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Allgaier Group

Autonomous maintenance  goes digital


“With the solution that is now available from nuveon, employees in production receive digital support in the form of images and instructions. The focus here was on simplicity for the workers.”

Berat Özdemir, Team Leader Car Body Production at the Mühlhausen site


The challenge

To date, autonomous maintenance (AIH) has been carried out and documented at the Allgaier Group using paper-based checklists. Detected deficiencies were communicated and processed using so-called deficiencies cards. The archiving of the results took place in informal paper collections.

Autonomous maintenance, also referred to as operator maintenance, includes the regular and systematic cleaning of the equipment by the production staff in order to ensure the functionality and service life of the systems over the long term.

As Mr. Özdemir, Team Leader Car Body Production, said: "Previously, the maintenance and cleaning activities took place in the form of printed lists at rather irregular intervals and without any archiving."


The solution

By using nuveon's maintenance module, the AIH is now carried out completely digitally and with intelligent support.

The employees who carry out the cleaning and testing activities during the system shutdown are navigated to the respective maintenance points via a tablet. Once there, the stored safety and cleaning instructions must be read and confirmed. Corresponding images are also stored for each cleaning point, which visualize to the employee what the respective system components should look like correctly. A visual target/actual comparison can be carried out directly in the system  !

In the event of a defect, the maintenance item is marked as "inadequate" and described in detail in a text field. If required, a photo or video can be loaded into the application or maintenance point via the tablet's camera module. If there are no abnormalities, the maintenance item is completed with "without defects". 

In the so-called "maintenance plan" view, the respective system is displayed with all its defined maintenance points. The checkpoints that have already been processed are marked in color and further displayed via a red/green logic for not ok and ok. This enables a cleaning run with two or more tablets. At the same time, maintenance or team leaders receive a visual insight into ongoing maintenance via any device via browser.

Once the maintenance run has been completed or had to be canceled again, an automated e-mail notification is sent to a defined distribution group with the information that a maintenance run has been completed on the system. The recipients are sent directly to the evaluation via a link, where all the details (e.g. list of defects, duration, etc.) can be viewed.

Once the production staff has completed the maintenance run, the defects are processed by the maintenance staff. They also carry out their work completely digitally with tablets. We are talking here about so-called "maintenance cases", which can be set to the status "closed", "in progress" or "blocked". An e-mail option is also available, with which the maintenance technician can send and comment on the respective maintenance case to any e-mail recipient (e.g. to order a necessary spare part).

With this new data, it will be possible in the future to evaluate different trends in plant wearing parts, which can indicate a defect in the plant design, for example.


After the successful implementation on a robot component system and presentation to the management, the rollout to all other systems in the areas of component production and the press shop is now being planned.



The logging of maintenance work is now much more efficient. Detected problems can be easily communicated and thus resolved promptly. What also contributes to efficiency is more reliable detection of defective equipment through visual comparison of an "OK" item of equipment.

One benefit of the nuveon mHub platform is the direct and timely electronic routing of repair actions into the maintenance tool, which helps with the accuracy of the repairs required. The fully electronic documentation and archiving of the cleaning and repair measures (ua  elementary for IATF audits) facilitates the search for past maintenance work, which can help to make wise decisions about future maintenance work.

Deep learning of the system can be possible by evaluating trends (e.g. increased wear div.  system components can indicate a deficiency in the system design).

Mr. Özdemir confirms the effectiveness of the mHub maintenance module in your production process: "Anomalies or defects are forwarded digitally to the maintenance staff and can thus be remedied before an imminent system malfunction occurs."

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