Neoception and ECLASS have partnered to lay the foundation for the digital twin for manufacturing companies, machine and plant manufacturers, and operators. The partnership aims primarily to establish standardized product descriptions and classifications using the concept of the administration shell (see below for explanation). Because transferring and interpreting inconsistent data is costly – keyword: IT interface costs.
Podcast episode summary
A key problem in practice is the inconsistency of data points from different systems. Example: If a machine in China and another one in France use different names for the same data points, this leads to high engineering efforts (data transfer tasks) and time losses. Manual efforts, misunderstandings and errors can occur to make data usable for the implementation of different use cases.
Also, in many cases today, a clear classification of products and services is missing, report Thorsten Kroke(Managing Director ECLASS e.V.) and Alaettin Dogan (Technical Consultant at Neoception) in this podcast episode. As a result, products are not fully comparable and duplication and manual data transformation can occur. This costs time, money and ties up resources unnecessarily.
ECLASS, as a worldwide ISO/IEC standards-compliant data standard, solves this challenge together with Neoception. Products and services can be clearly described or classified with the industry standard – a uniform semantics emerges. Once the data has the same meaning and structure, it can be correctly interpreted and used for many different use cases in all IT and IIoT systems. The Neoception® Asset Administration Shell provides the appropriate software modules and associated professional services.
Use Cases in the Podcast
Use Case 1: Digital nameplate for machine maintenance
A digital nameplate is an innovative solution for improving machine maintenance. Instead of relying on physical labels and manual data entry, the digital nameplate provides instant access to important information by scanning a QR code.
Imagine a maintenance technician standing in front of a complex machine that needs to be serviced. Previously, they had to search for the necessary information in written documents or in various systems. But now they can simply scan a QR code on the digital nameplate of the machine, and a structured overview of all relevant information is immediately displayed. This overview contains all documents, certificates and maintenance information stored in the machine’s administration shell.
The added values of this use case
- Increased efficiency: The technician saves time by no longer having to search for information in different systems or documents. This results in faster and more efficient maintenance and reduces machine downtime.
- Consistency and reliability of data: Since all information is stored centrally in the administration shell, it is always up-to-date and consistent. This minimizes the risk of errors due to outdated or inconsistent information.
- Environmental friendliness: Using digital nameplates instead of paper documents reduces paper consumption. This is a tangible contribution to sustainability, as fewer trees are needed per year for paper production, for example.
- Integration: The digital nameplate is compatible with existing systems, so no additional data entry is required. This ensures that all data already maintained in the inventory system can be used efficiently.
Use Case 2: Product Carbon Footprint – Determination of the carbon footprint for a product in the context of the EU Green Deal.
Under the EU Green Deal, it is planned to record energy consumption and the carbon footprint per product. This means that every company, whether large or small and medium-sized enterprise (SME), will ultimately be required to monitor and report on these metrics.
For this use case, a company would use the ECLASS standard features to calculate the energy consumption and carbon footprint of its products. This information could then be stored in the Neoception® Asset Administration Shell used to understand the company’s overall impact on the environment and develop plans to reduce that impact.
The added values of this use case
- Compliance: By calculating and reporting these metrics, a company can ensure it is compliant with EU Green Deal regulations and avoid potential fines.
- Sustainability management: Determining a product’s carbon footprint and energy consumption enables the company to manage its sustainability goals more effectively and take targeted action to reduce these values.
- Transparency and trust: By disclosing this information, companies can strengthen the trust of their stakeholders and customers and position their brand as environmentally conscious.
The EU Green Deal aims to make Europe carbon-neutral by 2050. It offers a range of benefits, including promoting green innovation, creating sustainable jobs, improving the health and well-being of citizens, reducing energy dependence, and strengthening Europe’s competitiveness. By recording and reducing the carbon footprint and energy consumption of their products, companies can make an active contribution to achieving these goals.
Classification of the topic: Digital twin > Asset Administration Shell > ECLASS
The Asset Administration Shell (AAS) is an important component of the concept of the digital twin of operations in the context of Industry 4.0. The first demonstrators for a core standard of a Digital Twin were presented in 2019, indicating the increasing importance of this topic.
The year 2020 was then marked by increased involvement of various companies, which approached the associations VDMA (German Engineering Federation) and ZVEI (German Electrical and Electronic Manufacturers’ Association). This movement was driven in particular by the Industry 4.0 platform, which was created as a result of the entire movement around Industrie 4.0.
Due to the growing interest, the development of a standardization of the digital twin by means of the administration shell was made a project. Founding members were sought and found to support and advance the issue. To this end, discussions were held within the VDMA and ZVEI associations and with end users to ensure a broad basis for the development and implementation of the standard.
Thus, Asset Administration Shell has its roots in the Industry 4.0 movement and is a central building block for the successful implementation of digital twin concepts. It serves to standardize and thus enable more efficient and uniform use and implementation of this technology in various industrial sectors. Read an application example from practice with VW Sachsen GmbH
The Asset Administration Shell (AAS) is now used in combination with the standardized product classification of ECLASS. ECLASS achieves the correct classification of products and services through error-free product master data. Neoception’s digital twin software expertise adds significant value to companies looking to utilize digital twins in their operations.
A partnership for the future: Neoception and ECLASS
The partnership between Neoception and ECLASS addresses these issues and offers solutions for various use cases, such as the digital nameplate, the carbon footprint per product or the digital product passport. The introduction of standards and cooperation with the IDTA will make these processes even more efficient and secure.
ECLASS serves as a global data standard that clearly describes and classifies products and services. The industry standard conforms to ISO/IEC standards and enables uniform preparation and international digital exchange of product data.
Neoception, as a partner in this equation, provides the technology and services to provide the data. With software solutions such as Core and Stream and a range of software building blocks (templates), the company enables mapping with the ECLASS data standard and closes the gap between sensor data and business processes.
Neoception was founded in 2017 as an IT consulting company by the Pepperl+Fuchs Group and has become a relevant IT service provider for the design of digital process optimization in the industrial environment.
ECLASS is a cross-industry global data standard that uniquely describes or classifies products and services. This industry standard is ISO/IEC standard compliant. With an ECLASS classification, product data can be uniformly prepared as well as translated into multiple languages and digitally exchanged internationally. Important product features and information are transmitted to the customer in a uniform data structure.
This podcast episode shows how the fruits of this partnership can lead to significant business benefits by increasing efficiencies and reducing costs while meeting the requirements of stakeholders such as legislators.
Neoception’s and ECLASS’ membership in the Industrial Digital Twin Association (IDTA).
Neoception and ECLASS are members of IDTA. IDTA is a platform committed to establishing and advancing the industrial Digital Twin concept, a concept that creates a virtual imprint of a physical object or process to perform analysis, optimization and improvement. Here, the 2 members help shape the standard, use the network, and work closely with other members to access knowledge and resources from the association. Together, they can thus sharpen market positioning and strengthen the application and further development of the industrial Digital Twin concept.
Episode 101 at a glance (and click):
- [09:35] Challenges, potentials and status quo – This is what the use case looks like in practice
- [19:46] Solutions, offerings and services – A look at the technologies used
- [31:48] Results, Business Models and Best Practices – How Success is Measured
Many people think of simulations when they think of digital twins, which is not wrong at first, but it is not all there is to it. There are about 20 use cases that we are discussing intensively in the context of IoT, especially with our network centered around the digital twin. Today we are looking at the Digital Twin from the perspective of products, or more precisely: product descriptions. Simplified example of this, we have two friends, one has created an Excel spreadsheet in China and entered some characteristics of the machine there in individual columns, such as type designation, nameplate, product information, units of force, kilowatt hour or temperatures. Now the second friend does the same, but in France.
So now one says to the other: “Hey, we’ve implemented a really cool project here. For the first time, we can now determine the carbon footprint via energy consumption for a specific product at our company.” Says the other one in disbelief, “We need that too! Let’s get this project started right away.” The challenge they have now is that all these features of the two machines are named quite differently in the Excel spreadsheets in the column headings. Now they have to get together online first and define the column headings.
Well, with 45,000 product classes and over 19,000 features, this can take a while, so to speak. How do you actually overcome this challenge? How do I actually turn Excel data into real IoT data? What other added value can I leverage from the data? With which use cases and what is important to consider during the implementation? That’s exactly what Thorsten Kroke, Managing Director at ECLASS and Alaettin Dogan from Neoception explain to us today. They are partnering up today and with that I would say, let’s go!
First of all, hello Alaettin, hello Thorsten, great to have you with us today and welcome to the IoT Use Case Podcast. Alaettin, we’ll start with you. How are you doing? And where are you right now? Where are you off to?
Yes, hello Madeleine, thank you for the invitation. I’m in Steinfurt near Münster. I work from home. Mannheim is a bit far away for me, so working from home is the optimal solution for me.
But your team is based in Mannheim?
Our team is based in Mannheim and a large part of it is also in Portugal. We have a lot of software developers in Portugal.
You could also do some coworking there. Glad to have you here. Thorsten, nice that you are also with us today and also took the time. First of all, how are you doing and where are you right now?
Yeah sure, thanks. Excellent, today I am actually working from home The last two days I was in the office in Cologne, and before that I was on a business trip during the week. I’m happy that you can go out and meet people again and also do business locally and talk to people. This is super important.
I’m glad to hear that. I am also looking forward to the next trade fairs and to meeting our IoT Use Case network on site again. That’s always nice because you really do a lot online. But I’m looking forward to seeing everyone on site again. But then maybe let’s jump right into the topic. Alaettin, a quick word about you, about Neoception. And then we can actually jump right into the use cases and practice. You were founded in 2017 as an IoT consultancy by the Pepperl+Fuchs Group and have developed very rapidly as an IT service provider for the design of digital process optimization in the industrial environment. Alaettin, you are a Technical Consultant at Neoception. Can you share with us who your clients are and what clients you work with in your department?
I am a Technical Consultant, to the outside world I represent Neoception in all topics and areas related to Digital Twin. So I’m out at different trade shows, different conferences, I’m involved in different working groups. Especially at the IDTA, where it’s all about the emergence of the digital twin. I’m involved in various working groups there. In principle, in all events that concern the topic of Digital Twin. Internally, I am responsible as a product owner. We have our own product with the digital twin. That’s where I get to define “what” we’re creating. Our customers are mainly the manufacturing sector in the industry. Here we focus primarily on small and medium-sized companies.
Very nice. I would also come back to the IDTA in a moment, because that might fit quite well for those who are not yet familiar with the IDTA. I always talk about use cases here on the podcast. We don’t want to talk so much about the technology itself, but really explain the added value of the technology from a practical perspective. Can you tell us a bit about what use cases you generally serve at Neoception? And which ones are we looking at in detail together with ECLASS today?
Yes, very much so. As an IT service provider, we support our customers in creating digital solutions for process optimization, especially in an industrial context. And there we have two areas. Smart Production, for one. The main focus here is on optimizing intralogistics processes. For example, we have our eKanban Light product, an automated, paperless Kanban system that simplifies inventory management and optimizes the flow of materials in modern operations.
The other area we cover is Smart Product. That’s where we offer solutions that deal with the topic of Digital Twin, i.e. solutions for the simple creation of production assets. This is the area we want to look at in detail today.
That’s actually the perfect transition to Thorsten and ECLASS. How did you two actually meet? Was this a joint project or a classic sales process?
Yes, this was indeed a joint project. I think that was 2016 or 2017. ECLASS has been around for a long time, very successfully, since 2000. And Neoception is a spin-off from Pepperl+Fuchs. A very successful spin-off for software-related issues from my external perspective. And Pepperl+Fuchs has been very active in ECLASS e.V., in standardization, for many years. That’s how we came together in a project and noticed right away: Neoception, super smart, ingenious solution, great software. ECLASS itself offers semantics, i.e. structures to represent data. It’s a perfect match.
Every now and then, we have had projects that are very innovative in nature. And now I’m not surprised that Neoception is very active in IDTA, very active in the topic of data exchange with automation, because they understood ECLASS and the use cases. That’s why I incredibly enjoy working with Alaettin and the colleagues.
Very nice. Because it was mentioned for the second time: IDTA. You can listen to episode 80, because IDTA was already a guest on the podcast, that was Christian Mosch. In episode 80, Dirk Thieme from Volkswagen explains how they approach the whole issue and what the IDTA does. Today we are talking about semantics, i.e. the meaning of data. You already said it, you from ECLASS, you have been successful since 2000, especially internationally. You are a non-profit organization with members from companies, associations, institutes, but also various branches of industry represented there. You are developing the leading digital data standard for product descriptions. You are focused on, above all, clearly describing and classifying these various data. We’ll find out exactly how this works in a moment. To begin with, what exactly is your vision? What are you driving forward in that regard?
Yes, ultimately, I think there needs to be a language for data to enable this exchange of data. This means that in every use case we have to deal with data exchange, and this must take place with as little loss of information as possible. Otherwise, I have to provide the data manually or there will be a misinterpretation. This can only be done in a standardized way, digitally if possible.
ECLASS is one of the few standards in the world that work independently of industry and, above all, are ISO/IEC compliant. Our vision is that all data is semantically defined and described according to ECLASS.
Very beautiful vision. It is about the data standard of product descriptions. Let’s go into detail about that now. A short note, you both are also represented in our network. That means, if you now think that this is exactly your topic and you want to learn more about standardization, feel free to contact Alaettin and Thorsten, either on LinkedIn or directly. Neoception is also represented in our network, we have the contact persons there. You can also just directly make an appointment.
[09:35] Challenges, potentials and status quo – This is what the use case looks like in practice
What I always find super exciting is talking about this “why” in particular. Why is what you do important and what are your clients’ business cases in particular? Can you share a few use cases from your customers, how they are using this and what you are doing? What use cases do your customers implement?
There are a lot of use cases, but we are still at the very beginning. Especially in the field of SME’s, the digital nameplate or product documentation are the classic use cases. Example: A maintenance on a machine is pending and I need to get to documents by rummaging through a stack of documents. There I have for example a QR code on a machine that provides access to the machine’s data. Documents, technical data, documentation, etc. This data provision is a simple use case, but enormously important. Making the existing data that resides somewhere in the systems available.
By nameplate, do you mean the nameplate that is simply printed on the product itself?
Exactly, the Machinery Directive stipulates that machine and machine parts must contain information on the product. This information can take on huge proportions. Especially with small products, you come up against limits. The digital variant is a good solution for this, which also saves time, money and material. If we use ECLASS we can easily represent this in many languages, it has enormous advantages.
Are you allowed to give references from this area?
We have Light customers with whom we work closely. I don’t know if I’m allowed to name anyone.
So the one use case is the digital nameplate. Thorsten, do you also have use cases from your customers?
A very topical issue is the carbon footprint or product carbon footprint. Perhaps here to explain, the EU Green Deal envisages recording energy consumption, the footprint of emissions per product. There will also be laws that will not only affect large companies in Europe, but also SME’s, small and medium-sized enterprises.
In fact, this is already solved in ECLASS. We looked at all the legal texts in the EU and at the national level and said, how do I have to digitally equip the product with what information in order to record this product carbon footprint? And that does have a massive impact. Example one, the product is made in China or the product is made in France. Then, of course, the journey to a German factory is much more CO2 intensive.
The second point is the production itself. Is green electricity used? How many emissions are produced? This is then calculated down to the product. And that has to take place in a standardized way, otherwise things can’t be compared. I incorporate intermediate products, and my product can, in turn, be an intermediate product for a downstream one in the value chain. So a screw is installed in a car and there we already have connections.
Exactly, and you have to get the data from somewhere first. That means that this is the second use case, so to speak, to really have the CO2 footprint measurable with product precision. What is the pain of the companies today? What are they losing in time and money today? Can you tell us a little bit about the practical side of things, what you would actually have to do without this solution, in order for me to understand it.
Yes, of course, you first have to imagine that today the software landscape in companies and beyond company boundaries is not interoperable, but very heterogeneous. We have software islands, we have data silos, and as soon as I transfer data, it becomes very expensive, because I have to interpret the data and the interface costs are very high there. This means that I first have to agree in principle on which units are to be used.
Not only across processes, but also across industries. Food is also needed in a hospital. A screw is also installed in an electronic device. So that also means across industries and systems. One has SAP, the other does not. That’s why we need initiatives like the administration shell, we need initiatives like ECLASS, we need intelligent software producers like Neoception to orchestrate the whole thing.
If this were not done, it would be a cost block of around 4 million euros per year for such a company with 5000 employees. That’s a study released by the Institute of German Business in 2018. You have to let that roll off your tongue. The costs are there today. Manual interventions, invoice control, product engineers. That can actually be saved and then actually works quickly with software. This is what it’s all about.
This means that today I do not have a clear classification of this product data, if you will. You just said it can be millimeters, tons, or even across countries that you need this uniform designations. Then, of course, you have a wide variety of manual data, which until now has been maintained manually. Alaettin, you just brought the example, also with the documents. That’s one of the things that simply runs manually, where you save a lot of time and money, and of course also production capacities or throughput times, etc. Everything that concerns production. Can you put it that way?
You could say that. And that’s ultimately what the impact is if I don’t use it in a standardized way, if I don’t have smart software. In the end, time is also money. And the product engineer spends time maintaining and correcting data, not designing products. Clearly, that’s not efficient working there.
However, I would say that the business case is clear and it is also obvious where to start now. One more question, after all, it’s all about data. You have just mentioned a few examples. It’s about QR code data, but that can also be CO2 per ton, for example. Can you bring some examples of what data is relevant to these types of projects?
Yes, you have to distinguish between morphological data: Length, width, height, weight; between functional data, between security-related data; between certificates that are stored. After all, Alaettin just said, it comes to the maintenance case. Where is the documentation? What are the safety instructions? What to maintain and how? Instructions for use, data required by law, marketing relevant data. So you can already group that.
Also in the construction. There length, width, height are not enough. For example, when I design a product now, I need a bit more in terms of dimensions. Where are the connections? Where can I plug in which cable? How thick are the connectors standardized again? It then fans out like this. You can assume that there are always around 500 to 2000 of these standardized characteristics per product type. Maintaining all of these manually and then interpreting them is already very time-consuming.
Yeah, okay. Yes, that’s really crazy. Now I can’t even imagine how that actually still works manually today. Thorsten, you’ve already touched on it, it’s about standards. What are the requirements for such a digital nameplate and the various use cases that go hand in hand with it? What are requirements for your customers that are important here?
The industry actually also calls for the relevant industry standards to be set, which DKE, ISO/IEC issues. The USB connector is standardized, the units of measurement, volts, amps, that’s all clear. That is one part. On the other hand, of course, to let that all flow into the further processing. Purchasing, scheduling, design, engineering, quality assurance. This, of course, has different requirements from the process.
Ultimately now new also the legislator, who want to have environmental information. Industry 4.0, saying they’d like a nameplate later for maintenance. And what is also coming now is the so-called digital product passport, which is supposed to summarize the characteristics of the product once again in a very transparent way. For the next stage in the value chain, but also for the end consumer. As digital as possible. The EU is currently working on a legislative proposal and these are the things that are required nowadays in digitalization.
[19:46] Solutions, offerings and services – A look at the technologies used
Alaettin, maybe you can explain to us a little bit, how I can approach this now, what is the solution from both of you? Can you introduce that to us? What is the solution that you have now built together?
We have developed a solution, an automatism, to transform product data from inventory systems into standardized templates and make this data usable. However, it is also important to us that this data can be used on the one hand by our customers who purchase our solution in the end, but their customers should also expect benefits from our solution. Topics such as user-friendliness and security play an essential role. If we go deeper into it, I can go into more detail.
Very nice. Then I would go into more detail now. It’s also about data acquisition. First of all, I have to take these data types, be it morphological data as well as maintenance information, from the infrastructure. If we now put ourselves in your customer’s shoes a bit, how does this data acquisition work? Alaettin, you are virtually closing this gap between the data on the shopfloor and the individual business processes. How does it work? How do you acquire the data?
For our customers, the data already exists, but it is not really usable yet. That’s exactly what we want to do with our solution, break down data silos and make them usable for everyone. We have data structures, our templates, or templates that structurally describe a product. But we must also have values to these data structures. These values are in our customers’ inventory systems.
With our solution, we create a mapping between structures and their corresponding values. For this mapping we use ECLASS. Example: As a template we use the administration shell, which consists of submodels. The submodels describe different aspects of an asset, e.g. nameplate, technical data, documents, etc. These aspects have designations that are global but not one-to-one. To make this unique there is the attribute “semantic identifier”, that’s where ECLASS comes in. We use this attribute to perform the mapping.
That’s hard to explain in short here on the podcast. Mapping is a complex process, besides the “semantic identifier” there is a lot of contextual information involved. The big picture is cross-company collaboration. To achieve this, semantics is very important. Language connects, in our case ECLASS connects.
You have certain templates, which are first of all software modules, I assume, that make it possible to record this individual data. The asset would now be, for example, a machine from a specific manufacturing company. You’re now acquiring a wide variety of data from that asset, bringing meaning to that data. That is, you have the possibility, using CO2 as an example, to record how many tons of CO2 this machine consumes per year. You guys are just bringing a meaning to this data that every machine across sites is sort of speaking this language, as you said. And you sort of then translate that to the next level. Can you put it that way? If we apply it to the use cases, is that right?
Yes, maybe as an illustration, Alaettin, you said in the podcast it’s very difficult, I’ll try to simplify it. There are two friends who exchange data in an Excel spreadsheet. However, everyone has set up the Excel spreadsheet differently. And then they meet in the middle and say, let’s define the column headings together. ECLASS brings these column headings and definitions and the software from Neoception with the templates ensures that Excel 1 and Excel 2 are assembled into a uniform standardized “Super Excel” with the templates.
And then all parties are happy. If now a third person comes along, they get the “Super Excel” through the mapping and the others also find themselves there, because they ultimately helped in the semantics. So now Excel is a very simple thing. Based on ECLASS, we have to imagine: we have up to 19,000 different characteristics. We have over 1000 ISO units. I can no longer map that in an Excel and a data sheet. For the variety of products that the European industry produces, software is needed. That’s why what Neoception is doing is so brilliant. They say, come here, I have my templates, I make sure, on the software side, that we break down all these silos, all these boundaries.
Thorsten, first of all thank you for the example. You gave the example of China and France earlier, and there you already have designations that are different for each country in terms of standardization alone. You have to say machine A speaks this language, machine B speaks that language, CO2 in tons may be the same, but for other characteristics it is not. That’s just this standardization of semantics that everyone talks about, where you give it an equal meaning. But then bringing in the ability to do that in interaction with other systems to realize the use case. Could you put it that way?
Exactly. And we must not forget, ECLASS, as well as other semantic standards, are only machine readable. That means that the translations that we have are then translated into the respective national language, but actually from these machine-readable, machine-interpretable structures, in order to make this possible. Your example was very good, China with France, the machines ultimately ensure that clean communication.
Okay, and that sort of means, Alaettin, your software has these templates in the “gut”. That means your software analyzes that data and closes those gaps between, for example, sensor data and whatever hardware data it needs at the machine level, but also then with the business processes. You also mentioned earlier that now suddenly purchasing, scheduling, other departments are coming in, but also the legislator, who wants the data in the end. This means that your software also allows access to this data for other external parties, so to speak. Is that right?
Exactly. In principle, we have two roles in our software: the customer who uses the data, but also their customer. We make this possible with our solution. The mapping is so intelligent that we can couple a wide variety of end systems to it and tap into the data. This has the charm that we transform the data to an administration shell only when it is needed. The administration shell does not have to be there to work with, but only when it is needed with the standardized data. At this point, the administration shell is first assembled and this forms the database. Based on this, you can run different use cases.
You mentioned QR codes earlier. This means that your customer, your manufacturing company, now has the opportunity to use semantic data. No matter what data accumulates on this machine, you can use it and then evaluate it, so to speak. Do I have a dashboard there where I can then display this data as well? Or how does the software work at that point then?
The example with the QR code, what I talked about at the beginning, would be our customer’s customer. They have a QR code with a coded identifier, a URL. The URL then leads to our viewer, to a website, there is all the information mapped. The different submodels, the different aspects, documents, images, anything that the producer of those assets wants to provide.
A viewer is not always standardized. Since we always talk about standardization: a administration shell is, after all, a standardized digital twin. We also offer this interface with our software. There are different types of administration shells. If we look at the simplest variant of a administration shell, a simple exchange format, if our customer’s customer wants to have the data standardized, then they can export that as an administration shell in that exchange format.
Torsten, how does this work for your case? So you gave the example of the CO2 footprint. How does the evaluation of the data work at that point?
Yes, we “only” provide the structures. So ultimately, we leave it up to our customers to ask someone like Neoception, but of course also other software providers, or to look in their own site how they ultimately get these structures into their systems.
What’s relatively new now, ECLASS has just created a subsidiary for the small and medium-sized businesses that also offers software, but it’s purely online. There is no need to install anything. For 20 euros I can then ultimately build myself an administration shell online with ECLASS to test it out. This does not solve the challenge we have discussed so far. It also doesn’t solve the problem of collecting things at different data silos. A mapping is needed, a neoception is needed. But to get a feel for it, it helps to try it out.
Thank you for the elaboration. From the hardware side, how do I record the data in the first place, how do I manage to get these semantics in with the individual software templates that exist there accordingly. And then, of course, a scalable infrastructure that allows processing in other systems. That means you at Neoception bridge the gap between sensor data and business processes. You have the possibility to provide this data together with ECLASS. I think that was understood very well. I talk to so many different companies. That’s one of the key factors in bringing in scaling, because the manual effort involved and also what the legislator is now bringing in are simply issues that can only be solved in this way. And that’s really exciting.
[31:48] Results, Business Models and Best Practices – How Success is Measured
Can you briefly summarize the business case for your customers again? What experiences have you gained in the project?
Information loss-free data exchange is made possible with ECLASS as digital semantics and saves massive amounts of time and money along the entire value chain through clean data.
Very nice. Alaettin, from your side, do you have any additions to the business case?
I can only underline what Thorsten just mentioned. Especially when we look at our partners or Pepperl+Fuchs, with whom we work very closely, the example is often brought up: if you sell a simple sensor, then so much paper and plastic is included. The effort alone of having to put in the paper costs a lot of time, energy and CO2.
Very nicely summarized. I’m also always asked about best practices and what to look for and what the bottlenecks are. Do you have something like that from this project? Do you have any experiences you might want to share from the projects?
In any case, we still need to do a lot of education and awareness-raising on the subject of digital twins and administration shells. New customers in particular have a major education problem internally, which means we still have to support our customers’ internal marketing in order to engage their employees. The whole subject must first be made tangible for everyone. Many people also think of simulation when they hear the word “digital twin”, but this is only a small part of it. First of all, the first step is necessary, and that is the standardized provision of data, which we have to make possible.
Perhaps a small addition to this: don’t be afraid of standardization. There will certainly also be missing content. Through new products, through use cases that we don’t have. It doesn’t hurt to say, let’s standardize them. At the end of the day, we see that: the companies that actively participate in standardization as well, create competitive advantages over their competitors because they know what flows in such a standard and have a say in it. Don’t worry, this won’t hurt and this is no longer a nerd topic. Get your colleagues out of the archive basement. That’s super important to have clean data in the enterprise.
That was a nice closing word for today and I would like to point out what I also said in the intro: Automatica trade fair is just around the corner. Alaettin, you are on site, right?
That’s right, we are on site and look forward to seeing everyone.
The Automatica fair. Feel free to stop by Neoception on site, if you’re listening to the podcast now and Automatica is already over, no problem. They are both represented in the network, just contact them and discuss your use case there. That’s it from my side for today. First of all, thank you for taking the time to participate in this podcast. I found it exciting not only in terms of content, how the whole thing works, but also the use cases and especially the business cases. I personally find that very exciting, just to experience the practice. You have presented that very nicely. Thank you very much for that, and with that, I would just turn the final word over to you. A heartfelt thank you from my side.
Thank you, Madeleine, for allowing us to participate. Until next time I would say!
Thank you, Madeleine. Thank you for the great questions. You see, Alaettin and I take great pleasure in talking about this. Gladly again at any time.
Thank you very much and I wish you a nice rest of the week. Take care!