German gearbox manufacturer
What was the problem?
This company manufactures gearboxes for a lot of major brands in its plants. Parts of the manufacturing machines suffer from wear and tear during the process. This wear and tear is gradual, which means that before breakage accuracy is decreased below acceptable levels already. When the machine breaks it has to be repaired immediately to continue production. Furthermore the batch that was produced just before breakage has to be disposed due to low accuracy causing an inacceptable low quality. This means that extra quality control is needed to check which batches need to be thrown out after every breakage.
What was the solution?
The machines’s sensors collect all sorts of data, such as temperature and vibration levels. The data analysts at PwC analysed this data in conjunction with the machine repair logs in order to make accurate predictions about when specific parts would break. The predictions based on data analytics are now used to replace parts preventively, before they break.
What was the result?
The machine repairs can now be scheduled because they do not occur unexpectedly. No manufactured parts have to be destroyed after a breakdown because quality levels are guaranteed. As a result, the costs of quality assurance checks also decreased. Using a comparable approach, PwC saved a major airline tens of millions of euros on aircraft maintenance. Delay and cancellation costs are avoided because component failure became predictable.
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