One of the major issues in any automated industry is handling the error rate. As much as the machines are complex, the error rates are also going to be high which often is the case in multistage production processes. As the complexity is increased, the error rate also increases and in worst cases, can lead to high fatal consequences.
These complexities can be handled by software or cloud application where the data can be forwarded by networked machines or robots, to AI, to identify and analyze such patterns or anomalies in the production process from the obtained data.
Data that are important in the real-time analysis are from the production processes or maintenance work as the occurrence of potential malfunctions is much higher and therefore the knowledge is needed in advance to be identified and eliminated much before the occurrence to cause any fatal damage. Predictive analysis with its advanced features of forecasting such error can be strong support for reducing such error.
Optimization of productivity is possible by machine learning and also its availability during ongoing production, in terms of process quality, cycle time, energy consumption, or maintenance during intervals can be achieved.
Knowledge about short interruptions or downtimes is also important being related to the economic effect that can be avoided, and in the long run, results in higher quality with less laborious as humanely as possible.