GROUPED DATA EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHARTS

In the manufacture of metal fasteners in a progressive die operation, and other industrial situations, important quality dimensions cannot b e measured on a continuous scale, and manufactured parts are classifie d into groups by using a step gauge. This paper proposes a version of exponentially weighted moving average (EWMA) control charts that are a pplicable to monitoring the grouped data for process shifts. The run l ength properties of this new grouped data EWMA chart are compared with similar results previously obtained for EWMA charts for variables dat a and with those for cumulative sum (CUSUM) schemes based on grouped d ata. Grouped data EWMA charts are shown to be nearly as efficient as v ariables-based EWMA charts and are thus an attractive alternative when the collection of variables data is not feasible. In addition, groupe d data EWMA charts are less affected by the discreteness that is inher ent in grouped data than are grouped data CUSUM charts. In the metal f asteners application, grouped data EWMA charts were simple to implemen t and allowed the rapid detection of undesirable process shifts.