• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.132-153
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• 2004

CUSUM control charts are widely used to monitor processes with small shifts. CUSUM control charts are, however, less effective in detecting for recurring cycles or frequent small shifts in the processes. With Shewhart control charts, we have applied the variety of run rules to check the stability of process in addition to the situations that some points fall outside the control limits. In this paper, we propose the Z -CUSUM control chart for monitoring the process with recurring cycles or frequent small shifts by use of the zone concept as like the Shewhart control charts.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.57-63
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• 2004

CUSUM control charts are widely used to monitor processes with small shifts. CUSUM control charts, however, are less effective in detecting for recurring cycles or frequent small shifts in the process. With Shewhart control charts, we have applied the variety of run rules to check the stability of process in addition to the situations that some points fall outside the control limits. In this paper, we propose the Z-CUSUM control chart for monitoring the process with recurring cycles or frequent small shifts by use of the zone concept as like the Shewhart control charts.

• Journal of Korean Society for Quality Management
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• v.15 no.2
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• pp.10-19
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• 1987

Statistical control charts are useful tools to monitor and control the manufacturing processes and are widely used in most Korean industries. Many Korean companies, however, do not always obtain desired results from the traditional control charts by Shewhart such as the $\overline{X}$-chart, X-chart, $\widetilde{X}$-chart, etc. This is partly because the quality charterstics of the process are not distributed normally but are skewed due to the intermittent production, small lot size, etc. In the Shewhart $\overline{X}$-chart, which is the most widely used one in Korea, such skewed distributions make the plots to be inclined below or above the central line or outside the control limits although no assignable causes can be found. To overcome such shortcomings in nonnormally distributed processes, a distribution-free type of confidence interval can be used, which should be based on order statistics. This thesis is concerned with the design of control chart based on a sample median which is easy to use in practical situation and therefore properties for nonnormal distributions may be easily analyzed. Control limits and central lines are given for the more famous nonnormal distributions, such as Gamma, Beta, Lognormal, Weibull, Pareto, and Truncated-normal distributions.

• Proceedings of the Safety Management and Science Conference
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• 2013.04a
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• pp.581-591
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• 2013

제조공정에서 사용되어 지는 SPC(Statistical Process Control)관리 기법은 가피원인을 탐지하여 변동을 감소시키는 통계적 공정관리 시스템이다. SPC의 대표적인 관리 기법으로는 Shewhart관리도, Cusum관리도, EWMA관리도가 있으며 이러한 관리 기법들은 공정을 보다 안정적으로 관리 할 수 있도록 유지 및 예측하는데 사용 되어 진다. 하지만 제조 공정의 유형에 따라 샘플링 방법, 관리한계선 등을 다양하게 설정하여 보다 효율적인 관리를 모색하고 있다. 공정 형태에 따라 다양한 관리 방법과 분석 결과가 나타난다. 일반적으로 Xbar-R 관리도와 같은 Shewhart 관리도를 사용하지만 Batch 단위의 공정, 연속 공정의 라인에서 사용되기에는 부분적인 한계를 보이고 있다. 본 논문에서는 일반적인 관리도와 공정 변화에 민감하게 반응 할 수 있는 누적합 관리도와 지수가중치이동평균 관리도를 비교해 보고 작은 변동에 대한 탐지 능력이 우수한 지수가중치이동평균 관리도에 대한 연구동향과 사례를 분석하여 제조 공정에 적합한 관리 방법을 모색하고자 한다.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.219-231
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• 2004

Process adjustment is a complimentary tool to process monitoring in process control. Although original intention of process adjustment is not identifying a special cause, detection and elimination of special causes may lead to significant process improvement. In this paper, we examine the impact of special causes on process adjustment. The bias in the adjusted output process is derived for each type of special causes, and average run length (ARL) of the Shewhart chart applied to the adjusted output is computed for each special cause types. Numerical results are illustrated for the ARL of the Shewhart chart, thereupon seriousness of special causes on process adjustment is evaluated for each type of special causes.

• Proceedings of the Safety Management and Science Conference
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• 2012.11a
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• pp.445-451
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• 2012

제조공정에서 사용되어지는 SPC(Statistical Process Control)관리 기법은 가피원인을 탐지하여 변동을 감소시키는 통계적 공정관리 시스템이다. SPC의 대표적인 관리기법으로는 Shewhart관리도, Cusum관리도, EWMA관리도가 있으며 이러한 관리 기법들은 공정을 보다 안정적으로 관리 할 수 있도록 유지 및 예측하는데 사용 되어 진다. 본 논문에서는 일반적으로 사용되어 지는 Shewhart관리도와 공정 예측에 유리한 EWMA 관리도에 대해 연구해보고 공정변화에 민감하게 반응하는 EWMA 관리도의 적용 사례를 제시하고자 한다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.151-154
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• 2006

The high degree of viscosity and the non-Newtonian fluid dynamics characterizes the process inside a glass furnace. Because the temperature is fluctuating in very short time-intervals, it is hard to determine that the status of its fluctuation is stable or unstable. Usually Shewhart-chart is used to determine the control status. However because of the characteristics of the temperature fluctuations in the glass furnace it does not directly serve the purpose here. Therefore we suggest using ARIMA to diagnose control status and confirm that the method using ARIMA can be a better tool than Shewhart-chart.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.617-626
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• 2012

Multivariate Shewhart control charts are considered for the simultaneous monitoring the variance-covariance matrix when the joint distribution of process variables is multivariate normal. The performances of the multivariate Shewhart control charts based on control statistic proposed by Hotelling (1947) are evaluated in term of average run length (ARL) for 2 or 4 correlated variables, 2 or 4 samples at each sampling point. The performance is investigated in three cases, that is, the variances, covariances, and variances and covariances are changed respectively.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1025-1034
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• 2015

Traditional Shewhart control charts assume that the observations are independent over time. Current progress in measurement and data collection technology lead to the presence of autocorrelated process data that may affect poor performance in statistical process control. One of the most popular charts for autocorrelated data is to model a correlative structure with an appropriate time series model and apply control chart to the sequence of residuals. Model parameters are estimated by an in-control Phase I reference sample since they are usually unknown in practice. This paper deals with the effects of parameter estimation on Phase II control limits to monitor autocorrelated data.

• 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.