Detecting selected non-random patterns with individuals control charts

Western Electric (1956) and Nelson (1984) proposed rule sets to detect visually obvious process upsets on a control chart. Performance evaluation of these rules require simulating situations that correspond to their intended detection purposes such as linear drift, cycling, seesaw and sustained process mean shifts. These non-random process patterns become visually obvious in the presence of reduced variation. Contrary to previous assessments where variance is assumed constant, Western Electric and Nelson rule sets are shown to be preferred over Shewhart <i>X</i> and CUSUM charts for detecting non-random patterns of process mean in the presence of variance reduction over wide ranges of slopes, cycle period/amplitude combinations, alternating shift and sustained shift sizes. One real-data example from Deming's book <i>Out of the Crisis</i> is provided that affirms implications of extensive simulation analyses.