Time-Series Modeling for Statistical Process Control

Abstract

In statistical process control, a state of statistical control is identified with a process generating independent and identically distributed random variables. It is often difficult in practice to attain a state of statistical control in this strict sense; autocorrelations and other systematic time-series effects are often substantial. In the face of these effects, standard control-chart procedures can be seriously misleading. We propose and illustrate statistical modeling and fitting of time-series effects and the application of standard control-chart procedures to the residuals from these fits. The fitted values can be plotted separately to show estimates of the systematic effects.

KEY WORDS:

• Shewhart charts
• CUSUM charts
• EWMA charts
• ARIMA models
• Special cause
• Common cause