very excited that in Hexagon, we have with the Central Development Group under Claudio, but also with the R and Ds that on our innovation factories that we have within the divisions around the world that we have every year, very disruptive innovations that help us to not only improve what we do already and help our customers to go on the journey with us in the digitalization, but also to some of these disruptive solutions to enter new segments that provide us growth potentials and excitement in a day to do more than what we have done the last 200 years and continue to grow. With that, I would like Well, I'm happy to be here. For those of you who don't know me, even though it's quite a lot of familiar faces me, at least considering my previous jobs in the company. I've been with Hexagon since 2009. I used to run the strategy function earlier, but I've been in this position for 3 years running PPM. And today, in this session, we'll talk about smart digital assets, what that is a buzzword, right? But basically, we'll talk about how we, as PPM, putting data to work, really. You heard Jurgen talking about sensors, creating digital realities, getting all of this data. And at PPM, obviously, we're a software company, right? So I'm going to talk about how do we use this information to basically get more productivity, more efficiency for our customers, which are predominantly in, I would say, Engineering, Construction, Oil and Gas, Power Industries. All right. But before we get into all of that, one slide on PPM sort of overview. We say we create smart digital assets in the design, build and operate and maintain phase of all kind of complex facilities. So our customer base is, I would say, all or nearly all of the Fortune 500 owner operators in the world. That would be the Shells, the BP, the Exons, BASFs, those large owner operator companies. It would also be more or less all of the big EPCs. It would be the floors, Technip, Worley Parsons, Jacobs, those kind of companies. We have revenue of roughly EUR 500,000,000. Roughly 75% of our revenue is recurring, meaning that we have some part of the revenue is perpetual software, right, a smaller part. So that is the non recurring. And then we have a small services piece, but it's basically predominantly subscriptions of software. So that's our business model. In terms of customer segments, as you can see from the pie chart here, we have a big exposure to, they call it, engineering and construction sector. So whether it's somebody building an oil rig or a plant or a nuclear facility, could be a big ship in the offshore world, could be metals and mining, could also be food and beverage facility, medical facilities. So those kind of more, as I said, complex facilities tend to use our technology. In terms of geographical exposure, we have a fairly, I would say, even split between the 3 big regions, if you look at them as EMEA, America and APAC. And we also have decent, I would say, exposure in China and South America if you sort of compare it to the world GDP. Before we get into kind of what's our strategy, what's our portfolio, some new developments and hopefully exciting stuff, I thought I'll give you a quick overview of the market we serve, sort of what is the characteristics of that market, what are the trends, what are the implications for us. So when I meet sometimes investors, analysts, they tend to talk and ask about the oil price. I would say, yes, that is important, but it's not that important. It's not the thing that is driving our business. It for sure has an impact whether the oil price is high or low, but really what determines our growth, I would say, is more the investment levels we see in terms of CapEx with the big owner operators. And it's also the technology adoption or the adoption of new innovations in this industry, call it digital transformation, right, that adoption curve is much more important to our growth than the oil price. So if we take a look at the CapEx then, the investment levels, a couple of things you can learn from this slide. 1, there is a lag effect between an oil price and a CapEx investment, right? So the oil price tanked in 2014. The CapEx troughed in 2017. Our revenue was still going up in 2014, 2015. And then we had a couple of tough quarters between early 2016 to late 2017. And since then, we've been kind of on a nice growth path again. So that is I guess one observation here. Another one is of course that this has been a very successful story PPM. The company, as you can see, have quadrupled basically in size over the last 15 years. Another observation here, I think, is the CapEx forecast. As you can see, this is public information collected from the 30 largest owner operators in the world. They are projecting a slight uptick in CapEx going forward next year and then a bigger acceleration into 2021. If you look at the more longer term though, and you have to, what to say, bear with me here. I know this is a complicated slide, but this one is looking at supply and demand for oil. So it's 1,000,000 barrels of oil there on the chart for the next sort of 20 years. And as a backdrop to this graph, I think you need to, what do you say, look at the fact that the need for energy is going to double in the next 20 years. And why is that, right? I mean, if you pick one reason why that is, it's because a little more than 1,000,000,000 people today don't have electricity. So something like 14%, 15% of the world's population don't have electricity. So need for energy will increase. So the blue curve here represent the demand for oil oil and gas, if sort of current projections prevail. So I would say this is the most likely scenario. This is assuming what the experts and analysts, etcetera, know today of the world. That's how they predict the demand will look like. So basically, that it will grow for the 20 years. The yellow curve is using, I would say, very aggressive assumptions, I mean, honestly, unrealistic assumptions on a transition to renewables, assuming things like everybody has an electric car tomorrow, right? Things we know won't happen, but just to illustrate what it would look like. So even in that scenario, it would take 5, 6 years before demand will start to level off. And why is that? Well, it's of course because, yes, renewables is growing fast, but it's a tiny, tiny portion of supply, right? So it cannot replace traditional energy. The second thing to note here is, of course, the supply curve, the white curve, right? This is what they project it will look like unless investments in CapEx grow significantly. Because as I showed you on the previous chart, right, there's been a lack of investments over the last couple of years. And there are roughly 11,000 plants around the world that are in need of investments to just sustain current production capability. So my point being that unless investments increase, we will see a significant gap between supply and demand, which should be good for our customers and in the end us. Okay. So that's kind of the owner operator part of the market. The other, what you say, significant player you have to think about is the EPC, the construction company. And the characteristics of this market is, it's a market of very fierce competition. There's a lot of players. As a group, they do less than 5% profit. Some of them do obviously better, some of them do worse, but as a group, they struggle to make a lot of money. And why is that? Well, it's because roughly 90% of these projects are late and or over budget. And last year alone, Ernst and Young did a study where they calculated that it was roughly $500,000,000,000 in, let's say, cost overruns due to not meeting the schedules. So huge numbers, right? So to me, obviously, this is a problem, but it's also an opportunity because if you look at what we're trying to do with our technology is to address these exact problems. So why are they not more efficient? I would argue it's a lack of adopting technology. Sort of been a boom and bust industry, right? You invest a lot in good times, not so much in bad times. But they have not really adopted digital technologies until very recently. So if you look at this chart here in terms of yes, okay, it works. Technology, the digital maturity, down here you find oil and gas, metals and mining, engineering and construction, right? Whereas over here, you have media, automotive, even banking. Even you guys are better, right? So it's clearly, something needs to be done, okay. Tough crowd here, okay. So we did a study here recently using a consulting company. They interviewed a couple of 100 companies in our industry customers asking them how much are you planning to invest in, call it, digital transformation this year? As you can read from the bars here, roughly 70% said they will invest less than $10,000,000 1% said more than 100,000,000 dollars So I mean, when you hear those numbers, is that big or small? I mean, to me, it's tiny, right? If we had a 500,000,000,000 dollars completion miss and you invest less than $10,000,000 to fix the problem, to me, that's not a lot. So I think the good news here is that I bet you these numbers were a lot lower a couple of years ago, and I'll bet you that they'll be a lot higher next year and next year and next year, right? These investments are growing. If you go to any of our customers and ask them what their strategy is, it is to digitally transform their operation. And why is that? Why will these investments increase? Same study, we also asked them, why will you invest? And the owner operators said as their number one priority to stop project delays. They also want to be more competitive, of course. And then it's a long list of things here, many of them related to safety. If you look at the EPCs, their number one reason as well was to stop the project delays. Number 2 was to improve construction productivity, and I think that's only natural, right? If you look at studies, only about 25% of the time on a construction project is time on tool. So it's a lot of time wasted. So in summary, in terms of the market, as I said, I think the CapEx is expected to increase, maybe not skyrocket, but slowly, steadily should CapEx increase, I think. The industry is clearly behind when it comes to technology adoption. So these investments are bound to increase, in my opinion. This should be good for us. And in the next section here, I will try to explain why we, as Hexagon PPM, are perfectly positioned to benefit from these trends. So if you look at our strategy and our portfolio, if you're going to remember one slide from this presentation, you should remember this one because it's our, I think, unique selling point in Hexagon PPM. We are the only company in the world, at least to my knowledge, that can put up such a slide. We cover the workflow in this industry from A to Z. Anything from early design throughout the construction phase with our products through materials, our project control software ecosystem, fabrication, construction, completions and all the way into operations and maintenance. And we do this in an integrated way. These are not I mean, you heard Claudio talk about these are not point solutions. This is a data centric workflow using Xalt and other technologies to make this integration native to the customer. And we can deliver this on the desktop or in a cloud environment, right, depending on what the customer prefers. Another way to kind of slice and dice the same slide trying to simplify here a bit. You can think of our offering like design. We have a bunch of product there. We have a bunch of products in build and construct, and we have a number of products in Operate and Maintain. Then in the middle here, you see some products that we have that really go throughout these 3, call it, workflows. It would be Ecosys, which here says Enterprise Project Controls, so cost scheduling, managing the project. It will be an information management platform that we call SDX. I'll show that later a bit. And it's a product we call situational awareness, which is basically a visualization software in real time to display what's going on. And we do this also, what you say, in 2 verticals. 1, we call heavy industrial, which would be the traditional oil and gas, power, engineering, etcetera. And then more recently, we've also expanded into the AEC and infrastructure space, partly through innovation with our SmartBuild platform and partly through acquisition with a company called Brixis that we acquired, I think about a year ago. All right. If you try to explain our strategy on one slide, what is the most important thing going on here. I think it's a dual strategy. It's a strategy of protecting the core, if you like, or design business to these EPCs. And then it is making bets into construction and the owner operator space to diversify and grow faster. And this is what we have been doing for the last couple of years and it has already had a, I would say, significant impact on our revenue and our growth, and this will also be the strategy going forward. If we then dive into a bit more detail on the different areas and if we start with the design portfolio, this is still the majority of our revenue. When I started in this position 3 years ago, I kind of traveled around the world, met customers, asked them what would you like to see from us. And the number one thing the number one feedback I got was, we would like to see more investments into the U. S. So I went back to our developers and I asked somebody over there, do we have a problem with our UX? Do we need to do something here? And the person said to me, no, I don't really think so. Once they know how to use it, it's actually pretty easy to use. So I said, okay, that's got to be bullshit, right? I mean, that can't be the right answer. It should be easy to use, period, right? So we had some meetings, and we looked at the roadmaps. And back then, it said that we were going to launch the new UX in 2021. So we said that cannot be the plan. It needs to be better. It needs to go faster. So we shifted around some resources. We actually invested a little bit more to be blunt about it. And the good news is we just launched a new UX here in October this year. So that's been really good, and I think it will be really exciting for us going forward. If you want to see what it looks like, here are some snapshots. I think other than that it looks really good, I think, more importantly, we did a lot of studies on how can we speed up, make it easier for the user, right? So we did a lot of studies on how you move your hand and the mouse, how your eyes move on the screen to reduce the number of clicks and reduce no drop down menus and searching for feature functions, right? Everything should, using algorithms, pop up at your fingertips depending on what you're trying to computer should understand what you're trying to do. So we think by doing this, by upgrading to this, our customers can save something like 15% to 20% of the design time. But what's the ultimate level of UI, right? When we talk autonomy, you heard Ola talk about this as well, right? We want to, in the end, automate as much as possible. And what you see here on the screen is a short video of a project we're running right now called auto tagging, where we're using artificial intelligence to laser scan a facility. We overlay that scan with the design drawings, the so called P and IDs, And we can basically automatically tag everything in this facility and, yes, saving significant cost basically when you're trying to renovate or upgrade these brownfield facilities. If we move to the build area and start with the strategy here. I know this is a busy slide. It's a study we got from McKinsey. Actually, they looked at what companies provide say they provide technology into the construction industry in terms of software, and they found 2,400 companies. And if you look at let's see if I can get this here. So 4 84 companies said they do document management. 314 said they do BIM modeling. And the lines here between these technologies represent companies that do more than one technology. And if you read this whole report, the conclusion is that very few companies do more than 2 or 3 of these technologies, and they're not often very well or frankly at all integrated, right? It's more a landscape of a lot of point solutions. So a couple of conclusions from this, right? 1, if you're a customer here, it's quite hard to know which technology to pick. 2, how are we going to play as hexagon here? So when we laid out our strategy here a few years ago, we said the strategy cannot be to create another document management solution, right? That won't help anybody. So we need to build a more comprehensive platform. So what we have done, and you also kind of heard Jurgen talk about this to some extent, We built a platform that we now call SmartBuild, and it's using everything from data that Jurgen Sensors collect into using design software, could be our own software like the Brixxis or it could be 3rd party software that we import into a construction, call it management platform, where we do things like scheduling the work into work packages. We look at quantities in terms of materials, what material is going to be used. We use functionality from to do planning, scheduling, costing, etcetera, estimating. And finally, we also take in data from the construction site in real time using things like the total stations, the laser scanners, etcetera, that the Geosystems has. So that is basically what SmartBuild is. And I know I will get a question later, so I might as well take it here. We have a handful of customers on this platform today. Our plan is to go broad commercially with this early next year, and we're quite excited about that. I want to focus on the design part for just a minute. This is some snapshots from the Brixx's design suite. They use a lot of artificial intelligence in their product, which I think is pretty cool. They have a function called Bimify, as you see here, where the software automatically can specify objects in a drawing instead of you having to manually tell the software what you have done. They have another function called propagate, which is basically an advanced copy paste function, you could say, using artificial intelligence, where the user only has to draw an object once and then the software will automatically suggest where you could put another similar object. So here it's creating columns, right? And you can see how basically the user then can select if he wants to accept these suggestions or not. So saving a lot of time for the user instead of drawing each of these columns 50 times. Right? You do it once and then basically use this propagate function. Finally, like I said, at the end of the process, we do use the real time information from the construction site. So this example is from a project we're doing in China. It's a huge technology park that's being built there, where we're using the information from the total stations and the laser scanners to update the model and do progress measurements, etcetera. All right. Final area here of PPM would be the operations and maintenance space. So similarly here, when we laid out the strategy a couple of years ago, we tried to look at the market and kind of decide where we wanted to play. And you could look at it like these puzzle pieces here, where there is a big chunk of the market that is kind of control systems and data historians. So it would be players like Emerson, Honeywell, OSIsoft, AspenTech, etcetera. And we said that is probably not for us to play. It's quite consolidated and well established player already. Another area would be asset reliability, space kind of dominated by GE and AspenTech, ABB, players like that. Also, I think, not really an area we wanted to attack. And we obviously didn't want to try to compete with SAP and IBM in the ERP space. But if you go to one of these owners, they buy hundreds of other software packages. So what is that, right? So to make it simple, we said, let's call that everything else, right? There's a lot of other stuff they buy. And I said, what if we can try to consolidate this consolidate this everything else and make that into an offering? So when doing that, we came up with this. We defined our offering as 4 different things. We want to start with a digital twin of the plant, right? You've already heard that word a lot of times today, so I don't have to explain it to you. But a digital twin in itself doesn't provide much value, right? You need to connect people to it, information, etcetera. So, we developed a product that we call the Connected Worker. Then when you have connected these people to the model, let's say, you want to give them tasks, you want them to report back. So we started adding work procedures that we call operations and safety management. And then finally, you want to visualize this, right, for management purposes. So taking a look at these offerings, starting with the digital twin. Here you see a short video. This is looking at our SDX platform, so the information management tool. So here you see a plant or facility, you can click on any object, any tag here, get all the information about that plant, and then it will automatically open up the 3 d model. So the important thing here is that this is a data centric model. If you make an edit in one of these systems, it gets reflected in both. That might sound simple, but I promise you, it's a very complicated procedure to do that, and it's something that is a competitive advantage that we can do. So once you have built this digital twin, as I said, then you want to connect your people to it. So using this Connected Worker product we have, we can take information from our own system, 3rd party systems, display it on an iPad or a phone and give it to like in the hands of the worker on the site. But then as I said, you want these people to do certain tasks. And in the owner operator space, there are a couple of tasks that are the most common. It would be things like shift handover, permit to work, safety rounds, completions activities, things like that. And we didn't have much of an offering there, to be honest, a couple of years ago. So we decided to acquire this company called J5, a while back, and they gave us exactly a lot of those activities. So now we have integrated J5 into our SDX platform, making it a very good product. And then finally, as I said, once you have all of that, you want to be able to visualize this in real time. So we have a product that we call situational awareness. And this is something that initially, I think, in that technology was used to companies like airlines. They wanted to visualize all their airplanes on a map moving around, see where they were. And then they wanted to link that to a real time schedule. So if one airplane is delayed, they need to recalculate the schedule for all the planes, right, and all the passengers. But we figured out that we why can't we sell this to anybody who wants to manage things in real time, right? So what you see playing here on the video is a customer we have in Germany. They obviously have a digital twin here of their plant. And the bars and colors you see at the bottom of the screen there represent people and activities throughout the day in real time. So basically, they can manage things like permit to work and safety applications and things like that using the model to see what's going on. This one is kind of showing the same thing, just on a bigger scale. Here, we're going to look at 2 plants. We're going to switch between SDX, laser scan and the Citra Innovation and Awareness product. You see here, we're looking at the scan. We're in SDX before. And then here in a second or 2, the user is switching to another plant that they have, basically using the mapping technology, and then we can do the same thing over at this plant. So again, kind of digital twins, but here we've connected 2 plants to it. And here, back to the tag, the asset, you can find all the information about that object, right? When was it last serviced? Where did it come from? Where do I fund replacements? Etcetera, etcetera. So pretty cool, right? Good. Need to make sure you're awake, okay. Good. So to summarize, we think we have a pretty solid offering in the operations and maintenance space today. That was the point. Short version, if you want to remember something. Okay. But even though we think we have a very comprehensive offering, we still need to connect to other software, course. So what you see here is, again, SDX, but it's pulling information from those 2 softwares. So to the user, you're still in our product, but you can pull data from other sources. And finally, to extend this, we announced earlier this year in the summer timeframe, we announced a partnership with AspenTech because that is one of the other major players in this industry, where we want to make the automation better and the integration better. So AspenTech is basically a leader in their space in terms of simulation. We are a leader when it comes to design and information management. So we think this is a very natural fit. In terms of focus, it's 2 things we're trying to achieve initially. 1 is facility and our design software. The second piece would be AspenTech has a lot of software when it comes to predictive maintenance, sensors and getting information from what's going on in real time, right? We want to integrate that data into our SDX platform just like I showed you with the OSIsoft product. So that's basically the plan. And it's a lot of work going on right now with AspenTech, both in terms of customer visits, in terms of R and D, etcetera. So you should expect some news to come out here hopefully, next year. All right. So I think that was it from me. If I were to conclude or summarize, I think I said pretty positive about the market, slow rebound of CapEx, but the long term demand is not going away. We have a unique positioning in the digital transformation market. You should expect our build and operate segments to grow outgrow the design space, but we still expect the design area to grow. In terms of strategy, we will continue to invest in our UX and our integration because I think that's our uniqueness. And you should also expect our diversification strategy to continue, both through innovation as well as through M and A. That was it for me. Thank you very much. I would like to introduce myself first. My name is Noel Hanke. I'm the President of Manufacturing Intelligence since 10 years. Now we want to make things smarter in the manufacturing world. With this, I would like to introduce as well the core capabilities of manufacturing intelligence. We go through this, but we will do a deep dive with Paolo Gollimini to go into MSC, the capabilities there, because we added this only 2 and a half years ago. And he will talk further actually about volume graphics as well. The latest acquisition we have done. Now let me start with where do we come from. We do come from metrology. What is this? That's an inspection verification of things where you compare parts as is as built towards a cat model. And that we have done for actually decades. We have seen some trends in this, in the sense like, okay, before it was in lap environment. Now everything has to be close to production or actually in line of the production as well. So therefore we had to build up shop floor capabilities as well. Now I hope that you tried out our arms with the RS6. We hope hopefully you scanned your face or your hand because that's actually is the opposite side. This is reverse engineering side. We digitalize things and then can produce things because there is no cat file, I assume, of your head around. So we need to make this happen. So another trend, and we are quite well known for the so called laser tracker as well, where we can measure things and align things in the so called aircraft industry. We needed to add equipment, which is multi sensory because there was a strong need from the electronic side from the segment. And what it means is actually to combine tactile probes, white light and other technology into one equipment. So this is about the metrology, but what have we seen over the last years as well was that this data needs to be used in an earlier stage of the product life cycle. And therefore we started off to add CAD cam, CAD cam, functionality as well. We call this now production software. And this was the start of the journey on the shop floor, I would call it, because it's super important for us to gather the data, make things more efficient on the machine tool side and the shop floor side. Now started with that. We added them as well tool management because in the machine tool area, quite often, actually the tool and the tooling cost is higher than the original investment of a machine tool. So after this, we wanted to start the journey into monitoring the machine tool, simulating the machine tool, and then optimizing this further. So you can see we added quite a portfolio on core capabilities. Now after this, the all the discussion we have heard starting in 201112 about smart factory and as well industry 4.0 thought that we need to deploy actually the information even earlier in the product life cycle. Therefore, we added MSC 2.5 years ago. And as I said, Paolo will talk a little bit more about the business, what MSC is doing, what they simulate as well, either on the parts side, material side or the system side or the process as well. So with this, this is the environment where we active on the manufacturing side. So design engineering, production software, and metrology. As you nicely can see, this is a sequel, sequel process at the moment, but what we want to drive, because we want to drive this to more a digital ecosystem. What is important to have that or for this to drive this 2 words? There are 3 imperatives. 1, for sure, it has to be digital. Things have to be connected. And I think you have heard a lot about connectivity, what Claudio was talking about, how we can do this inside Hexagon. And then we have to build intelligence into this so that at the end we break down the silos and create digital ecosystem where we actually can use and leverage data in real time. So at that, at the end, we create the autonomous factory. Now we know, and we talk quite a bit on automation. I think Ola, you mentioned this as well on the automation. Automation means it's a single discrete task or processes we apply. But what we really want is we need to use the data using advanced technology to bring data to work so that we have a self optimized system. With this, we don't have any intervention, no external intervention like from a human being. I give you an example for this. The autopilot of an airplane, because on or in certain situation when it's very foggy, the autopilot is only allowed to land the plane. This system is taking all the data, speed, height, wind, and other weather condition into the self optimizing things to land the airplane. So this is where we are driving towards. So automation is good. Autonomous is even better. So what does this mean now for us as well? We have the 3 data sources we talked earlier, design engineering, production and metrology. What we tried to do now and we'll develop further is for sure digital twins of our own assets, for example, with asset management or factory digital twins. You have seen from Europe a couple of very interesting technologies, which we will try and we will do to use this in the factory environment as well. Now, digital twin is one thing, but where we are really driving things towards is the digital threat. We want to be on creating the end to end solution of an industry of a very specific workflow. And I will take you on the journey to show you 4 or 5 examples of that. I will talk about the assembly, for example, or the production machining where I bring this in the context of the different industries. And Paolo will talk about the additive side, the end to end solution side there because that's heavily related to MSC and as well to volume graphics, our latest acquisition. So I talked about where do we come from, what are the capabilities, where we want to go. But I would like to take the opportunity as well to tell you a little bit about Manufacturing Intelligence at the moment. So we are active in the outer side and the aerospace side. That's the 2 major segments where we're active. We added electronics over the last 5 years, and we are further investing into segments which are at the moment under others on public transportation. When you then look from a regional point of view, where are we active? Maybe it's not a surprise, but we are equally active in North America, in EMEA and in China. I would like to come back a little bit to EMEA because we see a clear need of using our core capabilities in Eastern Europe. And in Asia, there's 2 countries where I can see a clear demand where we have developed our presence very, very nicely. One of them is Japan. And there, we developed our own organically developed in our own organization, but as well via the acquisition. So our presence in Japan has really improved. The second one is we are actually we go a little bit later in is Vietnam because that's for us a very interesting market as well. When you then look where we active, it shows here as well on the slide. We are active in 100 countries roundabout with either our own workforce or to 25% with our distributors and resellers. And sometimes they are related either to specific industries or to regions from our point of view. The last development in Manufacturing Intelligence over the last 5 years has for sure bumped up the percentage on software and services. This is now 60% of sales. And the total amount for or the sales for this group is €1,500,000,000 So where I would like to take you on the journey is really to look at the customer challenges in the 2 major segments where we are active. 1 of them, as I said, is error. Here. You have 2 challenges for sure. And I will go a bit more in detail is the time to market and the productivity. Time to market here as an example is the design of a wing. You have heard about there's new aircrafts coming out with new engines. For example, there is a requirement to design the wings new. So let's go into a little bit details of the workflow because that's the driving force for us. The digital threat going forward to create the end to end solution over the last couple of years, we have actively managed and added capabilities. You can see from the left, for example, the MSC simulation side, and this is the same pattern you will see from design to production to inspection. When it comes, for example, on the inspection side, you see their laser trackers, either T scan, for example, where we have a very strong business. The other thing is I would like to highlight to you as well, we're very active is on the metrology assisted assembly. So this is the typical workflow of how to design, produce and inspect a wing. Now we believe that we can create end to end solution sometimes with their own unique interface, user interface, for example, but with adding feedback loops. So we will try we will do and bring the data to work. Either we have shorter feedback loops, I would call it. So from inspection to production, for example, or the wider feedback loop from inspection to design. And here, just to talk about this inspection, there's data we can use to optimize, for example, the machine tool, or you have heard as well Ola talking about the virtual assembly, because if we have the digital or the digital twin from that, we can look at things and saying, is that the best fit particular on an aircraft because you have massive aero structures. So this is an example where we believe we can add value and we will go forward to create the end to end solution. One other thing is the aero engine side because there's, as you all know, high backlog for sure in the aerospace and the aero engines have to be inspected everything. And just only for information, an aero engine has around the 1, has around about 1500 blades in different segments of the engine and we are able to simulate on the forging side, for example, how to produce this, inspect this wire white light, produce this, and then use our tools on in process. Here's a machine tool, for example, or on the CMM with an HBO. So this is the information. That's the flow what we have and what we are now building is to have this digital threat. Again, same principle here as well is the shorter cycle is every time to the machine tool And then we have possibility to use the data here from the white light into the simulation sites and software called SIMO fact. That's another alternative of end to end solution which you're working on. So we leave now the error side and go more towards the customer challenges on the outer side. They are to a certain extent similar, see, challenges, the time to market and the productivity as well. Time to market, let's talk about this for a minute. We are talking about that normally to design a car, it does take between 4 to 6 years to really launch the car. Very important there is the chassis, the car body where we are very active. And in this process of creating the car body and design the car body, you need then to stamp the sheet metal parts. So either it's a bonnet, a hood or a door as you can see. So when you have stamped this and you have to prototype, then actually the process is to bring these sheet metal parts together on a very expensive fixture. Some customers are talking about the Meister bot. You may have heard this as well. So then you bring these parts together and you can see this here on the screen as well. Then it looks like the new car, but from distance, maybe. When you go then into the details, you see the flash and the gaps. And if, for example, the stamping process was not good, the design was not good. What you need to do is then to take the information and do an iteration. So you need to optimize them, sometimes even manually, the stamping tools. Now what can we do different? We can digitalize this. Here, for example, with the white light. With this, we can as well simulate. Thanks to MSC, the virtual temping, because that's very important because when you clamp, there's a deformation. So this we are able not to do. With this, we can then produce the virtual car body. What's the say advantage? The advantage for sure is the time because you can simulate this and do this a couple of times. If you then find a parameter, which you need to correct, you do it once to the production and optimize the tooling. This is the process which we will launch as well or part, and I would like to say this as well, is a journey from our point of view middle of next year. Now having said that, one other thing is super important is the productivity of the machine tools itself. Over, I would guess, the last 3 years, we added, developed or added by acquisitions capabilities. For example, calibration verification, we are now able to calibrate very, very good machine tools. We can simulate the machine tool itself. I talked earlier about that we can manage the tool and machining in the sense. And we were, for a long period of time, able anyhow to measure in process. So what are we doing? And Ola showed here very nice picture on the smart factory where we have developed our machine tool feedback loop. What are we doing there is the following. We are producing, monitoring the quality of the part. If we see trends that the part and the quality of the part is leaving the specification for a better word, then we react to it and we do an automated tool offset. We're optimizing the tool offset in this area. And that's continuously from my point of view. So it's going 2 words as we say this, 2 words self optimized tool, machine tool. Then the other thing is that we are seeing and try bring together process data and metrology data so that we have traceability on the machine tool itself. So this is a super exciting segment and end of end to end solution which we are working on. Now, I was talking earlier a little bit about the gear and the ECA. So it's maybe a little bit strange that I talk about transmission and ECA because your first reaction is there's not much transmission, which is correct, but the transmission you are using there and the gears are super high specification from our point because why? There's 2 elements to it. 1 is the noise level and the other one is actually the torque. So the noise level is I think very clear. When you're sitting in an ECA, you don't have the noise of a combustion engine. So that's relatively straightforward. But as well, on the torque and probably some of you have either ECA or has driven ECA, then you know the torque is unbelievable in this car. So meaning the specification of this year is unique, is super high, super high position for this. We need to measure the gear with our ultra high machines and feeding via the metrology software back to the gear design and simulation. For this, we are using the ADAM software. So there is as well the feedback loop in the ECA. So these are the examples, the end to end solutions where we believe that we can create customer values. It's very clear. We have the different capabilities, the technologies and now we are bringing things together either with say common user interface as well or that we create them the so called feedback loops in the shorter feedback loop or in the longer feedback loops I would call it. So having talked about all the solutions, I would like to talk a little bit more about customers as well. I mentioned to you earlier that we are very active as well in Asia and particularly in Vietnam. And we were able and we are the key supplier, I should say, for Windfast, which is the 1st carmaker in Vietnam. We came in via our quality inspection devices, extended our offering via the data management of this and now brought in as well MSC. So this is the way how we work. Now I think it's time and I was talking so much about the core capabilities and technologies, and it's very clear that MSC is one of what we missed out. And therefore, I'm very pleased as well that Paolo will now talk more about what is happening on the MSC side, particular as well with volume graphics. Thanks. Good afternoon. Thank you, Norbert. For those of you that don't know me, I've been with Ekerzagon for the last 10 years working in a variety of roles within manufacturing, product management and then increasingly M and A and strategy. So what we have tried to do over the last 10 years, as Ulla was saying, was to build capabilities, yeah? And so pretty much, I myself look forward to the next 10 years in which we're gonna to try and combine intelligently solutions. The market is ready for further and further connectivity between disciplines and really tried to make the most out of these digitization investments that have been made through the years. MSC Software has been with us for almost 3 years and I've been responsible for the acquisition for the last 2. So I want to take you through, first of all, the rationale, why we did that acquisition in the first place. The CAE market has been for those of you that are familiar with it, has been growing up to the current €5,000,000,000 to €6,000,000,000 through expanding, moving from the sort of analyst down into the supply chain. These tools have become easier to adopt. We've grown in simulation from structures to other physics. So why simulation and why MSC? So just a quick recap of what is the value that we saw in combining those capabilities to our portfolio already existing. So first of all, when you try to deliver the next set of values to your manufacturing customers, What they want is confidence, confidence in their processes, optimization of their processes and confidence on the quality of the parts that they manufacture. The 2 core tools that we have in the industry today for achieving those results are analytics and simulation, yeah, analytics to understand the process and drive it to the next level of productivity and simulation to test in a virtual world the performance that those components will have during operation. Yeah. So that was a key a key core component that was missing for our smart factory direction. The second aspect that if you want is a little more commercial or tactical is that in reality, the moment we're trying to expand the value of our solutions, expand deal sizes and try and become more and more strategic within our customers. The reality is that more and more of these decisions were taken at the level of technology leadership, R and D leadership. So MSC basically gave us an opportunity to access decision makers and budgets that we couldn't tap into up to a couple of years back. The third aspect is really the growth of the CAE market. The reality is that we are at an injection, at an inflection point in the adoption of CAE. We're going to be talking about this in a couple of slides. Traditionally, simulation expanded operating physics in isolation, point solutions, simulation runs applied to CAD. CAD is a great tool. When we talk about manufacturing, it is the repository for what we know about a component, the manufacturability of that component, the geometry, the material properties. But the reality is that by the time CAD is finished, probably we're already late when it comes to time to market. So when it comes to simulation, we want to use those physics much earlier in a pre CAD phase. Yeah. And we want to be able to use those technologies as a prediction tool further down in the process. So really expand the impact the simulation can have for our customers. If you look at the users of simulation today, we talk about a population of 300,000 analysts pretty much. Yeah. There are 10 times as many users of CAD and you can imagine how many users of engineering softwares you have further downstream in the production process. So what we're trying to do is to really boil down simulation to bite size usable components of technology that we can apply not only to CAD but to pre CAD to as built components, yeah, so that we expand the impact that it can have in these processes. Then the question became why MSC within simulation, yes. And MSC, again, a lot of you, I think, covered engineering software and the PLM market at large. MSC has been one of the pioneers in the simulation space, highly regarded, highly respected, especially when it comes to structures and systems simulation, but also a company that has been lagging behind when it comes to growth against some of the peers in the industry. So obviously, the mindset that we walked into pretty much with Open Eyes, we wanted to have a solid growth strategy for MSC. Now fast forward 3 years, almost 3 years, I think we announced the acquisition in February 2017. Our design and engineering market or operations went up to 1500 employees roughly. MSC has been growing solidly for the last couple of years back to market, average market growth for that specific discipline. And as a result of organic growth and free acquisitions in particularly, really we have accelerated the speed of innovation. We're highly recurrent when it comes to our software base and we are accretive to our bottom line. What are we trying to achieve from an innovation standpoint in detail? So I want to mention to you 4 things, yeah? The first one, APEX platform. So when you want to imagine the simulation space, yeah, and there's $5,000,000,000 worth of software, yeah, the reality is that simulation is hard. Yeah. It's super hard. It's probably the toughest piece of engineering software that you can get yourself into from a user perspective and I speak a little bit by experience here. It's tough and it's very hard to solve problems from A to Z staining staying in the same environment. So if you are an automotive engineer, you probably need to use 10 to 15 tools to solve a problem from A to Z. Once you know the performance of your vehicle, you have designed the system, the powertrain component, the transmission, you come down to structures, components, subcomponents, materials, the manufacturing process already there. You probably need today 10 to 15 tools and vendors to achieve that goal. Yeah. So we're going to try and alleviate some of that pain. Yeah. And according to us, an intelligent way of doing that is to develop a platform that allows you to be within the same front end, within the same environment that allows you to use your geometry across all of these products and that is open, yes, unlike the vast majority of the competitors that we are up against in this market. Because again, no CAE vendors can solve a problem A to Z. And if you have a platform that allows to connect solving capabilities from other vendors, that generates massive value and massive trust in your customer base. The second aspect is that MSC traditionally is very strong when it comes to systems and structural simulation. This is where CAE really developed. But there are in the CAE space today a variety of niches that are growing much faster than structures. Yes. So today we can simulate manufacturing processes. We can simulate microstructures of materials. We can simulate autonomous events, yeah? And what we're trying to do is to really concentrate our investment in those areas to make sure that we are state of the art, yes. And those areas have been growing very fast for us over the last years and they have been driving a lot of the growth that we have seen. The third aspect and really the key rationale behind the acquisition itself was the smart factory readiness. Yeah. So really the capability of tuning our simulation solvers in a way that make them usable further down in the process. Norvail was talking about additive manufacturing. I will give you an example of what we're trying to do, really bringing simulation out of the hands of the experts only and as much as possible throughout the life cycle. Last but not least, what we call force multipliers, which is a very elegant term to basically say we want our users to access simulation as easily as possible, scale simulation tasks as much as possible and consume as much as possible. Okay. Let's start from the Apex platform. A couple of examples in the background here. If you think of a way in which a car is developed today, really we start on the left hand side from defining the systems performance. So we explore the design space, we define the system, basically breaking, chassis performance, handling of the vehicles. We know what we're trying to achieve. And with Adams, we have probably the best technology in the market for achieving that goal. And then we come down to the level of actually requirements at the component level. We eliminate as much as possible matter from those components to make sure that we deliver on our sort of lightweight strategy. And for this, our topology optimization generative design technologies are extremely strong. And then what we want to do is to make sure that these components fit in the virtual world and ultimately are manufacturable. Yeah. All of these tools today are approachable, achievable within the same UX, within the same environment, which is not obvious. If you think of a design department in Volkswagen, we're talking about hundreds of developers, hundreds of engineers in multiple geographies working on the same models and trying to achieve exactly this workflow from A to Z. Yeah. If you have the capabilities of delivering, those technologies in the same front end with the same reporting, with the geometry that can be shared across models, that's a massive gain in terms of time to market. So this has been probably, I would say, the number one investment driver for us over the last year. The second point concerning adjacent markets to structures. So starting from the top left hand side, virtual manufacturing. I think the virtual manufacturing capabilities of MSC were one of the primary reasons why we thought that that was a great piece of technology for our offering. Yeah. So for a company like ours that has a footprint in the manufacturing process, so we can actually test and measure those components, see what they look like at the end of the process. The fact of being able of being able to virtually manufacture components in the front end and compare results is a massive benefit for us. Yeah. It creates innovative solutions and it really builds confidence in our simulation tools. The second area that I really want to unpick amongst all of the others is the development of autonomous vehicles and ADAS. If you go back with your memory to some of comments, Matthias comments before, it's unquestionable that we are state of the art when it comes to capturing reality, yeah? So fusing our sensor types, we can build very accurate maps that have a digital representation of the reality. Yes. Matthias was talking about tagging, which really means scanning with intelligence, which basically means recognizing objects, yeah, attributing properties. Now go back with your memory at all of the papers that I'm sure you read about autonomous vehicle development. We know that we want to run billions of simulations in the real in the virtual world to be able to run safely 1,000 and thousands of kilometers on the road. Yeah. So we want our 3 d systems, our virtual simulation to be as accurate as possible. So the joint capability of simulation together with creation of those 3 d environments give Hexagon today probably the most accomplished development ecosystem for ADAS and autonomous vehicle. We have acquired a capability in this space 3 years ago. I think in April of 2017 was probably the first acquisition that we have done on for MSC. That business has almost tripled in size. There's incredible demand and the race for autonomy really goes through building capabilities in the virtual world. Now still remaining focused on autonomous development and ADAS development. Take a look at this curve on the left hand side, the need for scalability. Again, the reason why we are very bullish about the future of simulation in isolation and also the value that we can create by bringing real and virtual world together is because no engineer today can beat competition running simulations in isolation. Yeah. That was the world of, 5 to 10 years ago. The world of today is much more about running multi physics in parallel using high performance computing. The world of tomorrow is a world in which basically machine learning will learn as much as possible from past simulation runs. Machine learning will help us pick the scenarios that we want to run-in the virtual space and we want simulation to be massively scalable. Yes? So we want our customers to run as many 100 and 1000 and tens of thousands of simulation runs in parallel on cloud as we possibly can. Yes. 1 of the most exciting and I will say the benefit of these technologies from a customer perspective are fairly obvious. It really is time to market. Yeah. You want to have a crash course and optimize your design in the minimum possible amount of time. Yeah. From the hexagon perspective, this really has got to do with consumption, consumption of CPUs and consumptions of our software environment. The example in which, according to me, this is the most visible, the most productive, yes. And from our perspective, commercially speaking, this got also the biggest potential is really the development of autonomous vehicle and ADAS systems, Yeah? We have started a development, roughly a year and a half ago about a tool that is about to be released at the end of the month called Scale. And Scale allows us to do exactly that. We use hexagon proprietary machine learning technologies. We want to allow customers to run as many simulation runs as possible and basically do reinforced learning for their artificial intelligence drivers. Yeah. This is, we think, super exciting. We have exposed the technology to a lot of our OEMs. We work with the vast majority of the players in the autonomous space and there really is the burning desire of maximizing learning by running multi core parallel simulation of their scenarios. Okay? The last example that I have for you today, it really is about CAE immersed in additive manufacturing. What do we do? Let's pick a vertical. What do we do to support smart factory today? So the reason why it makes sense to talk about additive manufacturing and this is one of the processes that we can disrupt the quickest, the fastest is because, of course, this is a growing technology. Processes are not as consolidated. Efficiency really is far out from the efficiency of traditional machine tool, manufacturing processes. So this is where connecting from the design to the manufacturing to the inspection can bring the best results. So if you take a look at our capabilities today, if we start from creating geometry and topology optimization, Today, 3 d printing is super exciting for all manufacturers because really allows you to create complex parts, shapes that wouldn't be possible according to traditional manufacturing processes and remove from those components as much material as possible. Make your components as lightweight as possible. Okay? So through our topology optimization technologies, we can really optimize that process goal and last but not least, we can follow this process inspecting within 3 d printers and then testing the quality of those components through our metrology devices. Okay. In terms of closing the feedback loop in between design and machining and optimizing this process, this set of capability is very unique in our industry. The challenge for us that we're going to try and address with the first drop of technology, I believe at the beginning of Q2, is really to have all of these capabilities available in one front end, in one interface. Yeah, we have one platform that designers, engineers, manufacturing engineers and quality engineers can really access. And obviously, that lifts the value of all of our point solution in the process. Okay? And this is only one of the examples in which we add value. I think Novell has talked about a few more from a sheet metal perspective. We've talked about gears. We've talked about e vehicles. Okay. The last slide that I have for you today really talks about volume graphics. Volume graphics is an acquisition that we have announced just a couple of weeks back, a software house based in Germany, a super exciting company, extremely unique in what they do and they are market leader in CT in software for computer tomography analysis. So what is computer tomography? What is X-ray technologies? Think of a world in which, typically using metals, we were able to cast a component and the internal geometry of that component was completely homogeneous. Now think again in the world of 3 d printed parts where we have internal geometries that we want to expect or think again in the world of composites in which we have tens and tens of layers, we have a different orientation and we really want to see inside those components in those parts as accurately as possible. Because of complex parts, because of additive manufacturing, computer tomography has become one of the fastest metrology devices used in the world of manufacturing today. CT allows us to be with all those users of computer tomography today. What is particularly exciting for us when it comes to volume graphics and their capabilities is that not only we can take a snapshot of those components, not only we can know everything about the interior geometry of those components, not only we can see cracks, defects and porosity but by connecting those components to MSC and our set of solvers, we can test virtually those really measured defects. Okay, so we have 3 d printed a component, we see there's a crack, We are uncertain if we can maintain those components or discard them. Now we can virtually test those components using our solvers and basically take a position on whether, that's a good path or a bad path, which is extremely unique in the industry today. Very importantly as well, volume graphics is highly utilized in the design process of electronics, in design processes in medical and that opens up a new set of markets that we can address through their solutions, our inspection capabilities and CAE capabilities. Okay. So thank you very much for your attention. Back to Norbert. Yes. So I think it came very clear across how important sorry, thanks, how important it is to have the possibility of simulation. I mean, we talked about this in the context of smart factory, but please think about beyond that. It's not only for MR, it's smart factory important, but it's as well important for Hexagon overall. Again, the journey we started from metrology, added production software, very important core technology on simulation. And this gives us a very unique position going forward to develop the end to end solution. And this is the driving force for us, what we talked about. You heard from Paolo, particularly on the Additive side. I think this is a super important segment where we want to really disrupt things because as you nicely said, there's not set things over the last 20 or 30 years. So people are more open to for changes from our side. So I think that's going forward where we are driving and want to have the disruptive technology when it comes to end to end solutions. Thanks a lot. Thank you. And now. Okay. Good afternoon, everyone. My name is Robert Belkic, and I'm the CFO. And I'm going to talk about Hexagon's financial transformation and then also focus on the financial implications of that transformational journey. So starting with this slide then, a slide that you've seen several times during the day and which in a very simple way explains Hexagon's strategy and stresses the importance of combining hardware with software. Hexagon's legacy in sensor solutions, combined with the software portfolio, has positioned us as a leader to enable autonomous solutions. So let's put this financial this slide in a financial context and see what it has meant for the financial development of Hexagon. So starting with taking you back then 20 years in time, back to 2000. At this point in time, nothing of today's Hexagon existed. We were a diversified Nordic conglomerate, €500,000,000 sales, 5 percent EBIT margin, market cap of SEK1.7 billion. And the transformational journey really took off in 2,001 when we acquired Brown and Sharp, which is our metrology business today, which is the core of our Manufacturing Intelligence division. So during the 1st 10 years of this period, Hexagon built a world leading portfolio of sensors, both for industrial applications and for geospatial applications. A portfolio that then, during the following 10 years, have been complemented with software that has been added to these sensors. And the takeoff of that was really us acquiring Intergraph in 2010. So the strategy of combining sensor and software has also led to very impressive financial development, which is evident from this slide. And today, when we are approaching the closing of 2019, we have sales of close to €3,900,000,000 close to an EBIT margin of 25% and a market cap of SEK193 1,000,000,000. So the addition of software and services has also made our business more stable and resilient, and I'll cover that in some future slides. So before leaving this slide, I just want to emphasize some more milestones. So I don't want to brag and no shadow over the 1st 10 years, but if I get to pick the last 10 years is definitely my favorite. Don't you agree? Okay. So moving on then. Okay. So holding on to this 10 year period then and comparing 2009 to 2019 and then focusing at recurring revenue and software and services. What is evident from this slide, recurring revenue has increased was 10% back in 2,009, it's now 40% in 2019. And it has very much been enabled by us growing the software and services part from 15% in 2,009 to 60% in 2019. So these metrics are great standalone and really proves that Hexagon has done it very well historically and will continue to do it well going forward. But more importantly, the increase in these metrics have created a very attractive So with this previous slide as base, let's look into how this shift in business model has increased our profitability, gross margins, EBIT margins. Adding more and more software to the portfolio has made our margins more resilient, which is and was evident this year, Q2 and Q3, we actually experienced negative organic growth. But despite that, we managed to increase the gross margin and the EBIT margin. So looking at the numbers on this slide, back in 2,009, gross margin was 43%, is now an impressive 63%. EBIT margin 15% in 2009 is now 25% in 2019. Looking then also then at the cash conversion, also a very nicely trending curve. If we disregard the anomaly in 2,009 with the financial crisis, this trend has been increasing each and every year. And today, when we are closing the books for end of September, the cash conversion on a year to date basis for year 2019 was 90%. And what we have said as a target as we grow, we should have a target of cash conversion between 80% 90%. So talking a little bit about cash flow then and looking at working capital to sales, once again, a very impressive trend. We started off at 30 plus working capital to sales back in 2010 prior to us acquiring Intergraph. Now end of September, we are at 12.7%. So yet another proof point that our strategy is strengthening our metrics. Looking at our balance sheet, our net debt and our deleveraging capacity. This is in our loan documentation today with our banks. We have one financial covenant, net debt EBITDA stipulated at a max of 3.5. We have an internal target of 2.5 and as of end of September, we were at 1.52. So that mathematically gives us a headroom on net debt of €2,500,000,000 and on EBITDA of €700,000,000 So looking at this time period, we have throughout this time period also done the 10% to 15% bolt on acquisitions each and every year. They have added 3% to 5% to our sales, which is in accordance to our financial plan. What is also evident from this graph, occasionally, we have spikes in this curve. Going back to 2014, we acquired 2 software companies in the same quarter, Verusoftware and Mintech. And also in April 2017, we did Hexagon's 3rd largest acquisition ever acquiring MSC. But what is important is the speedness, which we take down the leverage immediately after those acquisitions with the strong cash flows inherent in the core business. So to summarize this section, we've been growing the share of sales coming from software, a trend that will continue, which has been a driver of the margin expansion and also done the margin more and more resilient. Compared to 2,009, we are much less cyclical today, 10 years later. And also this transformation has also improved the working capital to sales and the cash conversion, which has enabled us to do further value added acquisitions of companies of great interest and also at the same time combining that with deleveraging our balance sheet. Today, we are at 1.5 net debt to EBITDA, which has given us which gives us a large headroom for doing future investments, both in M and A but also in R and D. So with that, I hand over to you, Ugla, to Right. It's time to wrap up this presentation, and I hope you have enjoyed it. So the big question is, will Santa come to Hexagon this year or not? This is the strategic plan that we presented to you 3 years ago. I think we held that Capital Markets Day in Central London. And we said we're going to grow from $3,100,000,000 to $4,900,000,000 to $5,100,000,000 in sales. We're going to improve our EBIT margin from 23% that we did in 2016 to 27% to 28%. So we had 2 scenarios. And we said that growth might vary between the years, and it usually does. So 8% to 10% growth on average, 5% organic, 3.5% acquired growth. And FX was expected to be 0. So what's the outcome 3 years into the plan? Well, we've grown in average 7% over the past 3 years. Organic growth has been 4%, and we've added another 4% through M and A. But we've had 1% headwind from currencies over this period. And of course, there has been other headwinds as well. We all know about what's going on, and it's not helpful for a global technology business when your 2 largest markets collide. And that's what we've seen, especially in 2019. So when we recap the situation in December 2019, we need to add €693,000,000 in top line over the next 2 years. And we need to add €270,000,000 to the EBIT. And of course, as a consequence, we need to lift the EBIT margin by 2% over the next 24 months. You're now wondering, is this achievable? What do you think I'm going to say? It's Christmas in, well, if you're Swedish, it's actually what is it? The 11th, 13 days. Yes, of course, it's achievable. We're going to do this. And you need something to plug into your Excel models, and I suggest two numbers. You need to have an average growth of 8.5% per year, and you need 39% incremental margin, and that's how we do it. Piece of cake. So in summary, what you've heard today is the tale of 2 worms basically. This is what we're faced with in the global economy. We're seeing very sluggish GDP growth in average. And it stems from several factors coinciding. Exhausted consumers, emerging market growth slowdown, automation that is jeopardizing the labor markets and so on. And basically, we see that in companies as well. Certain companies are not performing well in this environment. But then we see other companies thriving that are evolving, developing, changing workflows and so on. And that is the green trend. You can outgrow GDP if you're on the green trajectory, and that's where we want to go with all our innovation and our products. So I think you choose yourself what you want to do in life. Do you want to accept the outside world as it is? Or do you want to change it and try to be the dictator of your own destiny? And that's what we're trying to achieve. So it's a quite positive outlook for us as a group over the longer term because we think that the world is actually evolving towards us. We've been collecting technologies, portfolios. We've been trying to stitch them together. And now we're beginning to see that our consumer or customers are picking up on this message. And we started in the mining industry. We're now seeing that these solutions are gaining traction with many other customers across the world. So I think it's fair to say that these solutions might be worth some €150,000,000 within the Hexagon Group today, but they're going to grow very fast from here on. So the future is good for us. And with that, I want to thank you for coming here today and listening to us.

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