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Smart Manufacturing Kaizen Level (SMKL) – data acquisition, visualization, analysis and optimization for customers


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IoT Use Case Podcast #118 - ICONICS + Mitsubishi Electric Europe

The 118th episode of the IoT Use Case Podcast is all about the integration and synergies between software and hardware in the context of the smart factory. The focus is on the teamwork between ICONICS and Mitsubishi Electric Europe. Sebastian Creischer, working in Business Development at ICONICS, and Stefan Knauf, Division Manager (Industrial Automation) at Mitsubishi Electric Europe, discuss how their companies work together to develop and implement innovative IoT solutions.

Episode 118 at a glance (and click):

  • [11:24] Challenges, potentials and status quo – This is what the use case looks like in practice
  • [23:08] Solutions, offerings and services – A look at the technologies used

Podcast episode summary

Sebastian Creischer describes how ICONICS, as part of the Mitsubishi Electric Group, acts as a link between ICONICS and Mitsubishi Electric in the DACH region. The focus here is on the realization of smart factory projects and the connection of a wide range of devices in production environments. The data provided by ICONICS is used for visualization, historization and analysis in order to map various applications on all end devices.

Stefan Knauf emphasizes the strategic importance of the acquisition of ICONICS by Mitsubishi Electric. This acquisition marked the start of a new strategy aimed at transforming Mitsubishi Electric into a digital-oriented company. By combining hardware expertise and software solutions, new solutions are created for customers, particularly in the area of smart factories and digitalization.

Both guests emphasize that the collaboration between ICONICS and Mitsubishi Electric covers a wide range of industries, with projects ranging from visualization and control of machine processes to the establishment of paperless factories. The efforts in the area of energy efficiency and quality improvement are also particularly noteworthy.

The discussion will also highlight the challenges and solutions associated with the integration of different hardware and software components, as well as the importance of partnerships and customer involvement in the realization of projects.

Podcast interview

Hello Sebastian. Hello Stefan. Welcome to the IoT Use Case Podcast. I am very happy that you are with us today. Sebastian, how are you and where are you?


It’s great to be part of it again. I’m still doing very well. In the last few days, I’ve been quite busy again, also with the Mitsubishi colleagues. That’s why I’m glad to be back working from home today, to be able to work on a few things and exchange ideas with you in peace.

Very nice. That’s right, when this episode goes online, the ContiTech episode with you will also be online. If you feel like it, have a listen. A mega-exciting episode with Maren, who works in IT at ContiTech. But today you brought someone else with you. Stefan, first of all a warm hello to you too. Where are you at the moment? Where are you?


Hello Madeleine. First of all, thank you for the invitation. I’m actually sitting in my office in Ratingen, in the German Branch, at the headquarters of Mitsubishi Electric Europe in Germany, near Düsseldorf. I’ve had a week’s vacation, I’m relaxed and now I’m looking forward to what’s to come.

Very nice. I just wanted to look up where Ratingen is. It’s been a few days since I was there. It is located right between Duisburg and Düsseldorf. Is that where your production is?


No, we are purely a sales company here: logistics, sales, support. The factories are mainly in Japan and Asia.

Greetings to Duisburg-Düsseldorf. There are quite some listeners from the region and of course to everyone else who is listening right now. ICONICS is generally active in the software development sector. You are also part of Mitsubishi Electric, you are sort of their Software Excellence Center. You are experts in the field, were founded in 1986 and are a global software provider with a correspondingly long and extensive history in the field of SCADA visualization. You are a founding member of the OPC Foundation, perhaps we will come back to this later. Now let’s talk about your department and your customers. Sebastian, what exactly does your department do and which customers do you work with here?


Yes, very much so. You’ve already illustrated that very well. I myself am employed in Business Development at ICONICS and therefore have a lot of direct and indirect contact with customers. I’m building various solutions and trying to implement smart factory projects in order to ultimately integrate or use our central platform to connect all the devices in a plant. Roughly summarized, this data is then used to visualize and historicize it, perform various analyses and make it available on all end devices in order to map a wide variety of applications. We have been part of the Mitsubishi Electric Group for about four years now, working mainly with our Factory Automation colleagues. I’m a bit of a link in the DACH region between ICONICS and Mitsubishi Electric Europe, where I also help to realize various projects and create complete solutions. Stefan will certainly explain exactly what this means.

Visualization, historization, what exactly that means in practice, we will certainly go into more detail today. Let’s go back to your customer segments. Do you work across industries or which types of customers do you speak to more frequently?


As ICONICS, we are truly industry-independent. We are also active in the manufacturing industry, infrastructure and the building sector, and we don’t just focus on specific areas, not just hospitals, but also in the factory, food and beverage, automotive and, ultimately, wherever there are devices that need to be networked.

Just to clarify why you are both here today. How is Mitsubishi Electric Europe connected to ICONICS? You are also a subsidiary, but how are the two of you connected in this project?


In the four years since ICONICS was acquired by Mitsubishi Electric, it was basically the start of the implementation of our new strategy from Mitsubishi Electric’s point of view. In other words, our new President has announced the strategy that we want to develop into a circular digital-engineering company. We are simply a company that has existed for a long time. We are simply very hardware-driven. Of course, we also have to face up to the challenges, and that can only be done with software solutions. It was important to acquire a company that suited us. That was the starting signal. Sebastian and I work very closely together because, of course, we are also driving forward the topic of digitalization and Smart Factory in combination with our hardware for the Central Europe region, for which I am responsible, and generating new solutions for our customers.

Mitsubishi Electric is one of the world’s leading brands for the manufacture and sale of electrical and electronic products and systems. You have a very broad range of products. Stefan, can you tell us who your customers are and can you elaborate on the vision you just mentioned?


Our classic clientele includes traditional mechanical engineering companies, system integrators and, of course, end customers. This varies a little from region to region and country to country. Here in Central Europe, we have a lot of mechanical engineering and system integrators. We have a very large installed base of hardware products worldwide. These form the basis for all data that flows from the hardware into the machine. We are now trying to create a joint solution for our customers in various areas such as maintenance, simulation of new systems and improvement of operating processes via simplified interfaces, including simple software packages that are placed on top. For us as a company that is very hardware-driven and very traditional, this is an important step towards not being a dying dinosaur in the future, but a living dinosaur.

In the podcast, I always talk about use cases, including practical ones. Which use cases are you implementing together? Especially when it comes to working together?


With pleasure. These are really very different use cases, because we are very broadly positioned with our platform. Basically, we actually create solutions, for example in mechanical and plant engineering for HMIs and mobile HMIs. These are various IoT use cases for providing digital services. These are Industry 4.0 solutions, as we have now also seen at ContiTech, classic SCADA solutions, energy management solutions and quality analyses. Everything that involves recording machine data, building on it and carrying out a wide range of analyses.

Your project also has a strong focus on smart manufacturing, particularly regarding Kaizen. Stefan, can you give us a little insight into what this is all about?


What lies behind this is the topic of SMKL. This is the abbreviation for Smart Manufacturing Kaizen Level. This is based on the Japanese philosophy of Kaizen, which means nothing other than continuous improvement. In other words, we try to define goals together with the customer and achieve them together in stages. The background is this philosophy that you really don’t want to take the big steps, but really small successive steps. This is also included in our claim. The “change is for the better” also comes from the fact that we want to try to define clear steps together with our customers. They don’t always have to be big, they can also be small at first.

Defining clear steps with your customers would now mean that you work together with your customers on these various projects. For example, who is the workplace operator or who is the person you work with in the projects?


The workplace operator is an important element here because the operator knows their machine and knows what to look out for. It actually starts with simply collecting the existing data, then visualizing, analysing and then optimizing it. We try to eliminate unimportant data by integrating the on-site workstations. Otherwise, the philosophy is always to collect all the data, put it in a cloud somewhere and have it analyzed by data analysts. But that’s not really necessary, if you involve people at shopfloor level, you can simplify things considerably.

Okay, that means your vision towards digitalization and IoT is a holistically driven strategy that you live behind. In the Kaizen context, this means that you strive for continuous improvement with your customers. You also work more closely with customers to see how you can better support them and possibly offer additional services. Is it fair to say that this is your vision in these areas?


Exactly, we always have a very long-term relationship with our customers. It’s a bit of a genetic thing for us that we really want to work with customers over a very long period of time to optimize and improve their systems and machines.

[11:24] Challenges, potentials and status quo – This is what the use case looks like in practice

Then let’s dive a little deeper into the business case behind it. It’s always exciting to understand the reason behind it. Why is this important? Sebastian, what is the SMKL method all about?


First of all, SMKL is a method, a philosophy for introducing a strategy to our customers and identifying an approach. We look at where the customer is and where they want to go, to what level they want to be raised. The business cases that we examine or create can be very different. First of all, we make a general distinction between system manufacturers or OEMs and end users. The OEM or system manufacturer has a different focus, they are focused on their system, on their product and want an HMI solution to visualize the processes. They want to enable IoT services and offer their expertise as a digital service model to their customers, but also learn something about how their customers actually use their systems. The end user is more focused on the company as a whole and wants to map a complete smart factory, integrate all processes there, connect all systems, take a holistic approach to the topic and create visualization from various locations and analyze them at this level. That’s what we did with the ContiTech example. Then you can generalize it like this. On the one hand, the aim is to save costs, reduce energy resources, optimize quality or avoid rejects, keep spare parts available and optimize processes at the right time in order to further improve the ROI of such a project. We want to scale performance, ensure OEE and uptime and involve maintenance in an agile way. We also want to make it possible to generate revenue through these digital services in order to be able to offer spare parts with IoT solutions, a better user experience through a particularly attractive HMI solution or a mobile HMI solution as a sales argument for the machine to further simplify operation. We have actually implemented such use cases or business cases and also saved costs at the main plant in Nagoya, for example by introducing an energy management system in their own application. We were able to save resources. The mobile working that we enable during the Covid pandemic is also described in detail on the website. We have created an HMI solution for a machine manufacturer of hygiene products, such as diapers or menstrual products, or a mobile HMI solution to visualize and control the system processes. We have mapped the requirements of international customers, i.e. multilingualism and user management, and are particularly flexible in terms of application design. At an automotive supplier, we have managed to create a paperless factory using various dashboards, reports and automation. On top of this, a few more topics relating to quality in order to avoid complaints and reduce rejects. We can access the systems on a mobile basis. We have succeeded in creating a central view of all devices or systems for a producer of electricity and heat, visualizing them, controlling them, adding energy monitoring, connecting all heat nodes in the operation, and thus ensuring cybersecurity requirements.

Stefan, you’ve already mentioned various business cases for your company. Can you elaborate on that a bit? Production efficiency, cost management and energy consumption. What does that mean to you? What does this mean for Mitsubishi Electric Europe in terms of the business case?


On the one hand, of course, because we have the challenge of mainly producing our products in Japan, we actually have the same conditions as in Germany, i.e. some countries with very high labor costs etc. pp. We know how to produce many products with extremely high quality. Our own products are also created from this. We are increasingly trying to bring this expertise to Central Europe and Europe in general. Due to the ongoing crisis with COVID-19 and the economic downturn, the trend is clearly visible that many are bringing production back in-house to avoid always depending on other countries. That means the topic of Smart Factory is suddenly getting a whole new boost here. We have this know-how. We can also prove this. The problem we used to have was that we somehow stopped being able to talk to customers at a certain hardware interface. This is precisely the case for us now that with ICONICS we have a company that has further data processing under control. This has not been our core expertise to date. With these two core areas of expertise that we have, i.e. from our hardware side and the software side of ICONICS, we now have the opportunity to offer our customers a complete solution to their problems in the area of digitalization. That is simply the business case for us. We are trying to organize production so effectively that we can ensure that we can continue to produce in Germany or Europe in the future.

To tackle a holistic project to provide hardware interfaces and co-develop with customers, could you please elaborate on the challenges you had to overcome? There is also a certain amount of work involved when it comes to introducing the system or certain IT-related expenses. What challenges did you face here?


I would perhaps like to start by saying that we also had internal challenges at first. Of course, you can imagine that when sales people and technicians have been very hardware-driven in their job for a very long time, you have to generate a certain level of acceptance. I think we have managed that well. But this is also the case on the customer side. There is still a barrier between the OT level, i.e. the operations level, and the IT level. Different languages, different approaches to different reporting lines and different targets. These are the first challenges we face in bringing the right people from the customer side to the table. That’s crucial at first, it’s a challenge because they have to talk to each other in order to make this project successful for the customer. I think that’s the biggest challenge, although it’s getting better and better because the young people who come into the companies naturally think differently. But it is still the case that two different worlds have to talk to each other with different objectives. I think that is one of the biggest challenges we have to overcome.

Yes, absolutely. What are the challenges for your customers? One must first identify where there are energy-saving measures. What are the operational errors that ultimately cost time and money, or assembly errors, etc.? What are the challenges for your customers in this regard?


That varies. If we take a traditional mechanical engineering company, for example, some of them have not yet dealt with the topic because they produce a machine that does some kind of job, a packaging machine for example. On the other hand, they are beginning to consider how they can digitalize their maintenance and service business. This means that they must ensure that the data relevant to them is visualized and historicized somewhere. On the other side is the end customer. You can still see that everyone is talking about digitalization, but when you delve deeper, you get some wild results or wild ideas because it’s not always clear what they actually mean. With the SMKL approach, we try to bring this to a level where we define goals together and also want to understand together with the customer where they actually want to go. Do you want to save energy? Want to improve your quality? Do you want to improve your supply chain? It doesn’t make sense to solve everything at once, you really have to clearly define what you want to achieve first. I think these are also the challenges faced by customers, who run around with buzzwords and are sometimes not quite sure what they actually mean.

What other technological requirements did you have for ICONICS that are particularly important when you work with your clients on such projects? Be it in relation to the historization or visualization of data.


Firstly, it was important that the integration of our products into the ICONICS platform was as simple as possible. This means standardized templates and communication interfaces that are just as easy for our users as if they were programming our controller or our robot, for example. The first requirement here was to bring our world into it. On the other hand, as Sebastian already mentioned, ICONICS is of course very open. We also know that not everything runs with Mitsubishi Electric for one customer. We are not alone in the world and that is why it is of course also important to include these other providers, because they exist and they are part of it. This is what ICONICS brings to the table and we have now tried to integrate our world more. Other software solutions that we already have in the field of AI have also been or will be increasingly integrated into the world of ICONICS in order to link our expertise even more closely with that of ICONICS.

As you’ve just mentioned, the hardware used is different. What would interest me now, you have the plants in Japan, are the standards actually different from those applied here in Europe? If so, what are they and how do you deal with these challenges? That’s a wide variety of data types that have to be recorded there in the first place, isn’t it?


The standards in the industrial environment are now quite harmonized. Of course, there are still differences at the fieldbus level, i.e. at the communication level within the machine or between machines. But OPC UA has already become a global standard for everything that goes up the pyramid. That is not necessarily the problem. What explains the whole situation is that the philosophy of machine operation in Asia, for example, is very different from that in Europe. When you see machines here, a lot of energy and time is spent on visualizing the HMI or the HMI solution very graphically, preferably in 3D. Funnily enough, when you walk through the factory in Japan, this is not the case at all. The HMIs are really very pragmatically structured. Very few buttons, a few numbers that are displayed to make it as easy as possible for the machine operator. There is not so much emphasis on appearance, but rather on functionality. That’s a difference. We notice this particularly when we bring customers to Asia, as many of our customers then sell their machines in the Asian region. It is of course a point that we explain that this is not always absolutely necessary.

[23:08] Solutions, offerings and services – A look at the technologies used

Together you have the SMKL method, which you offer on the market to a certain extent. Can you tell me what the steps are? What exactly does this SMKL method involve?


So basically there are four steps. The first step is to collect the relevant data. The next step is to visualize the data in such a way that it matches the objective or task and also the language of the customer. As an example, HP was still used in the metal recycling sector. We actually use kilowatts, but our colleagues talk about horsepower. It’s atypical, but basically you have to set up the visualization in such a way that the customer understands it with their wording, with their vocabulary. The next step is to analyze the data that you collect and visualize in order to determine where my gaps actually are. Where can we save money? Where do I have peaks that I can work with when we talk about energy efficiency or management, for example? Where do I have quality problems in production, what are they caused by? The next step is to optimize this. This can be optimized technically, it can be optimized with software solutions, including AI approaches, in order to then feed the know-how back into the hardware, which in turn drives the machine and then improves the result or increases efficiency.

If I may delve a little deeper, perhaps we could go from data acquisition to processing to analysis, perhaps starting with data acquisition. Who does precisely what here? Stefan, what do you do in the team or teams and when do Sebastian and ICONICS come into play? Who takes on which role in these projects?


Let’s take an example from system integration with robotics. The robot arm, the programming of the robot, that’s what we do together with our customers on site. This also means integrating the vision systems, selecting the right grippers and optimizing the robot’s processes. This all happens at the hardware level and all the data that comes from the process and also from the device is then either transferred directly to the ICONICS world or via any line controllers, edge controllers, which collect it again via the line and then pass it on to the ICONICS products. Basically, there is also the transfer of data. We try to pre-select the data at the hardware level, which we then transfer to the respective software tools and then the ICONICS solution basically comes into play.

Sebastian, perhaps the transition to you. If the data is collected from workplaces in the factory or from entire supply chains or, as was the case with robotics, how does the processing work? I believe you also have standard software that you bring in, where this data is then integrated. How does data processing work once the data has been preselected?


The devices are available, they exist and they work. We now have two options. Now, with our parent company, Mitsubishi Electric, we have the option of directly accessing the devices natively via direct drivers. This is specific to Mitsubishi and is also developed there in Nagoya. The reality is that in a factory like this, in a machine, we have a wide variety of product manufacturers. This is precisely why we want to connect them in order to create a central level. We have therefore developed the concept of universal connectivity, which allows us to access the machine data regardless of the hardware. We have connectors for all kinds of products, such as all kinds of OPC variants, the latest of course being OPC UA, a BACnet in the building world, MQTT for IoT applications or even high-frequency data, a Modbus, an Ethernet, databases, web services, Rest APIs. We can then bring all the data into our platform, normalize it and give it a context.

This means that you have data connectors that extract this data from the devices, regardless of which ones they are. Of course, if they are Mitsubishi products, you have a direct driver there. Otherwise you can also integrate everything else. With this Universal Connectivity, you could say that you can then write all of this into different databases and further process it. How does this processing then work in terms of analysis, what do you do with the data? How exactly does the data analysis work?


First of all, we have a special starting point here in our collaboration with Mitsubishi, because data analysis can take place at various levels. In this collaboration, we don’t care where the analysis takes place. On the one hand, this can happen in the products themselves. The whole thing is then called Maisart for Mitsubishi products, for example. This can take place in a robot as equipment or as a component of a packaging machine or at an edge level, via gateway solutions or industrial PCs, in order to localize the intelligence directly on site. However, it can also be uploaded to the cloud or to a platform. This can take place in the cloud, but can also run on the server, depending on the situation. We can then use a wide variety of methods, on the one hand with different formulas, i.e. mathematics, to derive further information, calculate OEE, generate quality values, derive CO2 footprints and then react automatically if necessary. Or we use the data for AI evaluations; we are in the process of introducing our own algorithms into the platform. We can connect the aforementioned tool, the AI tool from Mitsubishi called MaiLab, or third-party tools such as Azure ML to enable even more analyses.

Stefan, how do you use the ICONICS software internally? Do you have access to the software? How does it work and how do you analyze the data with it??


So the AI, the brand name that Sebastian has just mentioned, is Maisart. This stands for Mitsubishi Electrics Artificial Intelligence creates state-of-the-art technology. We have optimized neural networks in such a way that we can run AI at a firmware level. In other words, we have the opportunity to use AI technologies in a servo amplifier or even in a robot in such a way that we can enable preventive maintenance from within the device, for example. With a robot, we can decide when the axis needs to be serviced or when a belt needs to be replaced or retightened based on the respective load on the axes. In the case of servo amplifiers, there are usually mechanical components behind a servo motor. We have the option of using vibration to optimize and tune the device so that the vibration of a packaging machine, for example, is eliminated. These are all AI technologies that are already integrated into the hardware. They actually ensure that we can use the ICONICS tools to ensure that something has already been pre-optimized at a hardware level. What is very important in the analysis is the customer. The customer knows their process and basically the customer has to do this analysis for their machine or for their application because we can’t be experts for all machines or applications, that’s the customer. We can ensure that we can use the right algorithms or the right tools to extract the relevant data in such a way that the customer receives a result as quickly as possible. The customer is extremely important in the analysis phase.

What do you do yourself and where do you find partners? What do you really implement internally and where do you build on partnerships? Are you open to that? How do you deal with it?


We are generally open to that. In all these sales situations, we work particularly closely with Mitsubishi Electric, who want to create a solution for their customers. We rely on various partners when it comes to project realization, for example. System integrators are then brought in who can ultimately implement the project if the customer does not want to do it independently. This is just an offer that we are making at this point. However, it can also happen that a machine manufacturer, if that is indeed the customer we are addressing here, becomes a sort of partner for us, as they will develop various of their own products in the future based on our shared products. In other words, they take our platform, develop it into their own individual HMI solution and make it available to their end customers.

I think that’s also exciting for many listeners of this podcast, we have a lot of mechanical and plant engineering companies or manufacturers who in turn sell to their end customers. It’s also important to have partners like you, where you simply rely or want to rely on scalable technology. Stefan, do you actually see yourselves as partners, because you theoretically also sell this to the end customer?


Yes, we actually have a similar concept. I would like to differentiate between the classic mechanical engineer. We have a team of application engineers who support the machine manufacturer in the prototype phase, i.e. until the machine is up and running. We train the customer on our technology, etc., but with the aim that the customer ultimately has the machine under control, programs it themselves and operates it with our components. ChatGPT We don’t undertake individual projects with Mitsubishi Electric at the customer’s site; instead, we also enter the competition with partners. That’s what I said at the beginning, that’s also the same philosophy that ICONICS and we pursue, that we really try to grow together with partners and also take them on board. We will never have a competitive situation where we take business away from anyone. We want to deliver a good hardware solution, a good software solution, but we want to realize the project with partners.

What we also see is that we at IoT Use Case have quite a large database of hundreds of use cases and thousands of projects. There are recurring approaches where it simply makes sense to rely on different solutions, where someone has already taken the learning curves and where there are already ready-made applications. Precisely because packaging machine construction was mentioned, there are already ready-made solutions where you can cooperate. A partner approach like this is of course very nice. First of all, thank you for this presentation of the joint project. It’s very exciting to see how you work together there. I think the Kaizen approach, including the SMKL method, has taught us how this works. Finally, I would just like to say thank you for your participation today. I would be delighted if we could connect again, perhaps even with a customer or another partner, to hear how everything is developing.

Thank you very much and I would give you the last word.


Thank you, Madeleine. It was once again a great exchange with you. I hope that you and your community will find another exciting article about how we are creating IoT solutions for OEMs and digital business models, but also realizing smart factories for end users. Thank you, Stefan, for being there and for sharing your experiences and plans with us here today. I look forward to continuing our journey together. It’s always fun with you and if anything is still unclear, feel free to write to me on LinkedIn. I am happy to help.


First of all, thank you for the invitation and for the opportunity here, it was fun. We are currently on an exciting journey to bring two worlds together, and this is also a kind of appeal and perhaps a wish to our customers that they do the same. So, instead of working in opposition, they should engage in collaborative efforts between IT and OT, and between hardware and software. I believe this is what we need to ensure the standard here and also secure our future, maintaining our standard. That is challenging enough, but I believe we have great solutions. Just like Sebastian mentioned, you can always reach out to me via LinkedIn or contact people from my team. I’m looking forward to the journey and I think a lot of exciting things will happen.

Absolutely. Many thanks for the closing words and your contacts are also linked in the show notes. Feel free to get in touch with us through LinkedIn or directly. Thank you very much for joining me and have a nice week. Take care. Ciao!

Please do not hesitate to contact me if you have any questions.

Questions? Contact Madeleine Mickeleit

Ing. Madeleine Mickeleit

Host & General Manager
IoT Use Case Podcast