• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.211-216
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• 2006

Monitoring variability is a vital part of modem statistical process control. The conventional Shewhart Rand S charts address the setting where the in-control process readings have a constant variance. In some settings, however, it is the coefficient of variation, rather than the variance, that should be constant. This paper develops a chart, equivalent to the S chart, for monitoring the coefficient of variation using rational groups of observations.

• IE interfaces
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• v.6 no.1
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• pp.19-30
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• 1993

Various types of control charts are available, but their full advantages are not being utilized due to the lack of experts who can select, design, implement, and interpret. An expert system is developed which would perform these functions as if each process had a resident expert.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.156-168
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• 2004

This research investigates the statistical efficiency of variable sampling size & sampling interval(VSSI) $\bar{X}$ charts under two assignable causes. Algorithms for calculating the average run length(ARL) and average time to signal(ATS) of the VSSI $\bar{X}$ chart are proposed by employing Markov chain method. States of the process are defined according to the process characteristics after the occurrence of an assignable cause. Transition probabilities are carefully derived from the state definition. Statistical properties of the proposed chart are also investigated. A simple procedure for designing the proposed chart is presented based on the properties. Extensive sensitivity analyses show that the VSSI $\bar{X}$ chart is superior to the VSS or VSI $\bar{X}$ chart as well as to the Shewhart $\bar{X}$ chart in statistical sense, even tinder two assignable causes.

• Journal of Korean Society for Quality Management
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• v.22 no.1
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• pp.82-95
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• 1994

When independent individual measurements are taken both $S/c_4$ and $\bar{R}/d_2$ are unbiased estimators of the process standard deviation. However, with dependent data $\bar{R}/d_2$ is not an unbiased estimator of the process standard deviation. On the other hand $S/c_4$ is an asymptotic unbiased estimator. If there exists correlation in the data, positive(negative) correlation tends to increase(decrease) the ARL. The effect of using $\bar{R}/d_2$ is greater than $S/c_4$ if the assumption of independence is invalid. Supplementary runs rule shortens the ARL of X control charts dramatically in the presence of correlation in the data.

• Journal of Korean Society for Quality Management
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• v.37 no.1
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• pp.29-39
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• 2009

This article investigates economic-statistical design of VSSI(variable sampling size and interval) cause-selecting charts considering two assignable causes. We consider a process which is composed of two dependent sub-processes. In each sub-process, two kinds of assignable cause may exist. We propose a procedure for designing VSSI cause-selecting charts, based on Lorenzen and Vance model. Computational experiments show that the VSSI cause-selecting chart is superior to the FSSI cause-selecting chart in the economic-statistical characteristics, even under two assignable causes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.1-10
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• 1999

In recent industry society, it is revealed that, as an increase in the use of automated manufacturing and process inspection technology, the data from mass production system exhibits some degrees of autocorrelation. The operation characteristics of traditional control charts developed under the independence assumption are adversely affected by the presence of serial correlation. Therefore, when autocorrelated construction contacted with time-series models explain, the time-series models are the Box-Jenkins forecast models which have been proposed as the best forecasting tool which allows for partitioning of variation into result from the autocorrelation structure and variation due to unusual but assignable causes. In this paper, for the AR(1) process of Box-Jenkins forecast models, when the constant term ξ are zero and different from zero, I want to analyze the sensitivity of (equation omitted), CUSUM and EWMA control chart for forecast residuals.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.131-136
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• 2009

The research develops the calculation steps of Z sigma level, process capability index(PCI) and process performance index(PPI) applicable to the discrete process in the service industry. The paper presents three following topics used related process indexes according to the precision and accuray in the continuous process. The contents include diverse process indexs for nonconformities by SPC attribute control charts and bias coefficient. The same technique of the nonconformites is also used in the nonconforming units. The practical examples are provided to help easier understanding for users.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.939-950
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• 2003

Control charts are very useful tool for monitoring of process characteristics. This paper discusses the problem of design of control limits when the subgroups are composed by cluster sampling type. As an alternative method of design of control limits XbBar chart is proposed, which uses the control limits based on the variation between subgroups instead of using classical variation within subgroups. Two examples are presented for reasonable design of control limits and conditions of subgroups based on the cluster sampling. Through examples the guidelines for making proper control limits are proposed.

• Journal of Korean Society for Quality Management
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• v.27 no.4
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• pp.256-265
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• 1999

In this paper, we Present the implementation of statistical process control software by using XLISP-STAT which is a kind of object oriented language under Windows environment. This software can be used to generate the graphic objects for various control charts, histogram and plots using the full-down menu system. This software can also be used to calculate control limits, process capability indices and test procedures for normality.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.137-150
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• 1992

As a part of quality control activities for developing competitive products, the significant method of process quality assurance for solving initial production quality problems is just quality control process chart(QC process chart). However, the QC process chart which is used for domestic enterprises at present had obscured in basement and not itemized by industry and formally used. So. in this study, the improved QC process charts which classified by industry we suggested so that each enterprise may utilize them according to the each enterprise characteristics.