scholarly journals NEW APPROACH FOR DETECTING SLIGHT SHIFT ON PROCESS MEAN IN CHIP RESISTOR PRODUCTION

2021 ◽  
Vol 56 (5) ◽  
pp. 59-66
Author(s):  
Budi Susetyo ◽  
Anwar Fitrianto ◽  
Lai Ming Choon
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Normal Population ◽  
Moving Average ◽  
Distributed Data ◽  
Process Mean ◽  
New Approach ◽  
Parametric Control ◽  
Ewma Control Chart ◽  
Alternative Approach

This article highlights an alternative approach to identify a slight shift of the process mean for resistor production. Commonly, the industries use exponentially weighted moving average (EWMA) or classic I-MR charts for this kind of product. The parametric control chart consists of few underlying assumptions, especially observations that come from a normal distribution. A misleading conclusion was mainly made when non-normal distributed data were analyzed using a parametric control chart. A chip resistor manufacturing company provided the data for the study for future quality monitoring purposes. This study aims to determine a more appropriate analysis method according to the characteristics of the chip resistor data distribution. This article discusses the results of implementing one of the nonparametric methods that are still rarely known. The company’s current I-MR, corrective I-MR, parametric EWMA, and NPEWMA-SR control charts are used and compared in the analysis part. In the comparison, the I-MR control chart cannot detect a slight shift in the process mean. In contrast, the parametric EWMA control chart is not robust for data from a non-normal population. Since the data was not naturally from a normally distributed population, the nonparametric control chart is more appropriate, and the NPEWMA-SR control chart is suggested.

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

2008 ◽  
Vol 25 (06) ◽  
pp. 781-792 ◽  
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.

Design and Application of a Modified EWMA Control Chart for Monitoring Process Mean

2021 ◽  
Author(s):  
Yadpirun Supharakonsakun ◽  
Yupaporn Areepong
Keyword(s):  
White Noise ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Autoregressive Process ◽  
Process Mean ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Abrupt Shifts ◽  
Run Lengths

The modified exponentially weighted moving average (modified EWMA) control chart is an improvement on the performance of the standard EWMA control chart for detecting small and abrupt shifts in the process mean. In this study, the effect of varying the constant and exponential smoothing parameters for detecting shifts in the mean of an autoregressive process with exogenous variables (ARX(p,r)) with a trend and exponentially distributed white noise on the standard and modified EWMA control chart was investigated. The performances of the two control charts were compared via their average run lengths (ARLs) computed by using explicit formulas and the numerical integrated equation (NIE) technique. A comparative study of the two ARL methods on the modified and traditional EWMA control charts shows that the modified schemes had better detection ability at all levels of shift size. Finally, two examples using real datasets on gold and silver prices are given to illustrate the applicability of the proposed procedure. Our findings advocate that the modified EWMA chart is excellent for monitoring ARX(p,r) processes with exponentially distributed white noise

An EWMA control chart based on the Wilcoxon rank-sum statistic using repetitive sampling

2018 ◽  
Vol 35 (3) ◽  
pp. 711-728 ◽  
Author(s):  
Jean-Claude Malela-Majika ◽  
Olatunde Adebayo Adeoti ◽  
Eeva Rapoo
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Location Parameter ◽  
Process Mean ◽  
Content Type ◽  
Underlying Process ◽  
Run Length ◽  
Design And Implementation ◽  
Ewma Control Chart

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.

scholarly journals A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 312 ◽  
Author(s):  
Muhammad Zahir Khan ◽  
Muhammad Farid Khan ◽  
Muhammad Aslam ◽  
Seyed Taghi Akhavan Niaki ◽  
Abdur Razzaque Mughal
Keyword(s):  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Real Life ◽  
Fuzzy Data ◽  
Process Mean ◽  
Statistical Process ◽  
Control Charting ◽  
Real Life Data

Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.

Demerit-GWMA Control Chart for Demerit Statistics

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.

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.

ZIB-EWMA CONTROL CHART FOR MONITORING RARE HEALTH EVENTS

2011 ◽  
Vol 11 (04) ◽  
pp. 881-895 ◽  
Author(s):  
RASSOUL NOOROSSANA ◽  
AMIR AFSHIN FATAHI ◽  
PERSHANG DOKOUHAKI ◽  
MASSOUD BABAKHANI
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Cusum Chart ◽  
Health Events ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Significant Public Health ◽  
Health Related

Monitoring rare health events, as a significant public health subject, has been considered recently by different authors. In this regard, different statistical methods such as g-type control chart, Poisson CUSUM control chart, sets-based methods, and Bernoulli CUSUM chart have been developed. Zero-inflated binomial (ZIB) distribution, due to its structure, can also be considered to develop methods for monitoring rare health-related events. If zero inflation is considered in the sampling data, and the sampling subgroup size is mandatory greater than 1, then the data best fits the ZIB distribution and the aforementioned control charts cannot be applied. ZIB distribution assumes that random shocks, corresponding to rare health events, occur and then number of failures in each subgroup fits a binomial distribution. In this paper, an exponentially weighted moving average (EWMA) control chart is applied for ZIB data to develop a ZIB-EWMA chart. Since ZIB-EWMA statistic values are not independent, Markov chain approach is considered to evaluate the performance of the proposed control chart in terms of average run length (ARL). According to the ARL measure, this ZIB-EWMA chart has a better performance in comparison with the methods available in the literature. In addition, a real case study related to rare infections in a hospital is investigated to show the applicability of the proposed control chart.

Demerit-GWMA Control Chart with Fast Initial Response

2012 ◽  
Vol 157-158 ◽  
pp. 1655-1660
Author(s):  
Hae Woon Kang ◽  
Jae Won Baik ◽  
Young Jae Choi ◽  
Sung Ho Nam
Keyword(s):  
Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Initial Response ◽  
Process Mean ◽  
Complex Products ◽  
Different Types ◽  
And Performance ◽  
Weighted Moving Average

Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective systems to monitoring the different types of defects. So, classical demerit control chart used to monitor counts of several different types of defects simultaneously in complex products. Recently, H.W. Kang et al.[7] introduced Demerit-GWMA(generally weighted moving average) and Demerit-EWMA control charts that can detect small shifts of the process mean more sensitively than the classical demerit control charts. In this paper, we present an effective method for process control using the Demerit-GWMA statistics with fast initial response. Moreover, we evaluate exact performance of the Demerit-GWMA control chart with fast initial response(FIR), Demerit-GWMA and Demerit-EWMA according to changing sample size or parameters.

An Enhanced Performance to Monitor Process Mean with Modified Exponentially Weighted Moving Average – Sign Control Chart

2021 ◽  
Author(s):  
Rattikarn Taboran ◽  
Saowanit Sukparungsee
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Performance Comparison ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Small Shift ◽  
Weighted Moving Average ◽  
Exponentially Weighted

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.

scholarly journals Statistical Design for Monitoring Process Mean of a Modified EWMA Control Chart based on Autocorrelated Data

2021 ◽  
Vol 18 (12) ◽  
Author(s):  
Yadpirun SUPHARAKONSAKUN
Keyword(s):  
Explicit Formula ◽  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Series Data ◽  
Run Length ◽  
Ewma Control Chart ◽  
Autocorrelated Observations ◽  
Normally Distributed

From the principles of statistical process control, the observations are assumed to be identically and independently normally distributed, although this assumption is frequently untrue in practice. Therefore, control charts have been developed for monitoring and detecting data which are autocorrelated. Recently, a modified exponentially weighted moving average (EWMA) control chart has been introduced that is a correction of the EWMA statistic and is very effective for detecting small and abrupt changes in independent normally distributed or autocorrelated observations. In this study, the performance of a modified EWMA chart is investigated by examining the 2 sides of the exact average run length based on an explicit formula when the observations are from a general-order moving average process with exponential white noise. A performance comparison of the EWMA and the modified EWMA control charts is also presented. In addition, the performance of the modified and EWMA control charts is contrasted using Dow Jones composite average from a real-life dataset. The findings suggest that the modified EWMA control chart is more sensitive than the EWMA control chart for almost every case of the studied smoothing parameter and constant values of the control chart. HIGHLIGHTS Autocorrelation data is frequency untrue of assumption practice in time series data Modified EWMA is a new control chart that is effective for detecting change in independent normal distribution and autocorrelated observations The efficiency of the control chart is measured by average run length Explicit formula is easy to derive and provides the exact value of the average run length

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