Effectiveness of Exponential and Extended Weighting Control Charts in Identifying Process Mean Changes

This research aims to compare the efficiency in detecting changes of process mean between the Exponentially Weighted Moving Average (EWMA), Modified EWMA (MEWMA), Extended EWMA (EEWMA), and New EEWMA (NEEWMA) under observation values from processes with normal and Laplace distributions. The Monte Carlo simulation is used to carry on the numerical results by setting the in control average run length (ARL0) = 370, 500, and 1,000; the simulation is repeated 10,000 times, and sample size (n) = 10 by considering the minimum of out of control average run length (ARL1) and expected ARL (EARL) as the decision criterion. The numerical results found that the NEEWMA chart outperforms in detecting the minor to small changes. In addition, the EARL is an alternative effective to the ARL1 to benchmark the chart, and the NEEWMA chart is superior to other charts for all case studies. This study presents application to real data.