A novel single composite Shewhart‐EWMA control chart for monitoring the process mean

Purpose The purpose of this paper is to develop an exponentially weighted moving average (EWMA) control chart based on the Wilcoxon rank-sum (WRS) statistic using repetitive sampling to improve the sensitivity of the EWMA control chart to process mean shifts regardless of the prior knowledge of the underlying process distribution. Design/methodology/approach The proposed chart is developed without any distributional assumption of the underlying quality process for monitoring the location parameter. The authors developed formulae as well as algorithms to facilitate the design and implementation of the proposed chart. The performance of the proposed chart is investigated in terms of the average run-length, standard deviation of the run-length (RL), average sample size and percentiles of the RL distribution. Numerical examples are given as illustration of the design and implementation of the proposed chart. Findings The proposed control chart presents very attractive RL properties and outperforms the existing nonparametric EWMA control chart based on the WRS in the detection of the mean process shifts in many situations. However, the performance of the proposed chart relatively deteriorates for small phase I sample sizes. Originality/value This study develops a new control chart for monitoring the process mean using a two-sample test regardless of the nature of the underlying process distribution. The proposed control chart does not require any assumption on the type (or nature) of the process distribution. It requires a small number of subgroups in order to reach stability in the phase II performance.

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A new adaptive EWMA control chart using auxiliary information for monitoring the process mean

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2018.1448417 ◽

2018 ◽

Vol 47 (19) ◽

pp. 4840-4858 ◽

Cited By ~ 9

Author(s):

Abdul Haq

Keyword(s):

Control Chart ◽

Auxiliary Information ◽

Process Mean ◽

Ewma Control Chart

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A New Maximum EWMA Control Chart for Simultaneously Monitoring Process Mean and Dispersion Using Auxiliary Information

Quality and Reliability Engineering International ◽

10.1002/qre.2126 ◽

2017 ◽

Vol 33 (7) ◽

pp. 1577-1587 ◽

Cited By ~ 18

Author(s):

Abdul Haq

Keyword(s):

Control Chart ◽

Auxiliary Information ◽

Process Mean ◽

Ewma Control Chart ◽

Monitoring Process

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A new nonparametric synthetic EWMA control chart for monitoring process mean

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2017.1422750 ◽

2018 ◽

Vol 48 (6) ◽

pp. 1665-1676 ◽

Cited By ~ 13

Author(s):

Abdul Haq

Keyword(s):

Control Chart ◽

Process Mean ◽

Ewma Control Chart ◽

Monitoring Process

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Demerit-GWMA Control Chart for Demerit Statistics

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.156-157.413 ◽

2010 ◽

Vol 156-157 ◽

pp. 413-421

Author(s):

Hae Woon Kang ◽

Chang Wook Kang ◽

Jae Won Baik ◽

Sung Ho Nam

Keyword(s):

Control Chart ◽

Mobile Phones ◽

Moving Average ◽

Exponentially Weighted Moving Average ◽

Process Mean ◽

Average Technique ◽

Complex Products ◽

Ewma Control Chart ◽

Weighted Moving Average ◽

Exponentially Weighted

A classical Demerit control chart is used to monitor the process through which various types of defects in complex products, such as automobiles, computers, mobile phones, etc. are found in general. As a technique for rapidly detecting small shifts of the process mean in the control chart, the EWMA(exponentially weighted moving average) technique is very effective. This study suggested the Demerit-GWMA control chart, combining the GWMA(generally weighted moving average) technique, which shows better performance than EWMA technique in detecting small shifts of process mean, into the classical Demerit control chart, and evaluated its performance. Through the evaluation of its performance, it was found that the Demerit-GWMA control chart is more sensitive than both the classical Demerit control chart and the Demerit-EWMA control chart in detecting small shifts of process mean.

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MONITORING AUTOCORRELATED PROCESS MEAN AND VARIANCE USING A GWMA CHART BASED ON RESIDUALS

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595908002012 ◽

2008 ◽

Vol 25 (06) ◽

pp. 781-792 ◽

Cited By ~ 4

Author(s):

SHEY-HUEI SHEU ◽

SHIN-LI LU

Keyword(s):

Control Chart ◽

Control Charts ◽

Moving Average ◽

Autoregressive Process ◽

Process Mean ◽

Process Variability ◽

Autocorrelated Process ◽

Ewma Control Chart ◽

Mean And Variance ◽

Run Lengths

This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control charts of residuals used to monitor process variability and to monitor simultaneously the process mean and variance are considered to evaluate how average run lengths (ARLs) differ in each case.

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