ET in Dialogue - Peter Korte CTO Siemens India

Yeah, there's many differences but let me pick three. The first one is industrial AI where it's being used. Obviously it's not in the consumer space but in the industrial space where we talk a lot about physical AI, meaning making the physical infrastructure better, more productive, more resilient. The second one is that the AI we're using very often is mission critical, it needs to be very safe, reliable, trustworthy. It must not hallucinate. The third one is the way we develop this AI because industrial AI is not usually based on words and language that you can train on by going to the internet and downloading web pages. Actually it's trained on engineering data, production data, and usually you don't find that easily available. So the way you develop these models is very different than in the consumer space.

Yes. The number one thing where they see this is usually in energy and energy consumption and where we can cut a significant amount of energy that's being consumed. One of the most obvious ones for that are the energy heavy industries such as buildings as we're sitting in here right now.

And so we want to feel comfortable as we do right now and there's a lot of energy wasted today that goes into cooling of buildings. We've developed an application or an AI that is called Comfort AI that takes a reading every 50 minutes, learns, updates itself and cuts energy cost by a third and it's very easy to deploy. That's a great example of how we make infrastructure more intelligent thanks to AI. And that's not limited to buildings. You can go into cement industries where we look at energy and where we can cut it. So usually this is very stark but it's also in industries where time to market is very important and where we can help to significantly accelerate the development and the production cycle.

Yes.

Yeah. So the AI that we provide is usually through a platform called Siemens Accelerator. Think of it as a portfolio of software that enables our customers to achieve their results. This is where the AI is embedded. This is where we would be working with applications that are all AI powered and that help us to design better ways of how to produce cement or how to operate cement mills or buildings. For example, here we got a building. The application is called Building X and it's part of a SaaS platform where the AI is embedded and has algorithms to optimize operations.

Yeah, the building blocks are essentially lifeblood of that is having access to data. If you think about it today, if you go into a factory, in particular a brownfield factory that's been there for many years, usually there's very little data because these machines are manual, mechanical, and there are no sensors. So the first step is: do you have enough data to understand the shop floor? Can you digitize that? Very often we apply sensors, vibration sensors that tell us about the condition of the machine or machine vision that looks at material flow. Once you have connected all the sensors, then the question is aggregation of that data into a data layer with its own ontologies and semantics. Then comes the model layer where we put the model on top. Usually large language models but not always; sometimes traditional machine learning. Then you have the reasoning layer that makes sense of the data based on a human machine interface. These are the things that have to happen. At Siemens we believe if you want to be successful in the industrial world you have to have the whole stack. You cannot just do one layer. The key word is domain knowledge. You need to understand which data you need.

I give you an example. Trains, we also produce trains. On the train you would think maybe it's the propulsion systems or the brakes or whatever. But it turns out it's the doors because the job of a train is to transport people. It has to stop, open doors, people go in, doors shut, train goes to next station, open again. Very often these doors fail and are major cause for delays. Today we connect to all of these doors. We put sensors into the motors that drive the doors and we can see on the voltage curves whether the motor degrades and we can tell you 10 days in advance whether they're going to break. So these are very good examples that show you need to understand the domain because the use case drives the kind of data you need and that drives the application.

Yeah.

It is by having pretty much as you outlined it's building blocks. I think the word exactly the right word is we tend to think of microservices. You have a microservice that does the ingestion of data, a microservice that ensures the data lake is accessible and stored, a microservice that ensures data security with regards to cyber security. All these are components that we stitch together. They are part of what I call Siemens Accelerator where you have microservices. We build them together because we reuse them over and over again. You need to have a data layer no matter if it's buildings or cement mill. However, the environments are very different but the general logic is pretty much the same.

So the idea and this is in the process where we are. In the past, how was a digital twin created? Usually created by greenfield applications. You wanted to build a new building, new machine, car, plane. You had the digital replica first, the twin, and then the physical part. They coexisted next to each other and you had versioning issues because one of the two would change and they would come out of sync. What we see now is that the two worlds, the digital twin and the real object, are connected because of data and sensors. You have a continuous update. With that you can take a factory as an example. You always know almost in real time the condition of the machine, where the material is, where the worker is, what job is being manufactured. Now the AI can do time travel. It can go backwards and forwards by simulating what may happen. It can go backwards and show all failures with material shortages and predict that at station 8 the screws are getting low, raise an alarm. That's the predictive power. You can react or manually intervene. For example, semiconductor shortage a couple of years ago. Now you can run what-if scenarios: if this type of material is missing, can I still run the factory? Can I run something different based on the material at hand? All AI powered in real time. Thereby you have higher throughput, higher uptime, higher productivity.

You don't start on it if you don't think there's an ROI. If you don't see this clearly, you should not start on it. Proof of ROI is a good point. As Siemens, we would tell you that we expect you to save a third of your energy cost on the building example. That's the key driver. As a customer, I would trust Siemens that you know what you're talking about. The hardest part is usually the connectivity. It's really to connect your real world to the digital world. Very often a technician is missing who is able to translate the IT world to the operation technology world. So usually it's the data availability.

Actually surprise it's the AI.