This research aimed to propose a newly-mixed control chart called the Exponentially Weighted Moving Average-Moving Average Chart (EWMA-MA) to detect the mean change in a process underlying symmetric and asymmetric distributions. The performance of the proposed control chart are compared with Shewhart, MA, EWMA, MA-EWMA and EWMA-MA control charts by using average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) as the criteria for measuring efficiency which evaluated by using Monte Carlo simulation (MC), Moreover, the proposed control chart will be applied to real data. The results of performance comparison showed that the presented control charts performed better detection than the Shewhart, MA, and EWMA charts. However, the results of detection tended to be slower than those for the MA-EWMA chart. The value of ARL 1 for the mixed control chart depends on the parameters of the statistics for such control chart. The EWMA-MA chart is a variable following λ and the MA-EWMA chart is varied according to w. From applying the proposed control chart to the data for flow in the Nile River and data of the real GDP growth (%) in the Lebanese economy, it was found to be in accordance with the research results.

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Control chart is a useful statistical tool for the production process control to maintain the product value at the standard. The objectives of this research were to propose the Tukey Moving Average -Double Exponentially Weighted Moving Average control chart (MMD-TCC chart) to detect the change of mean in the symmetric and non-symmetric distribution, and to compare the effectiveness of the change detection of MMD-TCC with that of MA, DEWMA, MMD, MDM, TCC, and MDM-TCC at the different parameter levels. ARL 1 and MRL criteria were used to measure the efficiency by applying Monte Carlo (MC) simulation. Research results indicated that the change of mean in process under the control where ARL 0 = 370 with MMD-TCC was more efficient to detect the change than other control charts unless it was the symmetric distribution with the change of parameter at ±1.50, ±2.00, ±3.00, ±4.00, and the non-symmetric distributions with the change of parameter at 3.00, 4.00 with MA control chart. Furthermore, by applying the proposed control chart to real data, it was found to be in accordance with the research results from simulation technique. INDEX TERMSMixed control chart, Monte Carlo simulation, Tukey moving average-double exponentially weighted moving average control chart, nonparametric control charts.

The purpose of this research is to enhance performance for detecting a change in process mean by combining modified exponentially weighted moving average and sign control charts. This is nonparametric control chart which effective alternatives to the parametric control chart so called MEWMA-Sign. The nonparametric control chart can serve when process observations is deviated from normal distribution assumption. Generally, the performance of control charts are widely measured by average run length (ARL) divided into two cases; in control ARL (ARL0) and out of control ARL (ARL1). In this paper, the performance comparison is investigated when processes are non-normal distributions. The performance of the MEWMA-Sign is compared EWMA-Sign control chart by considering from a minimum value of ARL1. The numerical results found that the MEWMASign performs better than EWMA-Sign in order to detect a very small shift of mean process. Additionally, the real application of the MEWMA-Sign and EWMA-Sign are presented.