The goal is to forecast the course or outcome of any service case as early as possible. To achieve this, we first analyze and cluster historical data from MASTERSERVICE. Then, different models for predicting the case outcome are tested, including a Natural Language Processing (NLP) model trained with the free text descriptions of the tickets.
In other models, additional features, such as device type, age, previous repair history, are also considered. As a result, the prototype can predict the most frequent causes of faults with a very high accuracy - even before the defective device arrives at Würth.