Practice makes perfect

Predictive quality analytics (PQA) are a game changer. After all, the aim is to identify deficits even before real costs are incurred. However, this is an analysis with many unknowns. Analog computational methods quickly cut their teeth on this. With machine learning (ML), however, a technology is now available that is capable of reliably decoding even the most complex distribution patterns. But before an ML algorithm can deliver accordingly, it needs appropriate training. From the perspective of the implementation teams, these five things in particular need to be taken into account: