In this episode of Manufacturing Matters, Reinhold Niesing, Vice President, Automotive & Battery Vertical Markets, Siemens joined TECH B2B Marketing’s Winn Hardin and Jimmy Carroll to talk about why manufacturers and end users today should expect more out of their automation systems.

Leveraging technologies such as AI, for example, can help with preventative maintenance, programming automation systems, visual inspection, and more. As we enter a phase where AI can be grouped into traditional AI and emerging AI, the paradigm continues to shift in terms of how AI can add value.

The discussion covered a range of other topics, including EV battery manufacturing, IIoT technology, Siemens’ latest innovations, and more. In addition, Niesing offered a stalwart prediction for the next few years in the industrial automation space.

Jimmy Carroll: [00:00:07] Hi, Everybody. My name is Jimmy Carroll. I’m the vice president of operations at Tech B2B Marketing and here at Automate, and I have the pleasure of being joined by Reinhold Niesing, who is the head of the automotive and battery group at Siemens. Reinhold, thank you so much for taking the time out of your busy day. I really appreciate it. I suppose everyone knows Siemens, but if you could tell us a little bit about what you do specifically in your group, that would be nice.

Reinhold Niesing: [00:00:30] Yeah. This is what we do in our group, actually, we are focused on the automotive industry. This is my core topic. This is what I do when I wake up, and this is what I still do when I go to bed. So, this is our key focus. So, we are actually the primary interface for the automotive industry and taking care of the automotive industry, including batteries at Siemens.

Winn Hardin: [00:00:52] Great. Including the EV battery. That’s a hot spot. So, what do you think of Automate so far? For those who don’t know, we’re, we’re just below about 20,000 of our best friends who are upstairs checking out the latest innovations at Automate. Right now, we’re in day three of Automate. How’s the show been so far, Reinhold?

Reinhold Niesing: [00:01:07] It’s an amazing show. And first of all, I want to congratulate the organization of this Automate. It’s really amazing what . . . putting together such an event. We are excited to be actually a key contributor to this event, a part of this event. We are blown away about the attendees. We are very much excited about our keynote speech. The attendees, what we saw there. And last but not least, all these system engineers, industry engineers, leaders actually coming to our booth, visiting our booth, and having really great discussions about how the technology will work, how the future will look, and deep diving into our portfolio.

Winn Hardin: [00:01:46] Couldn’t agree more. Yeah, we’re lucky enough with the “Manufacturing Matters” podcast to be A3 media partners. So, we’re always astounded by what they do, and how much better they get every single year. I think this year, I think attendees, you know, I’ve heard whispers of over 40,000, possibly, which I think last year we were a little bit over 30,000. The numbers, I think both the number of exhibiting companies as well as the attendees, are probably up around 25% year over year.

Jimmy Carroll: [00:02:12] Nearly 800 exhibitors is what I heard too.

Winn Hardin: [00:02:14] Wow. Outstanding.

Jimmy Carroll: [00:02:14] I was talking to Reinhold on the way down here. It almost felt like last year was the welcome back post-COVID Automate, and this year it’s like it’s something even new. It’s evolved. That’s really exciting. What though I want to ask, you know, general industry impressions aside and your thoughts about the show. But what is Siemens most excited about right now?

Reinhold Niesing: [00:02:35] So, we’re excited about how the automotive, not only the automotive, but industry in the United States develops. How can we help the industry here in the United States to further evolve, to become a more sustainable, better world, help them actually to make good business not only for for the business itself, but also for helping the world.

Winn Hardin: [00:02:57] Do we have any new product introductions this year?

Reinhold Niesing: [00:02:59] So, we had a lot of new introductions actually. And maybe if you had a chance to listen to Del Costy’s keynote speech, it was all about you should expect more from automation, from your automation. And I guess this is really what we try to showcase and what do we do showcase in our booth. There are technologies, for example, artificial intelligence, generative AI, cockpit assistant for system engineers, how to develop code. Very innovative concept. But also, for example, we have systems for how we can do preventive maintenance, new ways to program an automation system. What is very exciting is that we can tap in additional resources so that we get the world closer to IT. So, it’s all about, for example, tapping in additional people, making them available. But also, for example, what we were very excited about is our automation workstation, what we developed together with Ford.

Winn Hardin: [00:03:58] What blows my mind is we keep talking about adding functionality, but we’re not requiring the engineers and the programmers and everyone to learn completely new platforms. It’s revolutionary results with evolutionary steps, you know. So, the ease-of-use component, which I know is something that’s talked about so often, thrown around as a buzzword, but it truly has value, especially it seems at this, at this moment in the industry’s evolution, right? I mean, as we bring in AI technologies and . . . but making these technologies seamless because you don’t have to be a mathematician to be able to develop statistical models, to be able to leverage this, this incredibly powerful technology. Does the new workstation . . . What is its connection to AI? Is there a component there?

Reinhold Niesing: [00:04:40] The automation workstation is . . . It’s an interesting, unique product because it’s a combination of multiple disciplines. So, we integrate all the traditional automation systems. What you need are PLC and HMI. But also a key component is our edge management system, what we put on top of this. And the edge management system allows us to manage the device by itself, to make it easier to manage updates, system updates, security updates. Because we all know living in the world of cybersecurity, you have to consider this. But also, for example, the edge system is the platform. How we can, for example, run additional applications — applications sometimes developed by Siemens, sometimes by our customers, or any other third party.

Winn Hardin: [00:05:27] So, does this help us provide better end-to-end data communication across the entire enterprise? I mean, when we talk about edge deployments, right? I mean, we’re talking about bringing communication. I don’t know whether it’s 5G or still leveraging the Ethernet IP core network of the plant or both. Is that a . . . What are, what are some of the differentiators and the real value propositions that come with this workstation?

Reinhold Niesing: [00:05:46] At the end of the day, you are dead-on because it’s all about the IT/OT integration. This is the one key topic where we are all working because we are working on the digital transformation of the factory, and one key component is the availability of information and data. And the IT/OT integration is implemented really via this edge system. Edge systems were already existing before. What makes this unique is that we baked it all in one single product. So, there is no integration work required for the different users of the system. We provide this as a one single station so that we make all this technology available, ready to be used out of the box.

Jimmy Carroll: [00:06:27] From an industry standpoint, I wanted to ask about some different verticals. And, you know, for so long automotive has been the the driver of automation, pun definitely intended. But there are others like food and beverage and warehousing and things like that that are kind of expanding and evolving. But now within the space of automotive, there are a lot of different new, you know, new applications. And in what ways are EV, for example, EV battery production and EV battery inspection, in what ways are these new demands driving automation advances?

Reinhold Niesing: [00:07:00] It’s an interesting aspect because one key component of the EV strategy are the batteries. So, we all need batteries to drive these electric vehicles. But so and that’s an interesting new technology. It’s sometimes new if automotive, for example, uses this. It’s a different process they have to implement because batteries are not only an assembly process. It also involves, for example, process industries. You have to work in categories like coating, mixing applications. But automotive traditionally didn’t do this. So, we have to convert them now to make these technologies available and adjust to the different process, what they have to implement right now. And this is now interesting, having these applications running in a process which is not traditionally used in the automotive industry, but making them now available. But that comes together. Same for different line builders now who are becoming part of this network and this ecosystem.

Winn Hardin: [00:07:58] I wanted to follow up one other question, if I could, on the workstation before we move too far away. So, we’re talking about the edge. I’m just wondering, does that bring intelligence to, to existing DOM systems? I mean, is it part of pushing the IoT capability out to systems that were not made to have those communication interfaces? Is that a component of that?

Reinhold Niesing: [00:08:16] Yes, what makes it unique is actually how fast we can implement these new technologies. So, the edge provides the ecosystem, the infrastructure behind it. So, for example, we can use AI applications which we develop, but how do we get them on the shop floor? This is the main concern actually. So, making this . . . What is the runtime environment? Edge gives us the capabilities to use AI applications but run them in a protected environment, almost in a sandbox on the manufacturing shop floor. Test them. And after we’ve tested them and we are convinced that they are doing the job the way they are supposed to do it, we distribute this to a large fleet.

Winn Hardin: [00:08:58] Fantastic. Is that in a small test node, or are you actually running basically parallel programs where you’re doing real production while also testing new routines?

Reinhold Niesing: [00:09:06] Exactly. This is exactly the concept where we say, okay, we can use the ecosystem of this edge system to say, first we test this, for example, in a lab environment, or we test this at a single station. But then we want to go large scale. And to be quite honest with you, this was sometimes really the hurdle. There are always good ideas about how to improve a manufacturing system. But many times we were struggling. How fast and easy is it to deploy these good ideas?

Winn Hardin: [00:09:32] Especially the large global enterprises.

Reinhold Niesing: [00:09:34] Exactly. Large enterprises. How consistent we are. So, it’s, it’s now having the capabilities to improve the edge to make it more agile, to have systems more current. We are not going to implement a fixed functionality and run the same functionality for a long period of time. We’re starting to make the whole system more dynamic, allow it to be updated, enrich them with new functionality, just like artificial intelligence.

Winn Hardin: [00:10:01] Gotcha.

Jimmy Carroll: [00:10:01] Well, on the topic of artificial intelligence, it does seem like we end up talking about it quite a bit, and everybody does. And, you know, it was probably seven or eight years ago where AI just started to become really hyped. There was a lot of marketing behind it, and it was being pushed as, deep learning in particular, was being pushed in the industrial automation space as something that’s going to change everything. But it hasn’t. But in some ways now, like it’s, it’s sort of settled in and become a valuable tool for the tool set. So, I do want to ask about AI. You know, I actually, I remember a couple of years back, I was at the AI & Smart Automation Conference that A3 did, and there were a number of your colleagues there that gave presentations. One was a panel discussion, one was a larger talk, but one topic in particular that I found particularly interesting and I wanted to ask you about is this idea of AI and the hype versus opportunity. What’s your view on AI? How does an industry leader like Siemens approach AI?

Reinhold Niesing: [00:10:57] So, in a nutshell, AI isn’t reality. So, it’s not already existing. So, it was the same, for example, the discussion, how much do we need AI experts really everywhere? Can I be so contained, self-sufficient that we even don’t need AI experts in many cases? So, you see great products like we have a visual inspection system inspector. This is actually a totally integrated AI solution. So, it, it’s so far developed that you even barely recognize that AI is already in the system. But it goes, in many ways, it goes beyond this. So, this is a completely integrated AI system where we say, okay, this is a commercially available product. You can use it, you can train it on the application even if you are not an AI expert. And this is very important. So, when we go in totally different directions where you say, okay, it becomes more sophisticated generative AI and so on.

Winn Hardin: [00:11:54] I was just going to ask that. Have we evolved past the deep learning phase? Would you say that we’re in a generative AI, which is not the same as general AI, but how do you, how do you define those terms? Can you give us some kind of benchmarks? What’s the difference between a traditional deep learning machine vision system and . . . is it just its ability to actually, you know, learn autonomously, meaning to run it in teach mode without having to really focus on the training, the data sets, doing all your model, building offline before you start doing inference, right? So how do you define it?

Reinhold Niesing: [00:12:26] It’s interesting because I would split this actually into big topics. This is where I would call it traditional AI. And it’s kind of weird already calling AI traditional. So, so.

Winn Hardin: [00:12:37] That was the past couple of years.

Reinhold Niesing: [00:12:38] Exactly. So, we have all these systems where anomaly detection, visual inspections. We use that actually for quality inspection systems, and it’s all about recognition of defects, for example.

Winn Hardin: [00:12:54] Variations.

Reinhold Niesing: [00:12:55] Variations in these spaces. There are a lot of applications in this area. And this is not only always provided by Siemens. So, we provide the same in infrastructure — how to develop these applications. And the same to deploy them. This is the same part of the edge system, which is part of the automation workstation again. So, that’s how it becomes a total circle. The total different aspect is this generative AI. And this becomes now really interesting. So, it is . . . you see it already there. This is the industrial cockpit. So, you really should have a look at this. It’s fascinating now that you can talk, you can chat with an engineering system. It’s a totally different interface. So far, I guess in many cases you had to learn how an engineering system works. You had to read the specifications, and you had to implement this. There are very many times, very repetitive tasks where you say, why do I really have to do this? And why do I have to do this a hundred times? Why doesn’t the system? So, now you start actually to talk with an engineering system within Chat. You describe what you want in parts. Sometimes you even copy complete sections out of a specification and tell the system this is what I really want. And the system analyzes your requirements and starts to design the PLC project for you.

Jimmy Carroll: [00:14:11] It is interesting to see generative AI being used in, in automation systems. It seems like a topic so many people are kind of dismissive of like, well, that’s just for fun, and it is fun. But, you know, now it’s being used in really valuable ways. So it’s . . .

Reinhold Niesing: [00:14:26] It’s fun because many people use ChatGPT and see the potential there. Sometimes you can use it, for example, you’ve got a document and you want to know, what is the summary of the document? So, you submit the whole document and tell it to summarize it. In the same way, we can use our industrial cockpit actually to say, okay, we use this specification, which maybe comes sometimes from a customer, and you have to implement this, and you tell it the same way as you interact with ChatGPT. You chat with the system, tell it what you want to do, and the system starts to implement it, analyze what you are trying to achieve.

Winn Hardin: [00:15:01] So, this is engineering fun. This is fun for . . .

Reinhold Niesing: [00:15:03] Engineering fun.

Winn Hardin: [00:15:04] Because it’s making their job easier.

Reinhold Niesing: [00:15:06] We had to do this to . . . we did this together with a partner at Microsoft. And they helped us actually to make an industrial version out of this ChatGPT. There are significant differences sometimes because if you work with ChatGPT and you ask a question, if you ask the same question a week later, you get a different answer. So, in this industrial generative AI, how we use it, actually, we have to make sure that we get the same answer if we ask the same question. So there are some interactions there too, and how we actually . . .

Winn Hardin: [00:15:38] Well, it’s a more defined contextual environment, right? I mean, the data sets which gives you, which allows you to do that, and have consistency of design, have more confidence in and its ability to to generate new programs and routines based on historical things. You know, the more you talk about the new workstation, I understand more about why you use workstation in the product name. I mean, back in the day, because I’m old, you know, we think of workstations, we think of super powerful PCs. They were, they were doing efforts. But but since you’re putting this on the edge, but it also has the ability to create new models and new data sets, basically a whole new AI routines, I assume that requires some horsepower. I mean, traditionally it’s been a data-farm based to be able to generate the models, bring it to the edge, and in a much smaller processing unit. But the full functionality that you’re, you’re putting together there helps me to understand why you chose that name.

Reinhold Niesing: [00:16:28] Yes. So, it was just we were really looking for what is the right name for it because we had to think about it. What do we call this now? Because actually it’s really it’s, it’s, it’s for us actually to help the customer to implement all these different disciplines. And so we didn’t want to go to the customer and tell the customer, okay, use all these tools. And now it’s your problem to integrate them.

Winn Hardin: [00:16:48] Yeah, that doesn’t really go over well anymore. Too much complexity.

Reinhold Niesing: [00:16:52] Much complexity in there.

Winn Hardin: [00:16:53] Too much barrier to adoption.

Reinhold Niesing: [00:16:55] To do the job for them. And then they say, okay, use the edge management system and use these technologies and use them in the way that the customer benefits from it, so that he gets real value out of it. But we use our own edge system to manage ourselves. So, it’s, so we prove the value by simply saying, okay, we can manage our own device. We can keep this device up to date. We can implement the cybersecurity aspects with it. So, that helps us actually not only to manage other applications. So, we said, okay, if we are so convinced that it will help, then it should be capable of managing our own device. And that’s what we do as well.

Winn Hardin: [00:17:32] See, down South we call that eating our own cooking.

Reinhold Niesing: [00:17:34] Exactly. Yes. So, that’s what we do. Yeah. We followed this and then said, yeah, okay. So we just say, okay, it should be able to do this. And we should be able to do it for our own product.

Winn Hardin: [00:17:45] Beautiful.

Jimmy Carroll: [00:17:46] Reinhold, I always like to ask people this question, but especially people like you from from leading companies like Siemens. What major trends or predictions do you see in the next couple of years? I know, maybe that’s a difficult question, but it’s a fun one to ask.

Reinhold Niesing: [00:17:59] It’s actually simple. Digitalization. Yes.

Winn Hardin: [00:18:03] Without question.

Reinhold Niesing: [00:18:04] No, no, it’s just digitalization. I guess that’s the key topic. The digital transformation. Digital threads. How to implement the digitalization. What does it really mean for my business. Really enabling the workforce to go this direction, implement the best practices inside the different businesses. And this is a goal to make the business more profitable and make the business better. But also make the world better. This is . . .

Winn Hardin: [00:18:33] Lofty goals. But, but I would agree with you. It’s funny, we were just having a conversation the other day, just about simple, you know, cobalt welding-type applications and, you know, the general populace doesn’t really understand that companies that adopt automation, whether they’re in welding or other applications, invariably grow and then invariably increase their headcount and invariably are more profitable and invariably offer better salaries and benefits to all of their employees across the board. So, you know, it may sound lofty, but it’s ,it’s actually true. And for the people who are working in the warehouses and in the production floors and everything, I think they’re starting to really see those benefits. I think we’re going to have an attitude adjustment very much over the next few years as the rest of the world, especially in manufacturing, really . . . because so much of the small and medium enterprises, and even some of the plants in the large enterprises, you know, really don’t have that day-to-day interaction with automated solutions, but they have had difficult, dirty, and dull jobs. And a lot of these systems are are helping to make their lives better each day. So, it’s a blessing for sure.

Jimmy Carroll: [00:19:37] It is nice, too, that it seems like we’ve kind of passed that hurdle of people thinking that robots are going to take their jobs.

Winn Hardin: [00:19:44] It’s true. I mean, it doesn’t matter what you look at. Every statistical report shows that that’s just not the case whatsoever. So, it’s, I don’t know, maybe . . . It’ll be interesting to see what happens when we start seeing the humanoid prototypes that are out there on the show floor. When they’re they’re more active in day-to-day operations. That that will be an exciting, exciting development for sure.

Jimmy Carroll: [00:20:05] Yeah. We’ll see. All right.

Winn Hardin: [00:20:07] C-3PO, bring it.

Jimmy Carroll: [00:20:08] Bring it. Yeah. Well, Reinhold, I really appreciate you taking the time. I know you have . . .

Reinhold Niesing: [00:20:12] Oh, thanks for having me.

Jimmy Carroll: [00:20:13] It’s been our pleasure. I would encourage everyone to go check out the information on that new workstation and check out the Siemens website. You know where to find them. And if you have any questions, comments, or you want to reach out to Reinhold or the team, you can visit us at Manufacturing-Matters.com. We’ll be happy to pass along any information. And thanks for listening or watching.

Reinhold Niesing: [00:20:32] Yeah. Thank you.