The management and analytics of the data comprises several levels typical of these sectors, covering all the phases from least to most difficult, but all of them useful depending on the actor involved.
Our work methodology for data management and analytics
Data acquisition ranges from current solutions in the industry, those coming from automata (PLC), dataloggers for telemanagement systems or data acquisition systems (SAD), to the new options in sensors and communications that the IoT brings us.
Depending on the needs, data storage could be local in the company’s facilities or using the new Cloud options for large amounts of information.
The integration of the different sources will allow us to draw conclusions from the data, achieving a complete vision and ending information silos.
The generated data obtains value when we have unified and contextualized it. Even more so when we relate them to each other directly (ratios) or through data mining techniques that allow us to discover associations and correlations.
The visualization is based on our experience in SCADA supervision systems that allow us to see the status of the facilities in real time, as well as the alarm and historical log.
We develop data management and analytics in these sectors
- Obtaining calculated ratios
- Obtaining industrial indicators (OEE, MBTF, MTTR)
Manufacturing Intelligence to unify production data, energies and production orders
- Machine learning for early incident detection