• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.431-439
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• 2004

In this paper, we present an effective method for process control using the Demerit-EWMA control chart in the process where nonconforming units or nonconformities are occurred by various types. We compare performance of Demerit control chart, Demerit-CUSUM control chart and Demerit-EWMA control chart based on the average run length(ARL).

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.237-246
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• 1998

In most statistical process control(SPC), control charts are used in which samples are taken and a suitable statistic is determined and plotted. In these control charts, control limits, ${\mu}{\pm}textsc{k{\sigma}}$, from which a decision is made are mostly ${\mu}{\pm}3{\sigma}$ and current literature in control charts are mainly concerned with detecting a shift in the mean. Therefore, when $\sigma$ is increased considerably after a long time, using control limits set at the first time causes a great deal of economic loss. In this paper the solutions to determine new control limits which maximizes the profit per unit produced and reduce $\sigma$ to economically optimal level for a certain cost when $\sigma$ is increased due to process deterioration are proposed. By applying new control limits, $\alpha$ error decreases considerably compared to apply initial control limits when $\sigma$ is increased due to process deterioration. Therefore, false alarm investigation cost drops down to the level of initial a error. And also this solution provides useful information regarding replacement of a process when the process is reviewed regularly.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.465-470
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• 2012

EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.87-92
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• 2004

This paper presents several control charts for safety and environmental performance monitoring. We also propose guidelines that control charts for statistical process control can be used for safety and environmental performance evaluation.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.102-109
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• 2006

This paper investigates the effects of non-normality on the performance of univariate and multivariate cumulative sum(CUSUM) control charts for monitoring the process mean. In-control and out-of-control average run lengths of the charts are examined for the univariate/multivariate lognormal and t distributions. The effects of the reference value and the correlation coefficient under the non-normal distributions are also studied. Simulation results show that the CUSUM charts with small reference values are robust to non-normality but those with moderate or large reference values are sensitive to non-normal data especially to process data from skewed distributions. The performance of the chart to detect mean shift of a process is not invariant to the direction of the shift for skewed distributions.

• Journal of Korean Society for Quality Management
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• v.30 no.4
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• pp.1-14
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• 2002

Traditional SPC techniques are looking out variation of process by fixed sampling interval and fixed sample size about every hour, the process of in-control or out-of-control couldn't be detected actually when the sample points are plotted near control limits, and it takes no notice of expense concerned with such sample points. In this paper, to overcome that, consider VSI(variable sampling interval) EWMA control charts which VSI method is applied. The VSI control charts use a short sampling internal if previous sample points are plotted near control limits, then the process has high probability of out-of-control. But it uses a long sampling interval if they are plotted near centerline of the control chart, since process has high possibility of in-control. And then a comparison and analysis between FSI(fixed sampling interval) and VSI EWMA in the statistical aspect and economic aspect is studied. Finally, we show that VSI EWMA control chart is more efficient than FSI EWMA control chart in the both aspects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.134-144
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• 2014

With the advent of lean-six sigma era, an extensive use of analytic tools such as control charts is required in the field of manufacturing. In relation to statistical quality control (SQC) or process control (SPC), the Korean standards have undergone a meaningful change. In this study, the theoretic backgrounds for evaluating the control limits in connection with the variable control charts are examined in view of better understanding the related constants and coefficients. This paper is intended to help the quality control practitioners understand the mathematical backgrounds by comparing related quality control constants and also to encourage them to make use of and to take the advantage of the variable control charts which are very useful for implementing the concept of lean-six sigma in many industrial sites.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.101-106
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• 2014

New control charts under repetitive sampling are proposed, which can be used for variables and attributes quality characteristics. The proposed control charts have inner and outer control limits so that repetitive sampling may be needed if the plotted statistic falls between the two limits. Particularly, the new np and variable X-bar control charts under repetitive sampling are considered in detail. The in-control and out-of-control average run lengths are analyzed according to various process shifts. The performance of the proposed control charts is compared with the existing np and the X-bar control charts in terms of the average run lengths.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.69-80
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• 2014

This paper considers the problem of monitoring the mean of a normally distributed process variable when the objective is to effectively detect both a sustained shift and a linear drift. The design and application of a generalized likelihood ratio (GLR) chart for simultaneously monitoring a sustained shift and a linear drift are evaluated. The GLR chart has the advantage that when we design this chart, we do not need to specify the size of the parameter change. The performance of the GLR chart is compared with that of other control charts, such as the standard cumulative sum (CUSUM) charts and the cumulative score (CUSCORE) charts. And we compare the proposed GLR chart with the GLR charts designed for monitoring only a sustained shift and for monitoring only a linear drift. Finally, we also compare the proposed GLR chart with the chart combinations. We show that the proposed GLR chart has better overall performance for a wide range of shift sizes and drift rates relative to other control charts, when a special cause produces a sustained shift and/or a linear drift in the process mean.