Patrick, what lies behind the buzzword Smart Factory?
For me, Smart Factory is primarily about machines, production systems and higher-level systems no longer operating in isolation, but being connected and exchanging information. A modern machine doesn’t just produce parts. It also generates vast amounts of data: production figures, process values, quality data, energy consumption, faults and status information about individual components. A smart production environment makes this data useful. The resulting insights help increase transparency across production processes, reduce downtime, improve quality and plan maintenance more effectively.
So, having lots of data doesn’t automatically make a production facility smart. It’s really about what you do with it?
Exactly. Generating and collecting data is only the beginning. It’s only useful if I know what information I can extract from it and what I want to achieve with it. That means processing the data appropriately and making it available so that it delivers real value.
When people think of Smart Factory, they often picture robots, screens and artificial intelligence. Is that too narrow a view?
Definitely. A robot or a modern operator panel doesn’t make a production facility smart on its own. AI is also just a technology to begin with. What matters is how everything works together.
A machine needs to know what it is supposed to produce. Production and quality data must be available wherever they are needed. Systems such as machine controls, MES and ERP must be able to exchange information.
AI is certainly playing an increasingly important role. But before I can use AI effectively, I need the right data foundation. If machine data isn’t recorded in a structured way or different systems can’t communicate with one another, even the best AI won’t achieve much. That’s why, for me, Smart Factory starts with something much more fundamental: standardised interfaces, structured data and seamless connectivity.
So, the foundations need to be in place before AI comes into the picture?
Exactly. Of course, you can implement individual AI applications. But without the right foundations, their benefits will remain limited. The better the data is structured and the more effectively systems are connected, the more opportunities emerge for data analysis, optimisation and, ultimately, artificial intelligence.
What does Smart Factory mean from the perspective of a machine builder like aerne engineering?
It means that an automation solution can no longer be viewed in isolation. Our job doesn’t end with a machine that reliably automates its process. Increasingly, a modern system must also integrate into the customer’s digital production environment.
This includes providing production and quality data, retrieving order or product information from higher-level systems and making machine status information transparent. Information should be available automatically wherever it is needed, and, above all, in a format that makes sense for its intended use. An operator needs different information from a production manager or a maintenance technician.
At the same time, the machine should be able to respond as autonomously as possible to different products and production scenarios.
If a company says today, “We want to make our production smarter”, where are the biggest opportunities?
The first priority is data transparency. Many companies already have enormous amounts of data, but they don’t make consistent use of it. If, for example, I can identify when and why a machine stops, which faults occur most frequently or how cycle times change over an extended period, I can derive very specific improvements.
So, companies don’t necessarily need to invest in new technology. They first need to understand what is happening in their existing production environment?
Absolutely. Before thinking about new technologies, it’s worth asking a few questions: What information do we already have? Where are we losing time? What information is missing? Are there causes of downtime that we don’t fully understand?
Once these relationships become transparent, it’s much easier to make targeted decisions about where changes will actually make a difference.
Transparency is the first major lever. What comes next?
Connectivity, without a doubt. Data should not have to be entered manually multiple times or transferred from one system to another. If a production order is already available in the ERP or MES system, the machine should be able to retrieve that information. Conversely, production and quality data should automatically flow back into the higher-level systems.
That’s precisely why open, modern interfaces are so important. We can’t know which systems our customers will be using in five or ten years. But we can ensure that machine data is structured and accessible.
So standardisation is another important factor?
Exactly. In established production environments, there are often many different machines, interfaces and data formats. The more we standardise these, the easier it becomes to build additional applications on top of them, including data analytics and artificial intelligence.
Implementing all of this sounds like a huge undertaking. Where should a company start?
First, I would recommend not treating Smart Factory as one enormous project. Instead, start by asking a few specific questions: Where are we losing time today? What information are we missing? Which manual tasks could we eliminate? Where do errors occur because information has to be transferred multiple times?
The most effective digitalisation projects usually emerge from these concrete requirements. Smart Factory then becomes less of an overwhelming challenge and more of a gradual development in which individual processes become smarter and more interconnected, step by step.
Let’s jump ten years into the future. Will we still be talking about Smart Factory then?
I think we’ll talk much less about whether a production facility is “smart”. Much of what we associate with Smart Factory today will have become standard practice.
Machines will communicate more extensively with one another and with higher-level systems. Data will be available more seamlessly, and AI systems will help us analyse large volumes of information, identify faults at an early stage and optimise production processes.
Thanks to robotics, image processing and AI, production systems will also be able to respond more flexibly to changing products and situations, without every possible variation having to be fully pre-programmed.
And what about people? What role will they play?
People certainly won’t become redundant. But their role will change. Instead of carrying out every process step themselves or analysing every fault individually, they will focus more on monitoring, decision-making and optimisation.
I therefore see AI and automation primarily as supporting tools. In mechanical engineering in particular, the practical experience of operators, engineers and maintenance specialists remains enormously important.
In my view, the truly “smart” production facility won’t be one without people, but one in which people, machines and software work together as effectively as possible.
What does all this mean for the future of aerne engineering’s systems?
That’s an exciting question, and one we’re currently exploring in depth. For us, it’s about no longer viewing our machines and systems as individual, self-contained units, but as part of a connected production environment.
This means considering from the very beginning of a project how information will be structured, how it will be accessed and how the system can be integrated into the customer’s existing infrastructure.
Can you be a little more specific?
We’re currently implementing a new digital solution of our own in some of our first systems. It addresses precisely these challenges. We’ll be sharing more about the technology behind it and the opportunities it creates soon.
