When it comes to analyzing large numbers of production assets, we use critical industrial data stored in factory historians and IoT platforms, combined with sophisticated, self-improving machine-learning algorithms. These are powered by deep condition-monitoring expertise and provide unique insights into the health of each monitored machine.
Doing so allows our customers to anticipate future problems.
But while this task makes use of plentiful quantitative information and cutting edge AI, predicting the future of manufacturing requires more qualitative insight and is prone to more of the vagaries of human interpretation.
