{"id":40558,"date":"2026-06-17T16:08:27","date_gmt":"2026-06-17T14:08:27","guid":{"rendered":"https:\/\/hitech.at\/den-erfahrungsschatz-heben\/"},"modified":"2026-07-27T13:41:56","modified_gmt":"2026-07-27T11:41:56","slug":"leveraging-a-wealth-of-experience","status":"publish","type":"post","link":"https:\/\/hitech.at\/en\/leveraging-a-wealth-of-experience\/","title":{"rendered":"Leveraging a wealth of experience"},"content":{"rendered":"\n

The Siemens research group Sustainable Automation Solutions is small but mighty: Based in Vienna, the team focuses its research on IT-OT integration, particularly in light of pressing future topics such as AI and business process mining. Since its founding in July 2024, the team led by research group leader Michael Hartl has been working to integrate artificial intelligence into the shop floor — that is, into factory operations. “The basic idea was to bring current challenges — primarily those related to AI projects — down to the data level,” says Siemens Technology Professional Steffen Schuhmann. Specifically, this means finding a way to extract information from the shop floor’s data layer, process it, and make it usable in a way that generates profit. “This ranges from metadata enrichment to generative AI systems — specifically, agent-based AI,” explains Schuhmann. “We want to develop a cohesive narrative, so to speak, and we’re aligning our research priorities with this objective.”<\/p>\n\n\n\n

While agent-based AI systems autonomously plan tasks, make decisions, and proactively carry them out to achieve complex goals with minimal human supervision, business process mining — one of the key research areas — builds on these capabilities. Schuhmann: “Agent-based AI systems have the disadvantage that they may invent information or answers when the available database is insufficient — a characteristic that is, in truth, of no use whatsoever in industry, and all the more so the more autonomously the systems are intended to operate.” This is where business process mining comes in. Essentially, this is a step in data preprocessing. “It is possible to make deterministic statements about the database and reconstruct processes to see how operators have interacted with the system in the past.” This information provides the necessary input for further processing.<\/p>\n\n\n\n

Business Process Mining<\/h2>\n\n\n\n

Allowing artificial intelligence to make decisions on its own is therefore proving to be the wrong approach. “Through business process mining, the actions employees have taken in the past and the experiences they have gained are incorporated into the AI’s future decisions,” the expert says. Business process mining has its origins in IT, where efforts are made to reconstruct and evaluate business processes from existing data — in a wide variety of ways. This reveals how employees behave in practice. Do they follow the predefined processes? Where and why do they differ? Does this cause any problems? Is work piling up in certain areas? “Through our research and the DCS Advisor — an AI-powered support system — we’ve brought these topics to the process industry and, in particular, to OT-IT integration,” explains Steffen Schuhmann. The DCS (Distributed Control System) Advisor is a proof of concept, a joint pre-development project with the Data Analytics & AI research division at Siemens Austria.<\/p>\n\n\n\n

In this way, the research team was able to connect on a deeply human level. When we look at demographic change, we can see that many employees in a certain age group will be retiring in the coming years. Schuhmann: “That led us to ask how we could preserve, reconstruct, and organize their knowledge for the company and make it usable for the future.” Consequently, special attention is paid to the data on interactions between operators and the systems. This results in “action patterns” that reflect which actions were taken in the past — for example, in response to an alarm. Based on this data, new employees receive a “recommendation template” for their own actions, into which the wealth of experience of former colleagues is incorporated almost automatically. The data comes from the DCS, such as the Siemens Simatic PCS7 process control system, and ideally covers as long a time period as possible. “The statistical distribution provides us with accurate patterns of behavior,” the technology specialist points out. Operating errors, which are rare but do occur, have no effect due to the weighting.<\/p>\n\n\n\n

\"\"© Siemens<\/span><\/span><\/figure>\n\n\n\n

The research group led by Michael Hartl (center): Ivan Svogor, Research Professional (left), and Steffen Schuhmann.<\/p>\n\n\n\n

The algorithms developed by the research group — which form the basis for the DCS Advisor — are currently being put to the test in two customer projects in the food & beverage and chemical industries. Steffen Schuhmann: “Our development has generated a great deal of interest, and our collaboration is very close.” We were also given a very clear demonstration of just how crucial experienced employees are to a company: Through the measures they implemented in response to alarms, they were able to maintain the operational readiness of the systems and ensure sustained productivity. What is remarkable is that these measures did not appear in this form in the theoretical analysis of the system. “However, we have now been able to incorporate these phenomena into the algorithms.”<\/p>\n\n\n\n

The results of the Sustainable Automation Solutions research group are particularly beneficial for the field of training and education. New employees can quickly and easily learn how to respond to alarms and emergency situations in the best possible way, even when no certified experts are available. In addition, many companies have “jumpers” — that is, employees who work at several different facilities. They, too, can benefit from this valuable information. “In the future, we’d like to explore the integration of additional systems, such as MES—Manufacturing Execution Systems,” Steffen Schuhmann explains. “We want to move into the actual production process and, based on historical process data, enable agent-based AI to draw the right conclusions for current production.” This includes — and this is where another research project for the pharmaceutical industry comes in — supplementing a historical database that is insufficient with AI-supported simulation, thereby verifying the plausibility and safety of the set parameters. This is currently being tested in the realistic production environment of the Living Lab at Siemens City in Vienna. There, in collaboration with customers, operations in the process industry can be mathematically modeled, simulated, analyzed, and optimized based on the data generated in this way. This makes it possible to digitize not only the core production process, but also the entire process chain.<\/p>\n\n\n\n

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Business process mining is used to develop AI-powered support systems that incorporate the actions of experienced employees \u2014 offering benefits for training or emergency management. 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