• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.467-476
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• 2005

Multivariate control charts for monitoring mean vector of several related quality variables with combine-accumulate approach and accumulate-combine apprach were investigated. Shewhart chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and multivariate charts based on accumulate- combine approach is more efficient than corresponding multivariate charts based on combine-accumulate approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.123-146
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• 1995

Several quality control schemes have been extensively compared using multivariate normal data sets simulated with various correlation structures. They include multiple univariate CUSUM charts, multivariate EWMA charts, multivariate CUSUM charts and Shewhart T$^{3}$ chart. This paper considers a new approach of the multivariate EWMA chart, in which the smoothing matrix has full elements instead of only diagonal elements. Performance of the schemes is measured by avaerage run length (ARL), coefficient of variation of run length (CVRL) and rank in order of signaling of off-target shifts in the process mean vector. The schemes are also compared by noncentrality parameter. The multiple univariate CUSUM charts are generally affected by the correlation structure. The multivariate EWMA charts provide better ARL performance. Especially, the new EWMA chart shows remarkable results in small shifts.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.285-295
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• 2004

Statistical process control(SPC) and engineering process control(EPC) are two complementary strategies for quality improvement. An integrated process control(IPC) can use EPC to reduce the effect of predictable quality variations and SPC to monitor the process for detection of special causes. In this paper we assume an IMA(1,1) model as a disturbance process and an occurrence of a level shift in the process, and we consider the economic performance for applying an EWMA chart to monitor MMSE-controlled processes. The numerical results suggest that the IPC scheme in an IMA(1,1) disturbance model does not give additional advantages in the economic aspect.

• 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.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.725-737
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• 2022

In the standard assumption of statistical process monitoring (SPM) under consideration, the in-control region of the control parameter of quality characteristic consists of a single point. However, if small deviations from the ideal situation may not be of practical importance, the parametric space can consist of three regions: In-control, indifference, and out-of-control. In this paper, we propose two exponentially weighted moving average (EWMA) charting procedures applicable to the situation with three parameter regions, and compare the efficiency of the proposed procedures with the Shewhart chart and the cumulative sum (CUSUM) chart.

• 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 Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.118-125
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• 2012

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called 'adjacent point operation', becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.94-101
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• 2009

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. S.M. Na et al.(2003) proposed the Demerit-CUSUM for the improvement of the demerit control chart performance and Nembhard, D. A. et al.(2001) and G.Y Cho et al.(2004) developed a Demerit control chart using the EWMA technique and evaluated the performance of the control chart. In this paper, we present an effective method for process control using the Demerit-CUSUM with fast initial response. Moreover, we evaluate exact performance of the Demerit-CUSUM control chart with fast initial response, Demerit-CUSUM and Demerit-EWMA according to changing sample size or parameters.

• Journal of Integrative Natural Science
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• v.7 no.3
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• pp.200-207
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• 2014

Average number of samples to signal (ANSS) and average time to signal (ATS) are the most widely used criterion for comparing the efficiencies of the quality control charts. In this study the method of evaluating ANSS and ATS values of the multivariate exponentially weighted moving average (EWMA) control charts with Markov chain approach was presented when the production process is in control state or out of control state. Through numerical results, it is found that when the number of transient state r is less than 50, the calculated ANSS and ATS values are unstable; and ATS(r) tends to be stabilized when r is greater than 100; in addition, when the properties of multivariate EWMA control chart is evaluated using Markov chain method, the number of transient state r requires bigger values when the smoothing constatnt ${\lambda}$ becomes smaller.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.70-88
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• 2000

Short runs where it is neither possible nor practical to obtain sufficient subgroups to estimate accurately the control limit are common in modem business environments. In this study, the standardized control chart, Hillier's exact method, Q chart, EWMA(Exponentially Weighted Moving Average) chart for Q statistics and EWMA chart for mean and absolute deviation among many SPC(Statistical Process Control) techniques for short runs have been reviewed and advantages and disadvantages of these techniques are discussed. The simulation experiments to compare performances of these variable charts for process mean and variations are conducted for combination of subgroup size, scale and timing of shifts of process mean an/or standard deviation. Based upon simulation results, some guidelines for practitioners to choose short run SPC techniques are recommended.