A hybrid exponentially weighted moving average chart for COM-Poisson distribution

2016 ◽  
Vol 40 (2) ◽  
pp. 456-461 ◽  
Author(s):  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Chi-Hyuck Jun
Keyword(s):  
New York ◽  
Poisson Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Statistical Quality Control ◽  
Exponentially Weighted Moving Average ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

We provide the complete design of a hybrid exponentially weighted moving average (HEWMA) control chart for COM-Poisson distribution. The necessary measures of the proposed control chart are given in this manuscript, and the average run lengths (ARLs) are determined through Monte Carlo simulation for various values of specified parameters. The performance of the proposed chart is compared with two existing control charts. The proposed chart is more efficient than these two existing charts in terms of ARLs; application of the proposed chart is described with the help of Montgomery’s data ( Introduction to Statistical Quality Control, John Wiley & Sons, New York, 2007).

On control chart for monitoring exponentially distributed quality characteristic

2019 ◽  
Vol 42 (2) ◽  
pp. 295-305 ◽  
Author(s):  
Olatunde A Adeoti
Keyword(s):  
Exponential Distribution ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Quality Characteristic ◽  
Exponentially Weighted Moving Average ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

The double exponentially weighted moving average (DEWMA) control chart has been observed to be more sensitive than the exponentially weighted moving average (EWMA) control chart for process monitoring assuming that the quality characteristic follows the normal distribution. In this paper, the DEWMA control chart is proposed for monitoring quality characteristics that follow the exponential distribution using variable transformation technique. The in-control and out-of-control average run lengths (ARLs) of the proposed control chart is obtained for equal and unequal smoothing constants. The performance of the proposed control chart with equal and unequal smoothing constants was investigated and compared with recent existing control charts in terms of the out-of-control average run lengths. Real life example is given to demonstrate the application of the proposed chart. The findings show that the performance of the proposed control chart outweighs existing control charts in the monitoring of process parameter when the quality variable follows exponential distribution for all shift sizes.

scholarly journals A Double Generally Weighted Moving Average Chart for Monitoring the COM-Poisson Processes

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1014
Author(s):  
Jen-Hsiang Chen
Keyword(s):  
Numerical Simulations ◽  
Poisson Distribution ◽  
Moving Average ◽  
Poisson Processes ◽  
Exponentially Weighted Moving Average ◽  
Run Lengths ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Generalized exponentially weighted moving average (EWMA) and double EWMA (DEWMA) charts based on the Conway–Maxwell–Poisson (CMP or COM-Poisson) distribution, also known as the GEWMA and CMP-DEWMA charts, are effectively used for monitoring the counts of non-conformities in a process. To further enhance their performance, this study utilizes design and adjustment parameters to develop generally weighted moving average (GWMA) and double GWMA charts, also known as the CMP-GWMA and CMP-DGWMA charts, to monitor COM-Poisson attributes. Numerical simulations indicate that the CMP-DGWMA chart outperforms its prototype CMP-DEWMA and CMP-GWMA charts in detecting small location and dispersion shifts, as well as both shifts together, in terms of average run lengths. Finally, an example is provided to demonstrate the efficiency of the proposed CMP-DGWMA chart and its counterparts.

DEWMA Control Chart for the Coefficient of Variation

2011 ◽  
Vol 201-203 ◽  
pp. 1682-1688 ◽  
Author(s):  
Eui Pyo Hong ◽  
Hae Woon Kang ◽  
Chang Wook Kang
Keyword(s):  
Control Chart ◽  
Coefficient Of Variation ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Average Technique ◽  
Small Shift ◽  
Production Run ◽  
Weighted Moving Average ◽  
Exponentially Weighted

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. The CV-EWMA (exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. In this paper, we propose the CV-DEWMA control chart, combining the DEWMA (double exponentially weighted moving average) technique. We show that CV-DEWMA control chart perform better than CV-EWMA control chart in detecting small shifts when sample size n is larger than 5.

CV Control Chart Using GWMA Technique

2011 ◽  
Vol 337 ◽  
pp. 247-254 ◽  
Author(s):  
Eui Pyo Hong ◽  
Hae Woon Kang ◽  
Chang Wook Kang ◽  
Jae Won Baik
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Average Technique ◽  
Ewma Control Chart ◽  
Production Run ◽  
Average Control ◽  
Weighted Moving Average ◽  
Exponentially Weighted

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shifts in the magnitude of CV. This study suggest the CV-GWMA(generally weighted moving average) control chart, combining the GWMA technique, which shows better performance than the EWMA(exponentially weighted moving average) or DEWMA(double exponentially weighted moving average) technique in detecting small shifts of the process. Through a performance evaluation, the proposed control chart showed more excellent performance than the existing CV-EWMA control chart or the CV-DEWMA control chart in detecting small shifts in CV.

scholarly journals Improved Hybrid Exponentially Weighted Moving Average Control Chart using Auxiliary Information

2020 ◽  
Author(s):  
Sadia Tariq ◽  
Muhammad Noor-ul-Amin ◽  
Muhammad Hanif ◽  
Chi-Hyuck Jun 
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Exponentially Weighted Moving Average ◽  
Statistical Process ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Statistical process control is an important tool for maintaining the quality of a production process. Several control charts are available to monitor changes in process parameters. In this study, a control chart for the process mean is proposed. For this purpose, an auxiliary variable is used in the form of a regression estimator under the configuration of the hybrid exponentially weighted moving average (HEWMA) control chart. The proposed chart is evaluated by conducting a simulation study. The results showed that the proposed chart is sensitive with respect to the HEWMA chart. A real-life application is also presented to demonstrate the performance of the proposed control chart.

scholarly journals HYBRID EXPONENTIALLY WEIGHTED MOVING AVERAGE (HEWMA) CONTROL CHART BASED ON EXPONENTIAL TYPE ESTIMATOR OF MEAN

2019 ◽  
pp. 187-198 ◽  
Author(s):  
Syed Muhammad Muslim Raza ◽  
Maqbool Hussain Sial ◽  
Muhammad Haider ◽  
Muhammad Moeen Butt
Keyword(s):  
Control Chart ◽  
Exponential Type ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Ewma Chart ◽  
Weighted Moving Average ◽  
Better Than ◽  
Exponentially Weighted

In this paper, we have proposed a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart. The proposed control chart is based on the exponential type estimator for mean using two auxiliary variables (cf. Noor-ul-Amin and Hanif, 2012). We call it an EHEWMA control chart because it is based on the exponential estimator of the mean. From this study, the fact is revealed that E-HEWMA control chart shows more efficient results as compared to traditional/simple EWMA chart and DS.EWMA control chart (cf. Raza and Butt, 2018). The comparison of the E-HEWMA control chart is also performed with the DS-EWMA chart. The proposed chart also outperforms the other control chartsin comparison. The E-HEWMA chart can be used for efficient monitoring of the production process in manufacturing industries.A simulated example has been used to compare the proposed and traditional/simple EWMA charts and DS.EWMA control chart. The control charts' performance is measured using the average run length-out of control (ARL1). It is observed that the proposed chart performs better than existing EWMA control charts.  

scholarly journals GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)

2021 ◽  
Vol 10 (1) ◽  
pp. 114-124
Author(s):  
Aulia Resti ◽  
Tatik Widiharih ◽  
Rukun Santoso
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Cumulative Sum ◽  
Statistical Process ◽  
Cusum Charts ◽  
Weighted Moving Average ◽  
Exponentially Weighted

Quality control is an important role in industry for maintain quality stability.  Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

scholarly journals A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 957 ◽  
Author(s):  
Muhammad Aslam ◽  
Ali Hussein AL-Marshadi ◽  
Nasrullah Khan
Keyword(s):  
Monte Carlo Simulation ◽  
Control Chart ◽  
Control Charts ◽  
Symmetry Property ◽  
Average Run Length ◽  
Moving Average ◽  
Exponentially Weighted Moving Average ◽  
Uncertainty Environment ◽  
Weighted Moving Average ◽  
Exponentially Weighted

The existing Shewhart X-bar control charts using the exponentially weighted moving average statistic are designed under the assumption that all observations are precise, determined, and known. In practice, it may be possible that the sample or the population observations are imprecise or fuzzy. In this paper, we present the designing of the X-bar control chart under the symmetry property of normal distribution using the neutrosophic exponentially weighted moving average statistics. We will first introduce the neutrosophic exponentially weighted moving average statistic, and then use it to design the X-bar control chart for monitoring the data under an uncertainty environment. We will determine the neutrosophic average run length using the neutrosophic Monte Carlo simulation. The efficiency of the proposed plan will be compared with existing control charts.

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