So hello and welcome to this episode of the Trend Detection podcast. I'm really delighted to invite back Peter Schopf. For those who remember we did a three part series a few months ago.
I think we actually possibly called it IoT time and I wanted to as a recurring theme, unfortunately we haven't had the chance to invite Peter back on before now but maybe we can make it more of a regular thing. If he's open to that, it'd be great. But we just want to talk about all things IoT today just for half an hour and we haven't really got an agenda as I was saying before, no questions or anything, we're just keeping it very open discussion.
So maybe Peter, if you just start by sort of introducing yourself and to the audience so we'll just open it up from there. Perfect. Well thanks a lot for having me back and I really enjoyed talking about those topics like digital transformation where IoT of course is the core topic and yeah, it's been a dynamic time let's say and we can also touch upon that kind of personal developments and organizational developments that are taking place. Well, just kind of a big little bit of background about myself is that I've been a long term Siemens employee for 14 years now in various managerial roles. In sales mainly, but also strategy and business development, covering project. Management covering sales from a commercial point of view but now also from a regular sales point of view being the sales leader for MindSphere which is now Insights up until recently for EMEA and well, I used kind of the current changes within.
Also the setting MindSphere being part of accelerator now, which we can talk about, which is quite, I think, an interesting development within Siemens. And I used that to try out different things, because during the last years we have learned a lot about digitalization about digital transformation of industrial companies. Throughout these learnings, the good ones and the bad ones basically I think I can really share a lot of insights on what to take care of, how to avoid pitfalls and best practices. So we started a consulting approach outside of Siemens in order to also discuss that communicate that we've created a checklist of what you should consider to make it easy, but not to miss topics, which is interesting that many companies miss the same topics and focus on not the wrong things, but put their focus too strongly on certain aspects of digital transformation and forget the rest. So basically interesting development. I'm still active with my PhD so I'm doing a PhD in Design Research Information system it's basically and with augmented reality, the industrial metaverse also this is one of my key topics I'm very fond about and which will develop, I'm sure about that.
It's just a matter of the time. Let's quickly wrap up about myself and we can take it from there. As I said, the digitalization checklist I think is interesting these learnings that we have, how IoT relates to also the other technologies of digital transformation.
All of that we can discuss. Yeah, I'd say congratulate you on a very good intro because that's left lots of little places we can dive into, I guess. But actually let's start first of all, so you said you're moving into sort of consulting role as well. So do you want to give a little bit of background of why that's the case and what you're sort of offering? What's your sort of reasons for doing that? I guess yeah, sure. So actually it was my still current supervisor who gave me that idea one year ago.
He said, hey, basically what we are struggling somewhat is that many people reach out to Siemens because Siemens is an innovation leader in the industry and they want to understand what is IoT? For example, what can I do with that? What's this digital transformation kind of thing? What is the digital twin? So they come with a lot of questions. And while we have been in the sales role promoting our MindSphere IoT platform or our software as a service or IoT as a service solution, this has been a little bit a friction let's say on the one hand side we want to sell something, provide value to the clients, we get a specific solution. While the clients very often had these very general concepts and mising understanding about what it really requires to do. And therefore it was actually his idea to say, hey, you explained this so well, why don't you start a consulting that would be actually great.
So I had a lot of my plate at that time with this augmented reality project I drive within Siemens IoT. Being a sales leader for EMEA, my first thought maybe it's too early, but then I started the company, I had some good contacts. Also my brother is part of it, some other colleagues will be part of this network approach on consulting.
So it kind of grew by time and now I have this possibility provided by Siemens to explore these opportunities to then now dedicate myself to that consulting approach. And really when you are at the early stage of digital transformation, when you want to understand what it is about and how should I do the plan to go ahead from that stage also until later stages of course, to verify the concepts, interesting enough is that my brother for example, is in the finance industry. So he has been a portfolio manager until recently and he wants to consult portfolio managers on hey, how do you use AI tools? And I've been thinking that fits very well together with our approach for the industry because many portfolio companies are heavily invested in industrial companies. But industrial companies that don't have a digitalization strategy, that don't have an AI strategy, that don't have a data strategy, going forward, they will struggle maybe not this year, maybe not still next year, but the years to come. So it's very important actually for also investors in companies to understand if their invested companies have such strategies.
And there we can come in and say, hey, we can provide you a digitalization strategy, support you in that process of doing so, a data strategy, and connect it to your company strategy. And I think that is also a very relevant point and one of the pitfalls many companies do because they kind of disconnected, they have their company strategy and then they have some kind of pilot project on digitalization and it's not connected. And that's also a problem on how you position digitalization projects with the management, how you get the resources, how you get the buy in of people touching upon change management, which is again, another pitfall many companies don't put enough focus on. There's many things and then I think we can share a lot of these learnings that we had from the past.
No, exactly. And it's interesting. Yeah, I think that's a really good point about business strategy. I think people sometimes forget to connect the technology, the reason why they're procuring a technology and connecting that to the business strategy and the business outcomes they want to achieve. So I think that's a very important, very important point to that checklist.
If I may interrupt shortly, exactly what you pointed out. There was a nice graph, I found it in the internet somewhere. I like these knowledge pictures from the Internet, information graphics and alts.
And it basically showed how companies normally look at digital transformation. And it's kind of a big chunk of technology, a little bit of process and very small the people. And normally it should be quite balanced. You should focus on technology, of course, but you should also look at your processes, derive use cases out of those and look what's the impact on your digital transformation on processes and also about the people.
And that is one of the most fundamental things people neglect to think about who is impacted, in what kind of way and how is that basically resonating with their target structure, but also with their personalities and with the company culture and those things there are sayings out there. Everybody knows, like kind of company culture, it's strategy for lunch or something like that. Everybody knows that, but nobody acts upon it.
Or it seems like that because it's so much easier and straightforward to look on technology instead of looking at these more dynamic processes and even much more dynamic people topics. Therefore, I think it's important to talk about that because many of the problems that arise later on can be fixed in advance. And that's also what we struggle as a technology provider. Obviously we talked about the technology, but then it's left to the customer to also think about their processes and their people. And that was also one of the friction that is difficult to solve when you don't include consultancy approaches. That's an interesting thing, actually, the cultural change, because we talk about that a lot as we maybe it's a grandiose statement, but it's like we enable cultural change of an organization.
But is that actually the case or is it the customer needs to do that, or is it a combination of both, helping the customer to achieve that? Do you see what I mean? Is it too simplistic to say a product can enable cultural change? I guess is the question. I think it needs to go hand in hand. You first need to understand why there's this golden circle of Simon Sinek I like so much.
First talk about the why. Why do we need to take care about the technology and engage with it? And that's basically the understanding to generalize it. I mean, each company can define it a little bit different, but to stay relevant over the next years because those companies that will not engage with Novel technologies, they will be out of business in some few decades. And if you want to go that short sighted as a leader of a company or as an employee of a company, that's really I think when you show the right analysis and talk about these changes that are upcoming, that people understand. So this why understanding.
And then you can talk about the how. Well, how do we do that? Okay, we need to change. We need to have these industry 4.0 concepts in mind, these North Stars in mind, to change our company going forward. But how do we do that and what do we do? It's kind of a succession and how is very often the technology, and indeed, I think their technology can help. Let's talk about low code, for example.
If you are enabled with low code, suddenly people who are in the business line who are not the It programmers can create small applications and test things out. If we now talk about AI, generative AI, Siemens, for example, had a very interesting approach to enable all their employees to access a secure AI environment in collaboration with Microsoft. So every Siemens employee can use Chatchi PT now. And this is kind of transforming people's daily work.
I mean, you wouldn't be able to create a big master plan how you can specifically train and enable all these people to do something specific with AI, but by enabling that, by providing access to that technology, obviously combined with training, communication and a clear message about, hey, we need to change. We need to do that. Then I think the culture is impacted by that technology because I love to use Chat GPT, for example, and low code. I tested it as well. There you need a little bit more time to really get something productive going.
But these possibilities are there. For example, my augmented reality project, also something that they can explore with this is an application where you can post content everywhere. For example, you place an IoT dashboard next to a machine and you suddenly have a communication pane, an HMI, virtual HMI, but localized and personalized. So things that you can explore, that you can just try out and then you find out how it could influence your processes. And this step by step changes people's minds, their relation to technology. If it's always kind of a master plan and this is what you need to do, it's much more difficult than when people within a company can start exploring because it's easy and easy to use, easy access, what a company can then provide to the employees.
I'm a marketing person and it's sort of taken over marketing in the sense the conversation of marketing is all Chat GPT everywhere, and for good reason. But it's the same thing happening on a manufacturing or IoT level. Is it quite sort of taken off on that side yet? Or is there still more development and understanding to come before that happens? I think it will take off soon because it's just so intuitive and I'm not now involved with those discussions on a product manager level. So it's just my wild thinking now. But for example, what was missing very strongly within IoT offerings and platforms was a very, very comprehensive reporting tool or interface.
So you got always some reporting for all the IoT tools in terms of how much interest rate and how much users and things like that. But if you think of a more comprehensive analysis that you need, for example, take Salesforce Customer Relationship Management tool. In there you have so many different aspects that you can correlate. You have your leads with all the information of the leads, location, address, their interest and else you have your accounts, the companies that you talk about, their structure, their location and else. And so all of these different topics, the opportunities per account, per lead, per contact with millions of information that you can put in information snippets, and you can then create reports by drag and drop these different things.
And that's really comprehensive and it's good because you have all kinds of questions as a sales leader, for example, what kind of product is asked within what country, from which kind of count, who is involved in LCI? And this is all possible with such reporting, transferring that to IoT, then if it's rolled out, you have similar reporting requirements. You want to understand which machine was operated or changed, maintenance type of things, by what person, how much interest rate, what application had, what kind of impact created, what kind of notifications and alts. So these questions are building up in complexity. And now if you use Chat GPT for example, or Generative AI, large language models in general, you can suddenly, from the books that you have, forget about these complex reporting tools.
You just put an interface, a large language model interface on top and you can suddenly ask a large language model. Very intuitively, all these questions, and especially those questions have been sometimes also with the reporting tool of salesforce. At some point in time it gets more complex and it would be just so easy to ask, hey dear IoT Chatbot, I want to understand the notifications and issues that have been created throughout my seven plants in the last seven days or something like that, and it can give it to you immediately. So the way you will interact with IoT data will be much more easy, much more improved, I believe, by generative AI, which also will increase adoption and the need for additional data within your shop floor. And that's, I mean, what is IoT about? It provides you additional data in your shop floor, not all the relevant data, so we can talk about that as well.
But at least it's a big portion of the truth. Exactly. I guess. Again, the thing I'll get with Chat GBT is again about focus on the business outcome because again, it's tempting just to think, oh, this is really cool what it does, and have a play around with it.
But actually what you're saying there is actually leads to it can lead or potentially lead to really significant business outcomes. Because if you're, like you say, reported across seven plants and you can ask GPC to summarize that into a concise report to management, how much time does that save you, which you can focus on elsewhere. That's the power of it, I guess. And this makes this interaction with data very important. And as I just said, what kind of data do we require? And that's also, I think, interesting to understand from an IoT perspective. Companies very often have more comprehensive requirements on their use cases.
And that's also interesting thing. Yet, are you going into a data first approach or a use case first approach in terms of digitalization, but let's for now stick with a use case. Many use cases actually require not only IoT data, which is providing you data from machines in your shop floor, so the production machinery data, but also they require data from the product, produced product as such, or the process. So these are basically three elements, the production machine, the product and the process that come together. And with IoT you have one part of the truth and somewhat part of the process action. While for example, with Mes systems or mom systems, you do have then the part of the product being processed, being planned and how that acts.
And when you think about you, many use cases center around, well, I have produced this kind of batch and it has been taken that long. What have been the parameters of the machine? How did the machine produce that batch and what was the outcome in order to optimize that as a package? For example, with Siemens, I mean, this minesphere towards accelerator move and becoming insights hub that also combined it now more and brought it closer together with this mom manufacturing operation management systems which then talk about different things like quality and also inventory and provide you a more comprehensive view on your shop floor. And to make it easy, you can then ask something like a large language model for these analysis. But you need the data first. You need to have all these topics structured in a way that such a large language model can understand.
And as you mentioned it Peter, and you mentioned it I guess towards the start as well about Siemens accelerator as well. So maybe you could explain a little bit about the concept of that, what it is and what's it there to achieve, let's say. Yeah, for now it's a vision, but I think it's a really cool vision that having this knob style we really will help Siemens to become a major player, also remain and become even more major player within this industrial software environment because it kind of gives some guide rails on what and how we should develop applications. And there should some simple things like being open, having open interfaces, being interoperable and all of those requirements that make it then easy to use those technologies. Also because it becomes so fast, so complex within the industry. When you talk about a comprehensive view on your production, you need IoT data, but you also need data from your SAP system or ERP system depending whatever you use from your SCADA, from your mom Mess system and all of these systems, and there are many modules below these systems and then the design of the product.
So it becomes very fast, very complicated if you try to build that yourself or your own company will never work in a way that how future companies will produce and will organize their manufacturing. So you need these different models to sum, not all at once of course, but the ones which provide most value to you. But then you need to make sure that they fit together because if you have one model and the other one and they don't talk to each other, you cannot optimize the holistic picture.
And this is kind of this accelerator vision from my point of view that you can provide all these different solutions and modules, but they work together, they are open, they are transparent and not only open within the Siemens environment, but also based on international standards. So I think it's kind of a must have and Siemens really committed to that. They also what I think is really helpful do foundational services that then all of these software components can use and much of what we developed during the minesphere time is now part of these foundational services within accelerator.
So that makes it much stronger than before, I mean in terms of access orchestration, in terms of cybersecurity, data governance and LTL. So all of these topics will be shared and will then just excel these single offerings much faster. So I think really cool approach, really good strategy, will take some time to be fully operational in all aspects, but I think the approach is the right one. I guess another important part of the partner ecosystem as well, because I think even not speak on behalf of the whole, I'm getting it's even saying we can't do it all ourselves, we have to work with partners. So what's your sort of view on the partner side of things as well? Yeah, that's definitely a big portion of that being that open. Siemens also clearly communicated that they cannot do everything themselves.
And so this partner ecosystem will grow on different levels, applications that are built, implementation support. And like me, I mean, I'm leaving Siemens soon, so I hope still that I be in that environment of how we as partners, working together in different areas, just provide the best to customers, to end customers and be efficient about it, share knowledge, share pitfalls that others already experienced. So I think this partner ecosystem, I'm really counting on it because that was one of the reasons I decided also to take that step, because I still believe in the Siemens story and success of this approach and I want to leverage that as well. Yeah, I guess that's important as well because partners or ecosystem, you tend to think of not simply, but obviously technology and how it integrates and how it all fits together in terms of existing workplace.
But you're right, speaking from your point of view, it's also about expert consultants as well, and the services side as well. I guess it's just as important part of that accelerated vision. Absolutely. And let's see how that works out. I'm really a strong believer in network effect.
It doesn't need to be one legal entity only, but rather networks of legal entities collaborating, helping each other. So I'm actually already beside, although our company is still small, we would be able to do much more because we have good relations to other companies that could support for specific areas like cybersecurity and else. And so this providing a network approach enables suddenly companies to solve challenges and questions much more easy and efficient than before. If you focus only on your own company and try to insource everything, do everything yourself.
So therefore this network approach based on such flexible technology, from my point of view, be the way forward because we're keeping this short and sharp to 30 minutes. So I wanted to just end on my question. It was actually something you mentioned, your sort of opening statement, Peter, and I think it was around what customers are focusing on in terms of digital transformation and maybe they're focusing in the wrong areas. So I thought maybe if you could expand on that a little bit, just as our final sort of topic, let's say, and it's an interesting point because there are different angles to look at it. One angle we discussed already, it's kind of this technology processes, people kind of way to look at it. Another one I mentioned in between also it was kind of this well do you look at the product or do you look at your process or do you look at your production machine and what do you optimize? Do you optimize the throughput or do you optimize the end date? Basically you know when exactly you need to deliver your product and how can I optimize the process up front and be able to deliver in time and in budget? So there are different ways to look at it.
I want to pick up another one. It's basically this I mentioned it as well, use case first or data first because that's kind of a topic, foundational topic that you need to consider at the beginning. Use cases can come up. I mean you can do a brainstorming session, you should do kind of a process analysis and derive use cases out of that. But then if you implement only one use case and we had that just two weeks ago I was at a conference and they discussed exactly the same thing. They had basically two customers who one went for use case, one went for data first strategy and the one who made a use case, well, the use case, the pilot use case worked and it was efficient and it was clear what the outcome should and would be.
But then they struggled afterwards a lot to get the right data in place. To scale it up to other plants was very difficult because just the foundations have not been set. If you go for data first and you consider what's your data architecture that would allow you to develop further use cases.
You connect machines just to get the data and then you think about the use cases that's a more risky approach because you will need to spend more time in connectivity topics, in integrating different tools and else and you don't know exactly what comes out but you can be explorative. You might find some things, find a use case, find a benefit that is much bigger than you ever would have expected, but more risky. This balance, what do you do first is quite interesting and many companies focus on the one or the other. And I think it is possible to do both to have a deliberate approach, think about use cases, but then align what kind of data structure is required and go a little back and forth and that should be a conscious decision to take. I think these learnings and pitfalls are very noteworthy for companies to think about into those two points, the use case and the data point.
Is that an It and OT discussion, OT convergence is a message that's constantly pushed in the market or the Gartners or whoever of this world. But is that essentially a lot of what you're talking about there, Peter? Much of it, of course, because OT and It for themselves already have well defined use cases and applications and interfaces as well. And it's just much more challenging than many expected to have this Itot conversion. It's easily set, much easier than done, because it's really two worlds colliding with these regular software updates.
Very frequent, always up to date, while OT needs to be very stable, very secure, and you just don't want to jeopardize your production in any way, because only shortages or mistakes could lead to fatal but significant problems in your production. And also costs, of course. Therefore yeah, it's definitely a big part of it because then you have your interesting use cases right at that convergence area. You want to leverage the competences of It on your OT data.
So definitely that's part of it and it's not easy. I do think that even in Siemens with very bright minds, people underestimated the complexity of that development. I'm still very happy that the management understands and pushes it through continuing and with accelerator even stepping It, I mean competition even more so. Tried to start an IoT platform. General Electric tried to do that in the past with many companies.
And actually there was a high volatility in terms of how many IoT platforms are available. And that's only because IoT is kind of, if you think about it, the spot to start is it OT conversions because it's in principle, if you talk about the Internet of things, you would use cloud. I mean, of course you can use HMLS, but the concept as such would imply that you take data out of your production and then leverage cloud and these possibilities via the internet and that's just the challenge. And you need to be then also in this It. Absolutely. So I think we've come to the end, I don't know if there's any well, I think you've summarized it quite well at the end, Peter.
I guess we didn't really have a theme for this. I think we did quite well to speak for half an hour, so have a pat on the back for that or maybe mainly you because you'll double the talking. But is there any other final thoughts you want based on sort of the discussions we've had this morning? Be aware of what you get into with digital transformation as companies.
It does require to have strong partners to understand what they are getting into. Also don't neglect the process point of view. There are nice processes which can help you to run through a digital transformation like OKR, it's kind of objectives and key results kind of process where you define clear objectives and the results you want to achieve and define this per team to really have this transparency and why you do it, who does what. Drive this. Change management through people and processes and combine that with technology. So it's really something you can do a lot of. Things right and you must do it as a company, I'm convinced about that, otherwise companies will fail.
But it's a challenge, and you should understand this as a challenge for the company to have this digital transformation. I think many companies don't have that yet. Very clear that it's really required and I'm worried about that a little bit because especially in Europe we are not that it effie. I'm really trying to convince people, especially in Europe, to really be active and not to wait too long because you cannot catch up in a short time. It takes time. It needs to change the mind of people, it needs to change the culture and if you wait too long, it will be really difficult.
And there are great partners out there, siemens being one of them, Scholzmita Consult being another one now and I think that is really something that within a network of partners, that's really something to solve. Yeah, no, excellent, excellent advice. And a nice plug there, but a good plug, I think, for you, Peter, so that sort of wraps things up. I'd also suggest actually following Peter on LinkedIn as well. It's very interesting updates there.
So when we post this episode in the show notes, there'll be a link to Peter so you can connect with him as well and find out more about his consulting business as well. But for now, I think we'll just say goodbye, but not farewell, because I will invite you definitely back on Peter again to do this a bit more regularly. It's fine by, as usual, but yeah, thank you for taking part and hopefully speak to you again soon. Thanks a lot for having me. Take care. Thank you, everyone. Bye.
2023-09-21