• average run length;
  • best linear unbiased estimator;
  • control chart;
  • exponentially weighted moving average;
  • Monte Carlo simulation;
  • ordered ranked set sampling;
  • standard deviation of run length;
  • statistical process control

Maximum exponentially weighted moving average (MaxEWMA) control charts have gained considerable attention for detecting changes in both process mean and process variability. In this paper, we propose an improved MaxEWMA control charts based on ordered ranked set sampling (ORSS) and ordered imperfect ranked set sampling (OIRSS) schemes for simultaneous detection of both increases and decreases in the process mean and/or variability, named MaxEWMA-ORSS and MaxEWMA-OIRSS control charts. These MaxEWMA control charts are based on the best linear unbiased estimators of location and scale parameters obtained under ORSS and OIRSS methods. Extensive Monte Carlo simulations have been used to estimate the average run length and standard deviation of run length of the proposed MaxEWMA control charts. These control charts are compared with their counterparts based on simple random sampling (SRS), that is, MaxEWMA-SRS and MaxGWMA-SRS control charts. The proposed MaxEWMA-ORSS and MaxEWMA-OIRSS control charts are able to perform better than the MaxEWMA-SRS and MaxGWMA-SRS control charts for detecting shifts in the process mean and dispersion. An application to real data is provided to illustrate the implementation of the proposed MaxEWMA control charts. Copyright © 2013 John Wiley & Sons, Ltd.