In most companies, the knowledge of long-standing experts is a decisive factor for success – however, this knowledge must not only remain in the minds of the experts, but also be effectively passed on in order to ensure sustainable success.
The Siemens electric motor factory in Bad Neustadt manufactures 500.000 motors in 30.000 customer-specific variants every year. In maintenance and technical service in particular, valuable knowledge and experience has been accumulated over many years, but this often remains in the minds of individuals. To ensure that this expertise is not lost, Siemens is focusing on the structured preservation and scaling of this knowledge.
Siemens Bad Neustadt opted for findIQ GmbH (findIQ) for knowledge transfer. Not only for production optimization, but also beyond that, as a global partner, for three main reasons:
Fast and intuitive use: The findIQ solution was quick to implement and achieved results in the short term, thus ensuring the transfer of know-how across shifts. The rapid acceptance by machine operators, technologists, and maintenance personnel was due to its ease of use and clear, precise instructions.
Specialists in technical systems knowledge: The solution fills its own “AI niche” at Siemens and has a clear distinction, for example, from the use of MS Copilot. While MS Copilot helps to process more open-ended questions based on existing data, it is not suitable for use cases that require correct and precise answers, which are more based on tacit knowledge—such as how to deal with machines and their errors. This is where findIQ comes in. The “digital service expert” specifically closes the gap left by MS Copilot when it comes to precise, knowledge-based tasks. While MS Copilot works generically and data-driven, findIQ provides context-specific expert knowledge for specific industrial challenges.
There are no technical or organizational limitations: the strategic relevance was determined based on operational successes, and further possible areas of application were identified. findIQ is now being expanded globally to other machines, other locations, and other areas.
“By using findIQ, we were able to reduce the average time for error diagnosis from four hours to just one hour – that's a time saving of 67%,” says Peter Zech. To achieve this, knowledge about specific error patterns and problem solutions was first structured and transferred to a digital knowledge base. In the future, this will enable all service employees to solve numerous problems that were previously only known to individual experts.
How it works: The AI underlying the findIQ solution can process previously untapped knowledge in particular and take into account mechanical, electrical engineering, and process-oriented knowledge. Unlike competitors' solutions, documentation is not carried out in complex error trees. Knowledge retrieval does not result in empty search results either. Step-by-step instructions guide employees through the troubleshooting process, with the algorithm providing guidance. Unlike language models, for example, the solution is also tailored to industrial reality. This enables it to provide assistance and immediate results with comparatively little data or knowledge. It also ensures that the knowledge base continues to learn and optimize itself correctly over time and remains closed via a review system. “Within 24 weeks, we were able to store 24 machines as a knowledge base in the software, which we can now work with,” says Peter Zech.
Siemens demonstrates how the targeted use of innovative technologies can not only secure knowledge, but also increase productivity and reduce machine downtime. Sharing knowledge is not only an advantage—it is a basic requirement for the future viability of any company.