Data analytics and Artificial Intelligence (AI) have emerged as essen-
tial tools in manufacturing over recent years, providing better insight into pro-
duction systems. Their importance can be highlighted by the way it can transform
quality control, from prescriptive to proactive. Data analytics combined with AI
can identify abnormal trends and patterns in huge amounts of data, that could
uncover potential defects and allow pre-emptive action to minimize or even pre-
vent these from happening. A direct effect of this is the contribution to waste
reduction, as well as saving time and resources. While data in a digital factory is
ample and the resources for developing artificial intelligence applications are ac-
cessible, the implementation of accurate, robust, standard, and economically vi-
able quality monitoring and assessment approaches is not straightforward. This
is also strengthened by the scarce skillset in today’s manufacturing companies in
this area. In this study, the capabilities and potential of data analytics combined
with AI are reviewed with a focus on manufacturing. The implementation chal-
lenges posed for a practitioner, as well as the benefits of implementing a solution
for a manufacturer using data analytics and AI for quality assessment are dis-
cussed, based on real-world experiences from existing production environments.
Lastly, a learning approach utilizing a high-fidelity digital twin at its core is pre-
sented which a practitioner can utilize to create, test and continuously improve a
predictive analytics model.
