The second use case is about error management in quality assurance in Werlte. "With the help of artificial intelligence (AI), we are setting up a sensitive early warning system in the areas of defect management and complaints. This means that all complaint data is recorded and algorithms calculate whether so-called error peaks occur in a product or component, for example. These are significant accumulations of faults. If the suspicion of a possible problem is confirmed, the product specialists take a close look at the problem and decide on the next steps," explains project manager Felix Bartels from the Krone Business Center and emphasizes: "value chAIn provides the analysis tools and uses the data to derive a recommendation for action for the employees in the specialist departments. The aim is to develop a decision support system. However, the decision-making authority always remains with the employees. Only they can classify the recommendation for action in the technical or economic context and evaluate it accordingly."

With smart foresight

The other two use cases focus on the Krone Telematics Box. Here, we are analyzing the extent to which the collected telematics data can be used for other digital services. "We are scrutinizing the digital services of the telematics box and checking whether, for example, the report data can be presented more clearly and simply. We would also like to use the telematics data to develop other useful features that make the transportation of goods from A to B even more transparent. It would be conceivable, for example, to record abrupt braking processes or strong vibrations of the vehicle due to poor road conditions, which could possibly affect the quality of the transported goods. It would also be conceivable to display the specific_{CO2 score} for a transport route. This is becoming increasingly important, especially when considering sustainability aspects," reports Felix Bartels.

Last but not least, Krone has been focusing on predictive maintenance for several years now. "The main aim here is to proactively minimize potential trailer downtimes. If, for example, the AI warns that a wearing part is highly likely to fail in the foreseeable future, this part could be replaced directly at the next opportunity as part of an already planned maintenance," explains Felix Bartels.