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The Factory of the Future: The Industry 4.0 Reference Factory

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Description

The Factory of the Future provides an answer to the question: What does our factory signify? Based on an international survey among 700 participants, we will present current challenges and changing requirements in the factory of the future. From a research point of view, we will evaluate the challenges and present solutions for each challenge. We will present the following technologies: smart robots, human-robot collaboration, additive manufacturing, augmented reality, production simulation, immersive training, integration of the value chain, and decentralization and production steering. We will introduce the example of the e.GO Mobile AG factory—an industry 4.0 reference factory—and present solutions for an integrated digital factory twin. The e.GO factory is a pioneer in the automotive industry, focusing on data-driven use cases to address future challenges. This example will show that the factory of the future is already here today.

Key Learnings

  • Discover the current challenges for the factory of the future
  • Understand the concept of an industry 4.0 factory and its dimensions
  • Learn how to design use cases for your individual production challenges
  • Learn about the road map for a factory of the future

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Transcript

PRESENTER 1: So I think we're all set. And we're happy to be here, and welcome you to our presentation regarding the factory of the future. This morning, we've heard a lot about the future of making things.

And in Aachen, in Germany, we are making things, especially cars. And in parallel to the cars, we also, for us, define the factory of the future, where you can see a first picture here. So our vision is that manufacturing in the future will be possible in a gym, where everything is mobile, especially regarding the automotive industry, some robots, and also manufacturing. And about that, we're going to talk about in a minute.

And we brought a reference case from Aachen, where we are developing and producing a small electric car, where we are trying a different approach to the automotive industry in Germany. And we have some kind of success. But we'll come to that in a few minutes.

First of all, where is Aachen? What is Aachen? Aachen is a mid-sized city in the center of Germany, close to the Netherlands border, close to the Belgium border. And this is where we are from-- my colleague, who is representing the [INAUDIBLE], and myself, where I'm representing the Aachen University. Earlier today, we've been called the MIT of Europe.

Were very proud of that. We're not saying that ourself. But, yeah, hopefully we can tell you something about that.

Aachen University is the biggest university in Germany. And we're specializing in engineering, mechanical engineering, electrical engineering, civil engineering. And so we've got a lot of topics covered that are also covered here at AU. And then there is some medical stuff also going on, 50,000 students in total, and a history of around 150 years, and like I said, especially focusing on manufacturing.

We've also set up something that is unique in Europe, which is called the campus concept. We call it also the engineering valley, where we are working together with companies on the future. So on future topics like heavy duty stuff, heavy duty robotics, and we founded different clusters, where companies together with us as the university are doing science, are doing research, on key topics of the future-- biomedical engineering, production, logistics, photonics, and so on.

So these are the topics, basically, that we are covering in this unique concept. So companies are located also at our Aachen plant. One of the results from our activities is this car. And this is where I'm handing over to my colleague, who is representing the company who is building this car, the e.GO Life.

PRESENTER 2: Thank you very much. We started in 2015 actually at the university campus, and founded a company which produces electric vehicle. We will show you the story later. This is our main showcase.

But this has started really, really small, with 15 people and our professor. Now, three years later, we have more than 300 people working on this concept. We just ramp up our first plant.

And today, we will show you a little bit what are we doing different, and what is our approach? And in this whole system, maybe some of the points are also interesting for you. And we identified, actually-- we identified five key learnings from that session that might be interesting for you, or that you can transfer to your own company.

The first is, what is the factory of the future about? We will show you something. What is our university background? And what is our opinion, and actually our interpretation of that?

You will understand our concept of an industry 4.0 factory, because this password, industry 4.0, but we wondered, what is that? Well, what do we understand? What can you do? What do we do?

Third is get to know our factory concept from e.GO. What does that mean, with agility? What is the connection to agility? We will explain that today. We will explain our use cases, which we realize which might be also interesting for you.

And, finally, we also re-target the challenges of the factory of the future. And there are many challenges. But we identified some of these challenges. And this will be one-- and it's a central part of our presentation.

So let's first start with the challenges. And then we actually switch to our use case. And since challenges is a university topic, he will presenting you these challenges we identified with a big survey we conducted.

PRESENTER 1: Yes, so let's start, why are we focusing on this topic, the factory of the future? Because we think it's a very hot topic. And we also have a society-- for the society, we have a responsibility as engineers to secure manufacturing, especially in a high-wage country in Germany.

And this is, basically, think about how does the factory of the future look like? There has been some research which says in the next 20 years, the world will change as much as it has during the last 100 years. And if you think about Moore's law of computing power, doubling every six months or something like that, so we're heading into that direction.

And if you look 100 years back nobody could have imagined how we'd look today. And so we are trying to get a grip on how the factory of the future of manufacturing might look like in about 15 years, in about 20 years, to prepare for that, because currently we are setting up the factory structures. Factory structures are there for 30 years, 50 years.

And we need to make sure that we have the ability to react whatever might be waiting for us, because what we have seen, or what we actually see, is that there is a lot of challenges in manufacturing that leads to, especially in an urban area, to a loss of manufacturing jobs. There is this digital economy that is rising up. And, actually, where companies in Germany have competed for talent, only with other manufacturing companies some years ago, currently these companies are also competing with companies like Google, with Microsoft, for the best engineers, for the best people.

And especially if you think, where is all the money going? All the money is going into a digital economy, because they have margins which are way higher than we can realize in manufacturing. And this is why we are working on the factory of the future, because we think there is this responsibility.

There is a lot of jobs covered in manufacturing. And we need to make sure that these are there in the future for the society. But there is hope. We have conducted a big survey on the factory of the future.

And we have basically identified also why it makes sense to work on the factory of the future. There was a big survey worldwide with about 760 participants, experts, who told us about their perspective on the factory of the future. And what we thought there, or what we have seen, that companies who we call the pioneers who have already adopted technologies of the factor of the future, which I will be talking about in a minute, they are way ahead in margin.

And they also write these topics higher. So this is, basically, there is a correlation. Causation and correlation also has to be questioned. That always has to be questioned, of course.

But we see this trend that if you focus on the factory of the future, you are more profitable already today, because you are preparing for that, or you have prepared in the past. We have basically identified eight technologies, or eight technology clusters, which make the factory of the future, which have a high relevancy.

Smart robots-- the collaboration between human and robots, because in our opinion the human-free factory is not the concept of the factory of the future. There will be a collaboration between additive manufacturing, of course we're talking about smaller series. We're talking about more complex products, more individual products.

We're talking about augmented reality, supporting the workers, supporting the people in the factory to cope with the complexity that they are facing. We are talking about simulation in production to be prepared to know the impact of the changes that might be coming. We're talking about immersive trainings to prepare the people.

And we are also talking about the integration of the value chain. So it's not only about the factory. But it's the integration of the complete value chain to make sure that we can reap the benefits.

And decentralization is also one of the topics. There will be so much to work on that we need to decentralize these topics. And also, when you look at the roadmap, when do the experts think these topics are coming, you can actually see that we are not talking about the factory of the future. But we are talking about the factory of today, because if you don't work on these topics already, or don't start working on these topics, you will be behind.

And we have seen on this slide before what the impact of thinking about the factory of the future might be like. we've. Also then broken down these technology clusters, and see, what are the most relevant topics?

Just to explain you this chart, what is the relevance today? And what is the relevance in the future of these topics? And, basically, we have identified some newcomers, and some evergreens.

So the factory of the future is not only about all the new technology stuff that's coming up. But also, we have these evergreens. We can see them here, like factory layout leadership, and especially lean manufacturing.

So if you don't control your own processes based on the lean principles, all the technology in the world won't help you. So this is still a topic. This is the basic. And then on top, you can install these new technologies to take advantage of them.

Where we basically have three newcomers. This is the smart robots. This is additive manufacturing, and especially big data and analytics. And I think we've heard about that this morning already, data in the center also done highlighted by Autodesk, where we see the future.

But for us, or in our opinion, it's not only enough to think about these technologies, or this concept which might be the evergreens. But we need a sophisticated approach, where we take all of these topics into account, and already start with them the planning phase. So only taking these technologies and bringing them to your factory won't have the impact it can have.

But you need to think about them in a holistic concept, starting with the construction integrating the value stream, up to the ramp up management. And, how do I take these technologies? And how do I do integrate them to achieve maximum agility that we need in today's environment to be successful?

And, therefore, we have developed a new planning approach. Sorry, there's some German on there, I see. But I will just explain it real quick.

We have set up this planning approach, where we take into account all the different disciplines which are involved in the factory-- so production, logistics, its organization, everything of that-- and have defined the information flow between these and how they should interact, and how they can also interact to achieve the potential of the factory of the future.

And if we talk about information flows and collaboration of people, we are also talking about data. We have learned about that before. And this is the division that we are having. So when you talk about the factory of the future, that it's fully integrated. The ultimate digital twin, we call it, in the very early phase where you can do all the simulation to understand, what is happening in my factory?

And how can I improve it based on data? And if we talk about data, we're talking about platforms. And how our platform concept for the factory of the future looks like will be explained by my colleague again.

PRESENTER 2: So I think the motivation, why we should work on this, so far is clear. I think we have enough motivation to work on the industry 4.0 and our digitalization. The platform concept-- actually, if you think on a very popular platform concepts, like UWork, for example, they really accelerate the whole process.

And our idea actually is to have a production platform, a production backbone, which is a non-proprietary platform. So it can be used from every company. It can be used from every supplier.

And we have a full data backbone. I think this is what everybody of us actually wants to have. But the reality is, it's not there. Actually, we all want to have that.

There are some products. There are products coming. Like, for example, we learned about [INAUDIBLE] this morning, which we can use for that kind of platforms.

But I think this platform idea, this is not new anymore. But you need to have a concept on how to build your data model based on that platform. And we developed the concept. And the concept is called the internet of production.

And what you see here actually is you have many sources of data from the physical world right at the bottom. So I have a machine data. You have development data.

You have also data from your user using your product over different life cycles. But what you're actually getting is a lot of data. And then you have a data backbone, where you can do this model and analytics of the data.

This is actually the second layer you need to take into account. And based on that, actually there is the use case you realize in your factory. Because you want to have apps and tools, you use based on their data, which helps you to better monitor your factory, better steering processes, and so on.

So the first idea, how we started with our concept is start at the bottom. You have the different layers. And they are not yet-- actually, the middle layer should be the platform in the end.

But we also have to take into account different cycles, because your product starts in development. Then we as a production engineer are manufacturing the product. And then our user will use the product.

And this whole lifecycle, for example, for a car is more than 15 years today. And you have to cover the whole lifecycle with your industry 4.0 approach. You cannot only cover production, or development, or the user cycle. Actually, you need a full integrated data backbone.

In our model, the internet of production is three layers from bottom to top. And then we have the three periods. And what you see here in this area, these are the apps-- event driven, autonomous, action, adaptive processes. We have different apps for different use cases in the different phases, from development cycle, production, and user cycle.

And here at the bottom, you see also the different IT systems. You have your PLM system, [? CAD ?] system, ERP system. And there are also the systems here that could be different solutions. And, today, the reality is, this is not coming from one supplier.

It's not one company supplying all systems. You have always different systems. And you don't need to manage the different systems. You need to manage the interface to your platform, whatever kind of platform that is. You need to work on the interfaces.

And this is also what we did. We built a factory in the last year. I will show that later. And we really work on the interfaces. And here at the bottom, we only took the best systems for each use case.

We are not turning around the software we have, and try to [INAUDIBLE], and place it for other, or take it for other applications. We only work with the best solutions for the task. And then we try to connect it to our data backbone.

Why do we need something like that? Because we also heard that morning, everything is changing. We need to be fast in changing. And this is one example from e.GO, we realized when we had a product development of our car.

We really started from scratch. And we only had a very short period of time. So we need something like an iterative development process.

And, actually, the digital backbone, and this digital data platform, enabled us to have an iterative development process. So we started with first prototypes, tested it. And we don't call it prototypes. We actually call that products primotypes.

It's like a minimum viable product which we test with our customers. We test with our stakeholders. And then we have a re-engineering. We get data back. And this process from engineering to production is fully automated. And this is an integrated data chain.

You see here, from first idea, concept idea of the car, that pretty much doesn't look like the car we have today. We have an iterative process with different steps. For example, this year was only for testing of the interior dimensions.

This one was for drive tests, but it still was made out of steel. Then we had a switch, to change over to aluminum. We had a change over from this different vehicular classes we have in Germany.

And, in total, we built more than 15 different prototypes, where we got the feedback, redesigned the car and the vehicle. And, finally, after 1 and 1/2 year, we had this car ready to produce. So you see, it's actually a little bit like a software product.

It's we develop a car like a software product, iterated in sprint logic. And, of course, this is needed, because we don't know what our customer wants. They said, OK, we want an electric mobility car.

And then the said, we want range. And when we ask the customer how far do we want to go, it's actually never more than 80 to 100 kilometers, in reality. But they expect to go 300 kilometers.

And this isn't a process on how to find out what the customer need is. And, yeah, it's an iterative process. And we actually, we believe that we pretty much will meet the target what our customer really wants with a high product maturity.

PRESENTER 1: And so we've had a glimpse on the car, on the electric mobility. But let's take a step back, because our journey, or our story of the factory of the future, is also a story of electric mobility in Aachen. Already starting in 2009, 2010, where our professors were thinking about the concept what's now known as industry 4.0.

But at that point, these concepts, they were so cloudy, that they said, OK, we need something to demonstrate the impact of these concepts. And at that point in time, electric mobility was a very hot topic in Germany. And so this was when they decided to set up this first company, the street scooter, with a mission of producing an affordable electric car around 5,000 euros so that everybody could have an electric car.

And we applied all these technologies in the development phase, in the user analysis that Mathias just talked about. And by this, we were able within one year to present a prototype at the International Automotive Fair, where we also got feedback from our chancellor, Angela Merkel, where she sat in that car. And her enthusiastic meter, she said, well, good job, go on. And that was the-- yeah, what we got, the contract from our government to go on with what we are doing.

But maybe more important than the chancellor visiting our booth was that DHL, and so the German postal service, which is also active worldwide, they saw what we were doing. And at that point, they wanted to electrify their fleet. And they didn't find a supplier for that.

So they went to Volkswagen. They went to Daimler Trucks. They went to Ford. And they always got the same answer, yes, we can redesign our combustion vehicle for you, make it electric.

And it will cost around 75,000 euros. And they said, well, OK, that's a lot for a small utility truck. And so they tried to go other ways. And then they phoned us at the International Automotive Fair and said, well, we really like your approach of what you're doing.

We need something different. And so they got into contact with us, and asked us to develop, together with them, this car. And we started very early in the user cycle, and really understand, what do the postal guys, the delivery guys, what do they need from a car? So as the user, what's really important to them?

And this is why, this car looks different than the usual combustion vehicles that we have in Germany. For example, the front bumper, it's a modular approach, because it's a utility vehicle and nobody's paying too much attention of how you use it. And you can just take it away, put on a new part, and it looks brand new.

So this is just one example. The door is bigger than the usual cars, because these guys, they have to get off and get on it into that car quite often every day. And, again, within around one year, we could present this commercial prototype.

And there was a great success. And the postal service ask us to do a first prototype for them, go on produce a small series. Can we build up a serious production for them? And all that was good.

And by 2014, the German postal service DHL, they decided to buy the whole company, because they liked the concept so much. And, yeah, fun fact-- last year, the German postal service was the company who was selling-- of course, they were selling to themselves. But they were selling the second most electric cars in Germany as a German postal service. So this is what electric mobility is in Germany right now.

But as they were buying this company, they were taking away our play field, where we could try something. And, of course, we got some money for that. So our professors, they took that money, and founded a new company, which is called the e.GO, the e.GO [INAUDIBLE], which Matthias was talking about already.

And we will now go a little in-depth on that company, and how we are setting up the factory of the future. And, hopefully, the video will work to give you a first impression.

[VIDEO PLAYBACK]

- What will the future be about? About unknown technology? About safety? Or simply about fun?

Maybe. We don't know. But we believe that whatever will happen, it will still be about people, about their individual lives. Our technology makes life of people less complicated, because we build cars that suit the needs of a city.

And we won't wait for tomorrow. We already have the future on stock. We build future mobility, now. We are e.GO.

[END PLAYBACK]

PRESENTER 1: So I think you saw many of our products already. Actually, we have a three products you can drive. It's a fun cart. This is for fun.

We have the e.GO Life, which you saw before. But we also have the e.GO Mover. And the e.GO Mover is an autonomous shuttle, which we are developing and producing together with a big German automotive supplier.

And we at e.GO want to redefine the mobility in the city. So we are not only thinking about building a car. But our motivation is to change mobility in the city. What you see here is one of our concepts.

In the background, you see Aachen. And there is one big road. When you come to Aachen, you will go there. And you drive through this road.

And our vision is that we create mobility hubs close to this big roads already running into the city. And at this mobility hub, actually you park your car. And you switch over to very flexible mobility solutions. For example, to our Mover, you can rent an e.GO Life, for example, to go in the city. Or you take one of our fun products we are developing.

So we really want to change the vehicles you use in the city. But we also want to change how you use it, and actually developing an on-demand system. So our goal is that we develop cars that are fun, practical, and affordable. So affordability is very important for us, of course, because electric cars today, they are very expensive.

So this is one of the reasons why we don't see so many electric vehicles on the road, especially in Europe. And to get this affordability, we have some enablers, which I will show you later. Our car, the e.GO Life, costs 15,900 euro. This is very cheap compared to other products on the market.

You see the smart Volkswagen concepts, Renault, Nissan, BMW, these are all very, very good concepts, and very good cars. They are much more experienced than us. But they are actually too expensive.

And the way how we produce a car is not very efficient for a big OEM. So we do it a bit different. And we are not saying we do better. The cars are all good. And they will have a lot of success changing the market.

But especially for this inner city transportation, we develop a car concept which is affordable. To reduce the costs, we have different concepts. One of these is the factory. We need one of these, an enabler.

The factory concept is actually one of our core enablers. We need an agile concept, because our product is still changing. We cannot invest so much in our factory. We only invest 12 million euro.

We rent the building. But it's designed and planned by us. But it's not built by us, and not the investment. We invest 12 million euro in all the equipment.

The total investment of the factory is 30 million euro. This is nothing compared to a conventional automotive plant. We are mastering, or we are developing, processes for efficient, small series production. Small series means up to 30,000 cars per year. And this is our target we want to produce.

And we need high scalability from prototyping to 30,000. Yeah, this is a long way in manufacturing, but a short period of time, because we need to bring the cars to the market. And our target is that we have a high international transferability of auto concepts, because the core market for electric mobility, it's not in the US. It's not in Germany.

It's in Mexico. It's in China. It's maybe in India. It's totally different markets. We want to copy our principles, our factory, our car, and then have something like a license-based concept.

Our car is different. Our car has an aluminum frame. And then we just attach plastic exterior components. So this is very cheap, very cheap in tooling, very cheap in assembly. And you don't need a press shop.

And you don't need a paint shop, because the plastic is already colored. And [INAUDIBLE] forming process to get this nice exterior. And because we are changing the value chain, we can change the production concept. We don't need the high unit number, because our investment is so high.

So we can reduce the unit number. And then we take our industry 4.0 approach on with our factory together to reduce the cost. We still have a very manual assembly process.

It's not automated. You will not see any robot in our factory, because the invest is still too high. And it's too complicated.

But the IT system is fully integrated. We have 100% documentation of our assembly processes. We are fully connected with our suppliers.

And we use our data with our intuitive production framework to optimize our processes. And this is how we actually reached this 15,900 euro, because this reduction of the value chain cannot be copied by a big OEM very fast. Maybe they also have same, or similar, ideas.

And also this terraforming and this aluminum structure, this is not cost-effective if you produce 200,000 Volkswagen Golf. You still produce these cars in the conventional way. But if you only produce 10,000 or 20,000 this is a very interesting concept.

Another enabler is the development cycle. I already said that we tried to shorten the development cycles by an iterative approach. And we increasing our product maturity when we start. This is also very cost effective in the end, because we don't need very big overhead of engineers designing our product.

We are trying to develop and innovate very fast. And here, you see a picture of the aluminum space frame. This is a very, very nice one, where you see this at aluminium profits.

This year, for example, is for the [? crash. ?] So we meet all the requirements that every car has to meet. We are no exception. From the legislative side, we have to fulfill all the requirements. But the concept is a different one, but it works.

But when it comes to the factory, the factory itself is also an enabler. And the way we use tools, and the way we plan factories-- we both are factory planners with a university background. And we try to bring the methods we learned, or the methods we developed during the last years, and our institute developed over the last 30 years actually, we try to bring these methods into this use case.

And, actually, this use case is the first time we are building our own factory. All the other years, I think over 30 years, we build factories for other companies. But this time, we are building our own factory.

So this is the unique opportunity to use everything we have to use it in a daily project. And, of course, we are here because we are using Autodesk products for our factory planning process. And, actually, there's always two dimensions when we talk about industry 4.0-- all the data you have from different machines. But you also need a visualization, and the connection to the building, connection to your assets. And this is when we started to use the Autodesk products.

Actually, we have a very basic approach. We started in 2D AutoCAD. But we also have a PowerPoint tool we use. We do lots of workshops like you do with your experts, when you start thinking about your layout in the factory.

Most architects in Germany, more than 70%, using Revit. This is very, very popular in Germany. I don't how it is in the US, if the numbers are similar. But we started with Revit, when it comes to our building.

And then we use Inventor for our production assets, for our process design. And we integrate, like Mathias explained in our vision. Our vision is to have a fully integrated twin. So production, layout, building, and then all the data we get from the [INAUDIBLE].

We want to integrate this. And we use Navisworks to integrate Revit into Inventor. And then always of course we do some visualization. And we use visualization into virtual reality, especially to validate what we've planned. So in the planning phase, but also in the realization phase.

This is our factory model. You see our factory here. Maybe I can give you a short explanation what you see. This here is our main assembly line.

It has a 28 stations where we assemble the car. The car is placed on an ATV. We will see this later. So it's fully autonomous driving through the factory, 28 stations.

Here is the testing and finish line. We have a rework and finish area here, and the car will leave here. All of this is logistics. We have a supply process.

And what you see here, for example, we have different maturity levels in this model. On the left side here, you already see what is already there. Like, this machine, it's a rain test cabin. We got this machine like three weeks ago.

It's there. We got the data from our supplier. But at the same time, you see some open space here. These are some areas where we still have a very low level of maturity.

Even today, we are in the planning phase. This is the pickup from the autonomous ATV, picking up the frame. This station is not there yet. We just do it with a forklift, manual, because we are not at the point where we can automate this process.

So in our factory model, these are still open areas here where we start to plan. And if we start planning, we just put some squares, blocks. They are very simple geometric forms here to make reservation for the area, to test our flow, to validate our processes. And once we get the data from our supplier, we integrate the real data.

And so this is how we move through the process of building up the factory. Here on the right side, this area is especially for pre-assembly components. And the main line here is then for the final assembly, where we just assemble the different components we get to our frame.

I always talked about the digital flow through our systems. This is an example from us, how we get the data from engineering to the shop floor. This looks maybe a little bit confusing. But I will guide you through this slide.

So let's start in the top left side here. This is engineering. We are evaluating design. We are developing the car.

And we are developing, actually, in development, bill of material. You all know the bill of material from the development side. And what we do is we create a manufacturing bill of material, of course. And then in the third step, we plan the process steps. I think everybody, if you're knowing that kind of process, and this is-- in our company, this one is fully connected.

And we also have the 3D visualization attached to that. So we have assembly instructions with a visualization. And this is directly, there's a constant flow through the system.

And then we have a switch from planning to operation. But it's connected. So here's our ERP, and our manufacturing execution system. And it's connected to this development process.

And the last step, actually, is the operator's screen. So we have from development room, manufacturing room, process steps, assets, like working instructions, illustrations. Then we go to the ERP system in the reality.

In the ERP system, there is the customer order coming. And we have this 150% manufacturing boom. We get it to a 90% or 100% boom. And then it's going to the operators screen.

But it's not a dead end. But here on the operator screen, we can give feedback that is a ticket system, which generates tickets with the right numbers, with an illustration, with a short description. And these tickets pop up here at the development side, or here at the process planning tool so that we have actually a closed chain of feedback from the operation to development.

And we need this, because our product is still developing where we are already producing cars. So today, we have produced-- we started in August. We produced around 60 cars in our factory. We are ramping up now.

We start the series production in January. And we already have 3,000 preorders, which we will produce until the end of June. And this is very, very helpful, if you are in this ramp up phase, and you're still developing. You get the feedback. The feedback directly goes to the engineer.

And we support this by different systems. And based on our industry 4.0 approach, we have some MES system, ERP system, warehouse management system. We use like a middleware platform to connect all the systems. And then we have our different apps from a KPI app, quality app, operator, cockpit, logistics, handling.

We are developing of our own app space on that platform. And we need this, because we really want to optimize our processes. For example, logistics is one very good example.

Our vision, or our principle, is that we don't touch any component more than two times in our factory. Actually, it's delivered. We do all the documentation. We scan it.

We get the numbers. And then it's actually directly going to the assembly station. We are working with our suppliers on that to really minimize material handling in our factory. The warehouse is a little bit smaller than our production. And we really try to optimize these processes.

We want to have a red button factory. It's also our vision that we can push a button, and really see, where are my components? In which process steps, which phase, which status? But we can also simulate in our processes.

This is the next step. When we have fully transparent processes, we can simulate how the process would behave if we changed some of the conditions. We are working on a 5G network in our factory, which will be realized very soon. It's a very, very interesting topic to really get rid of all these data cables we need, all the connections, and to make it more flexible and more easier. And we work on this electronic vehicle file so that we really document all assembly steps, all parameters we need, to show that we have built a very good car, and a fully documented car.

When it comes to investing, your see here we have very complex systems. They are ATV, but only one. Most of the other equipment we buy for our [INAUDIBLE] are very basic equipment, which is flexible, and which is designed for 10,000 car per year production, and not for 100,000 per-year production.

So we can use other infrastructures. We can use other equipment, compared to a normal automotive company. And this really reduces our costs in [INAUDIBLE]. Here is 8.6 million for this equipment. And then we have another, around 3.5, for building-related investments.

PRESENTER 2: Yes, so now let's have a look into the factory. You see here that we're using virtual reality to validate assembly processes, to test assembly processes, where we have our operators to validate an early phase to put them in the virtual reality model. So the complete factory is built up as a virtual reality model, where they can have a look at their workspace, simulate the first assembly steps, the first assembly processes, to make sure that everything we want to build there actually does work.

So a digital validation on all the processes, this is one of the use cases that we are having. Now--

AUDIENCE: [INAUDIBLE]

PRESENTER 1: New product, new process, and new people. And we need some support to speed this up. Otherwise, we would never make the ramp up to the 10,000 per year.

PRESENTER 2: Yeah. Now, we go one level deeper. You see here, how the factory actually looks like. We see here, the ATVs that we're using for transporting the car. And everything here in this factory, on our factory of the future, is designed for agility.

You see here, for example, this is [INAUDIBLE] construction. So we do don't have anything hanging from the rooftop. Everything is flexible.

Also, the conveyor system is very flexible. We can reroute whenever we need to. And also, like Matthias said, we are looking at low [INAUDIBLE], because we are a startup. We do not have the financial resources that big companies do.

And so this is why we are using this new construction, because we still need it for tools and everything. But other than that, the assembly hall, the complete factory, the building, it looks like it's an empty space like the gym that we saw in the very beginning. So this is how the loop is closing at that point.

And like you said, we do not have many robots. We were talking about them earlier in the factory of the future. But this is also an important takeaway.

Check what you need. So not all the technologies are relevant for every kind of manufacturing process. So really check where the value lies.

And for our concept, the value really lies in this agile process in the digital backbone that Matthias talked about, where we can get the feedback from the operator back into engineering to make sure that we have an agile system, where we can react to whatever is happening in our factory.

PRESENTER 1: And I think it's also very important to see that we started with the Autodesk products not more than a year ago. We just got started. We have many students.

They are very motivated. And we come to this in the last slide. We just give them the software, and say, OK, here, just try out here.

Of course, we have some experts close by helping us when we get in trouble, and have the right people to ask when we are in trouble. But we are also new to this world, to the BIM world. But we have a very high use of that, because we can connect this model with our digital backbone we already have.

And this is what we want to do in the next year, actually really is to bring together the worlds. We already bring together Revit model and Inventor model. We have a digital backbone. And we will integrate that in a fully twin of our factory.

I think it's still a big step, because it's not easy. But it's a step we will make. And we deeply hope that next year, we can show the fully integrated model with the late life data we have integrated here.

This is the next step to have a fully twin. And, of course, if you talk about that agile processes, we are talking about people, and about many motivated people we have in Aachen. You see our team here. We are growing very fast.

This is from July. From July to now, we have already around 60, 70 more people, 15 to 20 people starting every month at e.GO, full-time employees. And this is a real challenge for us. So every time we think we are in a good process, we are growing that fast, that every process we had until there needs to be redefined because everything changing.

Not only the product, the process, but also the people we have. But our most valuable resource are the people, and only very motivated people, and many young faces here. They are able to work in that agile environment we created.

Honestly, for me, it's not easy. It's never easy. And it's a lot of pain, because also good concepts need to be revised. Maybe you cannot realize your ideas. But it's never ending. We have many, many good ideas in Aachen, not only at e.GO, also in Aachen.

PRESENTER 2: And what we've shown here is just one of our success stories, where we are creating this car from scratch in less than three years, to a point where we have a factory now where we are selling the cars now. And because of this campus concept that I introduced earlier, where we working with companies, everybody is invited. If you have a footprint in Germany, or maybe if not a footprint in Germany but want to be active there, we would be happy if you contact us, and invent, and also make the future together with us.

And with that, we're closing our presentation. There's some room for questions, I think. We have about five to 10 minutes left. [INAUDIBLE] can, of course, leave. And otherwise, we're still here for questions.

Hope you enjoyed it. Hope you learned something facts about the technologies in the factory of the future, and also how we apply them regarding our car, the e.GO Life. And we hope you enjoyed it. And I wish you a good time here at AU. Thank you very much.

AUDIENCE: So if you're [INAUDIBLE] and changing [? process ?] or procedure, do you have AR training for your engineering staff? You mentioned training for the factory. Do you have training modules for [? engineers ?] as well?

PRESENTER 2: So for our operator, we have a 10-day onboarding week. Within that 10-day, they get our full e.GO spirit. First of all, we try to transfer the culture, first days.

And then we have a assembly training in virtual, but also then in reality. It's like a 10-day program. And then we bring people to the shop floor, and let them do the work.

But with this IT-supported process, we have a different level of working instructions. Every worker, they have a batch. They identify themselves at the workstation. And depending on your experience you get more or less instructions.

It's like a qualification metrics for each of our worker. And when they are new to a process, for example because another guy is on holiday and they need to jump in at a station they never work in, they get more detailed work instructions. Or they can request additional information. And with this different level of experience, we can train people very, very well.

PRESENTER 1: And if we talk about engineering, also there is a huge culture change, if we bring in people with experience already in other companies, because like he said, we are developing the car like a software product. And so that's totally different from what the OEMs in Germany are doing in the car industry, or in the automotive industry.

And so this a huge culture shock, where we also have to send them through training in agile methodology. So it's challenging. But it shows success. So it's worth it.

PRESENTER 2: More questions?

PRESENTER 1: Yeah?

AUDIENCE: Do you see-- is there a latency between no experience or smart young engineers and people who've been in the industry for a while-- 5 to 7 years?

PRESENTER 2: Depends on the background. The guys who worked in the industry, mostly are very, very experienced. And we need those guys, because they have very, very valuable knowledge.

And we are not able to get this knowledge in that short period of time. So we also need experts. But some of the experts are not happy when they work at e.GO because everything is changing so fast. And actually, they are focused on doing their things 100% correct.

This is maybe a German attitude, doing things very correct, and strict to the process, and getting the best results. But at e.GO, most of the time we have to live with an 80% result. These companies working together with us, they are really experiencing that our way is different.

We cannot give the full specification before they start working. We ask them to start working, and then maybe get the specification. If not, we take the best guess, realize it. And then we have to revise it.

And then we have many, many challenges in contracting, in paying, in costs and everything. But we manage that. And we have some partners. And some people can do that.

And some others are not that flexible. So then maybe e.GO is not the best customer. But, in general, it's working very, very well.

PRESENTER 1: And if we talk about these people, young or experienced people, it's just a different way of onboarding them, because on the one hand, these experienced people, they need to be at least some kind of open to the new way of doing things. And I think this is where the young, smart engineers, like you call them the students who are just coming in, where they can also help them, because they are very inspiring.

But at the same time, they need these experienced guys and the experienced leadership, because if you want to bring the current street, there is a lot more than smart concepts and trying everything. But because they've worked very hard regulations on that, and this is where the experienced guys come in. And I think this is a very, very fruitful combination of young and old, or young and experienced people I would say, that we are having. But it's challenging to get this kind of change into some people.

PRESENTER 2: Yes?

AUDIENCE: What is your timeline for reaching your goal? 12 months? 16 months?

PRESENTER 2: So 10,00 per year, for us means 45 cars per shift. The 45 cars per shift we want to reach in August next year. So we will produce more than 10,000 cars in the next year, because we will have a two-shift or three-shift operation.

But the cycle time, we plan to reach in August. Right now, honestly, we are around one hour cycle time we can realize. But it's maybe conception things that will change until January so that we are fast, especially gluing processes, tolerance processes, et cetera. And then we will make this big jump.

And we will start with a 45 minute, 30 minute, 20 minute. And then we will go for this last steps that we reach 10 minutes next year.

AUDIENCE: So you're not hiring anybody from Tesla right now, right?

PRESENTER 2: We have one guy from Tesla. He is responsible for our production infrastructure, IT infrastructure. But occasionally, we didn't hire them. He wanted to go back to Germany, because of his family.

AUDIENCE: So when you [? hit ?] your production rate-- so am I understanding you right? If your [INAUDIBLE] or your time to produce the cars [INAUDIBLE].

PRESENTER 2: 700 in total, 600 to 700, pieces per car. But we have around 300 components we need to assemble.

AUDIENCE: [INAUDIBLE]

PRESENTER 2: No, no, in 28 stations with 10 minutes each. So in total, per car, the assembly time is 10 hours. 20 stations, 10 minutes per station, two people per station. And this all multiplied will be 10 hours. Yes?

AUDIENCE: [INAUDIBLE] virtual reality and using that in your training. How did you [? do ?] that? Was it actual pieces that they're picking up [INAUDIBLE] parts or was that kind of like a [INAUDIBLE] where they were moving just stuff around? Or were they just in the factory?

PRESENTER 2: No, actually, we have a virtual reality training. We showed the guys at the workstation. We have the components in the model. I think maybe we have this so you can see that.

I think we can see that-- actually, it's a model like that. We have the components, which are assembled at the station we have there. And here, you see that this is like a pre-assembly line with the right jigs, the right tools, and the right components.

And then we can do some simulation. But we can also do it in our working instruction, because we have this manufacturing bill of material planning, and the process planning. And we can show how the components will be assembled.

And in one-day training, we guide them through all processes they will see at their station. And the next day, they can try out this in our protoshop. They have assembly training for two days in our protoshop. And we actually practice as long as the guys need.

Some are faster. Some need a little bit longer. And then we transfer them to the factory.

AUDIENCE: Are some components pre-assembled [INAUDIBLE]?

PRESENTER 2: Yes.

AUDIENCE: [INAUDIBLE]

PRESENTER 2: Yeah, the frame is welded, of course. Then we have some pre-assembled, for example the battery system we are not assembling on our own. We want to, but not today. We start with a supplier.

The big pre-assembles we do by ourself our front and rear axle, drive train. And then we have some smaller pre-assembles. And all the other components arrive pre-assembled, mostly.

AUDIENCE: When you're doing the virtual reality [INAUDIBLE] assembly process, does the computer measure [INAUDIBLE] a process chart for that?

PRESENTER 2: No, not yet. So we work on getting that simulation better.

AUDIENCE: So you're working on getting the simulation better?

PRESENTER 2: Yeah, the special reality, we are working on getting it better, and more realistic.

AUDIENCE: Right, so [INAUDIBLE] so with that, you create a process chart on the motions that they ended up doing. The question I had was does the computer, when you're doing the simulation, actually measure the distance and the motions that somebody's doing when they're doing it?

PRESENTER 2: No, it's not measured.

AUDIENCE: [INAUDIBLE] But the computer doesn't do it. You got to do it by hand.

PRESENTER 2: But we can validate. And, of course, we also do real assembly training, and processes, and economic concepts. But we try to do it in virtual reality.

But, mostly, it's easier to just assemble the parts, and see the workstation in the life environment. Mostly, it's easier. But once we have a running production we cannot use it as a test at the same time. So we need a virtual reality, because we have to prioritize running production. OK?

AUDIENCE: How do you feel about [INAUDIBLE]?

PRESENTER 2: Autonomous assembly.

AUDIENCE: Autonomous assembly. [INAUDIBLE]

PRESENTER 2: So we have autonomous transportation. We have very flexible-- for example, if you have variants, we can just add to the stations. For example, if you have a sport version, or a convertible, we can have additional stations.

But we don't think that assembly will be fully automated, because then you are in this process where the assembly is very expensive. You need very high unit numbers. And that makes you inflexible, in the end. So we think there will still be people in the factory also in 20 years. But the way they work is different.

PRESENTER 1: And that's what I said. You could go to the Tesla way and trying to automate a lot more. For us, it doesn't make sense because we have these smaller series, high variance, where it's just cheaper and quicker to do it with people, because in the end, I mean, the human is also a robot with different arms.

And they're way more flexible than a real robot. And this is why we are going away from the high physical automation, and more into enabling the people to fulfill the assembly operations as good as possible by being supported with augmented reality, assembly instructions according to their needs or to their skill level. And so this is the way we are going, and also planning to go for the next years.

PRESENTER 2: I think we run out of time.

PRESENTER 1: We will be here discussions. But then the next presenter can already prepare. So thank you very much.

______
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We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
LinkedIn
We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
Yahoo! Japan
We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
Naver
We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
Quantcast
We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
Call Tracking
We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
Wunderkind
We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
ADC Media
We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
AgrantSEM
We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
Bidtellect
We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
Bing
We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
G2Crowd
We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
NMPI Display
We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
VK
We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
Adobe Target
We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
Google Analytics (Advertising)
We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
Trendkite
We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
Hotjar
We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
6 Sense
We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
Terminus
We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
StackAdapt
We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
The Trade Desk
We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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