Data analysis and statistical tools available for process optimization make it possible to effectively diagnose and troubleshoot poor performance. The model is typically derived from the frequency response, which in turn is derived from analysis of real time data. A variety of data analysis software tools can derive the model of the controller based on real-time data. Once this happens, more advanced statistical analysis tools can evaluate the real-time data relative to controller performance and equipment performance. Regardless of whether analysis is focused on controller tuning or equipment behavior, the goal is to reduce variability to the greatest extent possible.
Standard deviation is the square root of variance.
Variability is expressed as a percentage of the mean and so allows comparison between the level of variability in different processes. Open loop analysis is useful for identifying disturbances that might be caused by defective or poorly designed process equipment, and closed loop analysis is a good measure of controller performance. Robustness plots graphically show the tradeoff between tight tuning and stability. Using robustness analysis and understanding the requirements of the process, the controller can be tuned according to the best behavior for the application. Correlation is also used to determine whether variability is introduced by controller tuning or by process equipment issues.