“Until now, we had been collecting tyre data manually using a hand scanner,” says Klaus Kleinfeller, service manager at Karl + Co. “But the data collection and assessment of the current condition easily took three to four minutes per tyre.” Like many car dealerships and tyre dealers, Karl + Co. was previously concerned that newer technologies that could partially automate this process would require lengthy employee training due to their complexity and therefore be more of a hassle than a solution. Last year, however, the Mainz-based BMW dealer decided to no longer accept the relatively high risk of measurement inaccuracies and transmission errors that the manual procedure inevitably entails. After all, the resulting extra work puts a strain on the entire company as well as the individual employees.
AI learns independently on the job
“At the time, we were looking for innovations that could help us improve our internal workflow,” says Kleinfeller. “Since we were already using a tread measurement solution from GL Technology GmbH, we decided to also use the TireScanMobile app as an extension and to make our daily work even easier.” Just a few days passed between ordering and setting up the technology. “Our staff got comfortable with the app after very little training thanks to its user-friendly functions,” Kleinfeller adds. The GL Technology customer decides whether the app is installed on existing devices or supplied with suitable mobile equipment.
Tyre recognition is conducted mostly automatically via the app, which greatly reduces the staff’s workload. “The tyre fitters simply take a photo of the tyre to be inspected using the mobile device, and the app does the rest,” explains Steffen Klein, the managing director of GL Technology GmbH. Using AI-based object recognition, the system independently determines all relevant tyre information such as the manufacturer, model, tyre dimension and other important information such as an existing OEM code or any run-flat properties of the tyre. All data and photos are then digitally saved as an overview report – entirely without paperwork. “The recorded measurement sizes are then seamlessly transferred via Wi-Fi into our document management system,” says Kleinfeller. The app also has a QR code scanner that can record all order and vehicle data at once so they never again have to be entered manually. “Our TireScanMobile app is also able to run completely independent of all tread measurement tools and be able to transfer data directly to our ERP system,” Klein adds.
Autonomous learning paired with flexible combination options
Through continuous use, the AI is automatically trained and learns to recognise and evaluate new and external tyre data even more effectively. “The more we use the system, the faster and more reliably it recognises the different models and information to be recorded,” says Klein. “This will help us design the AI for further promising areas of use.” So far, the app has primarily been designed for car tyres, but is currently also being trained to record truck tyres. Companies that specialise in larger vehicles or fleet management could also use the app in the near future to make daily documentation work easier.
The app is also customisable in terms of its compatibility with other GL Technology GmbH products. “In our work, we also use, for example, the StackedTireProfiler tyre tread measurement tool from GL Technology along with the mobile TireScanMobile app,” says Kleinfeller. This measurement system is designed for the precise tread measurement of an entire stack of tyres – in just a few seconds. The car owner can thus be informed immediately on site about the condition of the entire set of tyres and advised of any need for new tyres. “With our specially developed measurement solutions for complete wheels and tyres, we want to make day-to-day work easier for companies in the automotive aftermarket,” Klein says in summary. “With the TireScanMobile app as an independent app or expansion to existing systems, we are finally bringing tyre documentation into the digital age.”
