Fuzzy control charts represent a significant evolution in Statistical Process Control (SPC) by addressing intrinsic uncertainties in measurement and human evaluation that classical approaches ...

Control charts help you monitor the process and detect any signals of special causes of variation, such as outliers, trends, cycles, or shifts.

SPC tools like Control Charts (CC) and Time Series Analysis (TSA) offer accurate predictive analytics capabilities. CCs monitor process variation over time, detecting any deviations from the mean ...

Cumulative Sum (CUSUM) - Tracks process shifts over time. Shewhart Control Charts - Evaluates process variations against control limits. Ingredients vs. Strength - Analyzes the relationship between ...

As a result of these definitions, two cases of application of statistical process control (SPC) can be distinguished: influential factors are not fully identified: we need to observe process ...

Statistical Process Control Techniques Individual-Moving Range (I-MR) charts for monitoring PCA scores. Hotelling's T² control chart for multivariate analysis of original data and PCA scores.

Linguo Gong, Wushong Jwo, Kwei Tang, Using on-Line Sensors in Statistical Process Control, Management Science, Vol. 43, No. 7 (Jul., 1997), pp. 1017-1028 ...

This course addresses the basic theory behind Statistical Process Control (SPC), a method used in monitoring and controlling the quality of a process through statistical analysis to reduce variation.

Revision of statistical process control procedures, evaluation of control chart performance and statistical design of charts, control of high quality process, multivariate process control, new process ...