A Forecast-based Monitoring Methodology for Process Transitions

Nonconfonning parts are often produced when a process moves from one level to another due to transition events. Control charting, when applied to a stable state process, is an effective monitoring tool to continuously check for process shifts or upsets. However, the presence of transition events can impede the nonnal perfonnance of traditional control chart with increased false alarms. The presence of autocorrelation also requires modification to the control charting procedure. We present a methodology for characterizing the process transition which involves a tracking signal statistic, based on the forecast-based exponentially weighted moving average (EWMA). This test will supplement the forecast-based EWMA control charting as a means of detecting when the transition event is complete. Such a procedure facilitates smooth application of the appropriate control chart by knowing when the transition is over. The transition characterization methodology also carries benefits in cost and material savings. We use a color transition process in plastic extrusion to illustrate a transition event and demonstrate our proposed methodology. Simulation is employed to evaluate the perfonnance of the methodology. Copyright @ 2001 John Wiley & Sons, Ltd.