Recommendation systems can suggest suitable solutions during the entry of new issues, thereby contributing to knowledge management with minimal effort.

Knowledge transfer is still a problem area in companies today: the effort required for structured knowledge management is offset by an incalculable benefit, and the knowledge is spread more widely. As a result, work steps that serve knowledge management are often the first to be cut.

Recommendation systems can provide support here by extracting the best from the existing data. In day-to-day work, data on problems and their solutions is generated, for example, in quality management, digital shopfloor management, or open-item lists. A recommendation system can access this data and, when a new problem is entered, filter out the most similar one and thus contribute a proposed solution.

We show which recommendation systems best fit company data.