Our use case will show how we can use captured data from sensors together with machine learning algorithms to perform a more precise prediction of when different consumable parts (like brake pads etc.) need to be replaced.
The target for this is to reduce unnecessary waste of such parts by too early replacements while still keeping a required safety margin before the parts are completely worn out.
The team will be run by Sigma Technology as a SAS partner in cooperation with Volvo Group.
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