Systematized Experiential Knowledge Revolutionizes Maintenance

Industrial maintenance is undergoing a paradigm shift: while production processes have long been digitized and standardized, troubleshooting often remains an area of uncertainty. Unplanned downtimes require quick decisions—frequently based on individual experiential knowledge that is not systematically captured. Digital assistance systems like DE-maintain by DE software & control GmbH provide a solution: they make knowledge accessible, structure processes, and enable sustainable, predictive maintenance.

From Reacting to Understanding

Planned maintenance follows clear procedures, but unplanned disruptions are dominated by unpredictability and time pressure. “Maintenance technicians must identify symptoms, analyze causes, and develop solutions independently—much like a doctor who must derive the correct diagnosis from various symptoms, often without complete information,” explains Friedrich Steininger, Managing Director of DE software & control. However, this knowledge is often only stored in the minds of individual employees. If an experienced colleague is absent, valuable know-how is lost, and downtimes increase.

Knowledge as a Production Factor

Experiential knowledge is a critical success factor: certain maintenance technicians resolve disruptions faster because they rely on implicit knowledge—such as typical error patterns or proven solutions. Yet, this knowledge is rarely documented. “Typical statements include ‘this is how we do it’ or ‘from experience, I know.’ This is where enormous potential lies, which has so far remained untapped,” emphasizes Andreas Tobisch, COO of the company. Without systematization, maintenance remains reactive, errors recur, and learning processes do not take place.

Digital Assistance Systems as a Game-Changer

This is where solutions like DE-maintain come into play: they guide maintenance technicians systematically through symptom identification and provide context-specific hints about possible causes and measures. A central knowledge database stores documented experiences and makes them accessible to everyone. “Employees transition from help-seekers to independent problem-solvers,” says Tobisch. At the same time, experienced specialists are relieved, as their knowledge no longer needs to be passed on personally.

Future developments aim for stronger integration of AI-supported analyses and automated data collection. Historical data enables predictions to detect disruptions early and act preventively. In the long term, maintenance will no longer be reactive but predictive and data-driven, according to Steininger. Structured fault reports and machine learning methods create reliable analyses that prioritize maintenance measures and prevent production downtimes.

Digital assistance systems like DE-maintain make maintenance more transparent, efficient, and predictive. They reduce dependence on individual knowledge carriers and create a learning organization in which knowledge is continuously expanded and utilized.

You can read the article here (German): WARTUNG UND INSTANDHALTUNG