Allows the assessment of plants by Key Performance Indicators (KPI). The subsequent optimization is carried out using statistical methods based on "R".


  • Easy integration of statistics in WinCC OA projects

  • All operations inside SCADA, without any programming

  • Guided way to train the model

  • “R” operations inside SCADA environment


Data mining is used to select relevant data from a large amount of data. Here the connections and dependencies between different process values are recognized and used for further classification. When classifying the selected data, results are linked and states are automatically recognized and named by the user. Statistical models are generated from this analysed data. These models can then be used for optimization in the plantThis can be done both with historical data and with real-time data.
In addition to the existing KPI, data mining and classification functions, SmartSCADA also provides a generic interface to "R", which allows data from the SCADA system to be processed directly using statistical methods. "R" is a free programming language for statistical data analysis.

Use cases

  • Root Cause Analysis

  • Define KPIs, assign KPIs to devices with one click

  • Find dependencies by using correlation, find timeframes by filtering values, direct handover to statistical calculation

  • Classification based on a statistical model

  • Reuse model any time e.g. for new KPIs


SmartSCADA is industry-independent and can be used for any application. Examples include wind power plants, power supply systems or large production plants.