At its production line in Saarbrücken, Germany, ZF produces about 10.000 automatic gearboxes for cars per day. During this highly complex process specific machine parts scuff out – and therefore, stop working from time to time. The machines stand still and the manufacturing process is delayed. This costs time and money.
PwC helped ZF to establish a predictive maintenance system. To run this system, ZF needed to collect and utilize the information to subtract their next step. Therefore, PwC specialists helped develop a system that utilized data gathered by sensors throughout the production line and, using AI, identifies patterns indicating tool breakage. Hence, the output indicates when ZF needs to maintain or shut down a machine. Additionally, PwC made the first step to find out why the breakage occurs and we got ideas on how to prevent crashes in the future.
After the implementation, 99 percent of all tool breakages can be supervised in real time. Thanks to data based prognosis and learning algorithms, ZF will likely be able to predict when the need for maintenance will occur.
"Through the project, ZF gained valuable insights. This will help the company further enhancing the efficiency of their production. The project was driven by our strategic relationship with Microsoft, and powered by our strategic assets in IoT and predictive maintenance."
Partner, Analytics & IoT, PwC Germany
Tel: +49 211 981-4721
Dr. Reinhard Geissbauer
Partner bei Strategy& Deutschland, PwC Germany
Tel: +49 89 5790-6138