EWMA control charts for monitoring high-yield processes based on non-transformed observations

Abstract

We propose and study exponentially weighted moving average (EWMA) control charts for monitoring high-yield processes. The EWMA control charts are developed based on non-transformed geometric, binomial and Bernoulli counts. The proposed charts are evaluated based on the average number of items sampled before the first out-of-control signal is detected. By selecting small smoothing constants, the proposed EWMA control charts outperform in numerous cases the recently developed CUSUM control charts [Chang, T.C. and Gan, F.F., Cumulative sum charts for high yield processes. Statist. Sin., 2001, 11, 791–805], which are considered the most efficient control charting mechanisms in the existing literature for monitoring fraction non-conforming as small as 0.0001. Numerous simulations are included for performance comparisons. An example is also given to demonstrate the applicability of the proposed EWMA control charts.

Keywords:

• Average number of items sampled
• Bernoulli counts
• Binomial counts
• Geometric counts

Acknowledgement

We thank the two referees for their numerous suggestions which improved the presentation of the paper.