App settings:

AI-supported Service-Engineering


Patterns in the machine data are only assigned to possible defects based on simple rules, without deriving precise recommendations for action for the service employee.


Development of an AI solution based on existing machine and service data.

Added value

Support for the service employees with further information, which means that service cases can be processed more efficiently.

Diebold Nixdorf is a leading provider of IT solutions and services for retail banks and trading companies. The product portfolio ranges from ATMs and software to services. As part of the AI ​​marketplace, AI procedures are to be developed and trained in the field of cash automation, based on historical machine data and Information about so-called service calls.

Understanding ATMs through data

ATMs are highly complex mechatronic systems whose behavior is monitored by a large number of actuator and sensor data. This data is supplemented with information on a service platform, providing a complete picture of the machine. So far, patterns in the machine data have only been identified by simple rules for possible defects mapped without giving differentiated repair instructions to the service technician and without verifying the service instructions through the results of the technician's assignment.

Data analysis and AI ensure intelligent production planning

Diebold Nixdorf is now researching use cases together with the Fraunhofer IOSB-INA in order to develop a suitable AI solution based on existing machine and service data. The aim is to support the company's service employees with further information on repairs during service calls. This information is currently obtained from data pots such as service calls, service contracts or machine data. The product portfolio is now to be rounded off by new services.

AI saves time when service is required

With this project, Diebold Nixdorf is aiming for a significant reduction in the processing time of service cases when technicians are deployed. The method can also help bring about a reduction in the service call rate. In addition, interfaces to the AI ​​marketplace platform are being developed, taking into account the validity and meaningfulness of the database and the design of AI applications for product development.