• 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.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.108-125
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• 1998

The EWMA(Exponentially Weighted Moving Average) has recently received a great deal of attention in the quality control literature as a process monitoring tool on the shop floor of manufacturing industires, since it is easy to plot, to interpret, and its control limits are easy to obtain. Most a, pp.ications of the EWMA for process monitoring have concentrated on the problem of detecting shifts of a process mean and a process standard deviation with ARL(Average Run Length) properties. But there may be the necessity of controlling linearity on product quality such as the correlation coefficient to the process operator. Control managers may want to protect the increase of a process correlation coefficient value, such as 0, between two variables of interest. However, there are few studies concerned on this part. Therefore, we propose EWMA models for a process correlation coefficient using two transformed statistics, T-statistic and (Fisher's) Z-statistic. We also present some results of simulation by SAS/IML and compare two models.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.43-55
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• 2007

Moving average(MA) and exponentially weighted moving average(EWMA) are the two most popular control methods in manufacturing. Both methods are optimized under the assumption that the exact control equation is known. This paper focuses on the problems rising from estimation errors. Based on the accuracy of the estimated parameter and the range of the weight parameter $\lambda$, the limitations are identified and the performance of methods are evaluated. Optimal adjustment for process shift with misestimated control model and its application control methods to actual process is researched. The efficiency of proposed method is evaluated through simulation.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.255-263
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• 2010

The research developes short-run standardized control charts(SSCC) and short-run acceptance control charts(SACC) under the various demand patterns. The demand patterns considered in this paper are three types such as high-variety and repetitive low-volume pattern, extremely-high-variety and nonrepetitive low-volume pattern, and high-variety and extremely-low-volume pattern. The short-run standardized control charts developed by extending the long-run ${\bar{x}}$-R, ${\bar{x}}$-s and I-MR charts have strengths for practioners to understand and use easily. Moreover, the short-range acceptance control charts developed in the study can be efficiently used through combining the functions of the inspection and control chart. The weighting schemes such as Shewhart, moving average (MA) and exponentially weighted moving average (EWMA) can be considered by the reliability of data sets. The two types according to the use of control chart are presented in the short-range standardized charts and acceptance control charts. Finally, process capability index(PCI) and process performance index(PPI) classified by the demand patterns are presented.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.257-262
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• 2010

The study investigates the various Acceptance Control Charts (ACCs) based on the factors that include process independence, data weighting scheme, subgrouping, and use of control charts. USL - LSL > $6{\sigma}$ that used in the good condition processes in the ACCs are designed by considering user's perspective, producer's perspective and both perspectives. ACCs developed from the research is efficiently applied by using the simple control limit unified with APL (Acceptable Process Level), RLP (Rejectable Process Level), Type I Error $\alpha$, and Type II Error $\beta$. Sampling interval of subgroup examines i.i.d. (Identically and Independent Distributed) or auto-correlated processes. Three types of weight schemes according to the reliability of data include Shewhart, Moving Average(MA) and Exponentially Weighted Moving Average (EWMA) which are considered when designing ACCs. Two types of control charts by the purpose of improvement are also presented. Overall, $\alpha$, $\beta$ and APL for nonconforming proportion and RPL of claim proportion can be designed by practioners who emphasize productivity and claim defense cost.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.196-204
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• 2013

In this paper, a scheduling algorithm is proposed for military satellite networks to improve QoS(Quality of Service) based on WDRR(Weighted Deficit Round Robin) method. When the packet size that has been queued to be larger, the proposed scheme DWDRR(Dynamic WDRR) method give appropriate additional quantum using EWMA(Exponentially Weighted Moving Average). To demonstrate an usefulness of proposed algorithm using OPNET modeler that built the simulation environment, reliability and real-time availability of the proposed algorithm is analyzed. The simulation results show an availability of proposed scheme in terms of reduce queuing delay and packet drop rate compared and analyzed the existing algorithms WRR(Weighted Round Robin), DRR(Deficit Round Robin) and WDRR with DWDRR.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.155-167
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• 2007

Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose the maximum likelihood estimator (MLE) for the process change point when a control chart is used in monitoring the mean of a process in which the observations can be modeled as an AR(1) process plus an additional random error. The performance of the proposed MLE is compared to the performance of the built-in estimator when they are used in EWMA charts based on the residuals. The results show that the proposed MLE provides good performance in terms of both accuracy and precision of the estimator.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.915-925
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• 2007

Multivariate GARCH has been useful to model dynamic relationships between volatilities arising from each component series of multivariate time series. Methodologies including EWMA(Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model) models are comparatively reviewed for bivariate time series. In addition, these models are applied to evaluate VaR(Value at Risk) and to construct joint prediction region. To illustrate, bivariate stock prices data consisting of Samsung Electronics and LG Electronics are analysed.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.525-533
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• 2015

Modern production process is a very complex structure combined observations which are correlated with several factors. When the error signal occurs in the process, it is very difficult to know the root causes of an out-of-control signal because of insufficient information. However, if we know the time of the change, the system can be controlled more easily. To know it, we derive a maximum likelihood estimator (MLE) of the change point in a process when observations are from a multivariate IMA(1,1) process by monitoring residual vectors of the model. In this paper, numerical results show that the MLE of change point is effective in detecting changes in a process.