• Journal of the Korea Safety Management & Science
• /
• v.12 no.4
• /
• pp.255-263
• /
• 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 Korean Institute of Industrial Engineers
• /
• v.32 no.3
• /
• pp.210-218
• /
• 2006

Traditional statistical process control (SPC) applied to discrete part industry in the form of control charts can look for and eliminate assignable causes by process monitoring. On the other hand, engineering process control (EPC) applied to the process industry in the form of feedback control can maintain the process output on the target by continual adjustment of input variable. This study presents controlling and monitoring rules adopted by variable sampling interval (VSI) to change sampling intervals in a predetermined fashion on the predicted process levels under integrated EPC and SPC systems. Twelve rules classified by EPC schemes(MMSE, constrained PI, bounded or deadband adjustment policy) and type of sampling interval combined with EWMA chart of SPC are proposed under IMA (1,1) disturbance model and zero-order (responsive) dynamic system. Properties of twelve control rules under three patterns of process change (sudden shift, drift and random shift) are evaluated and discussed through simulation and control rules for integrated VSI EPC and SPC systems are recommended.

• Journal of Korean Society for Quality Management
• /
• v.38 no.2
• /
• pp.190-201
• /
• 2010

This research proposes a method for designing VSI (Variable Sampling Interval) control charts with supplementary run rules. The basic idea is to apply various run rules and the VSI scheme to a control chart in order to increase the sensitivity. The sampling process of the VSI run rules chart is constructed by Markov chain approach. A procedure for designing the VSI run rules chart is proposed based on Lorenzen and Vance's model. Sensitivity study shows that the VSI run rules charts outperform the FSI (Fixed Sampling Interval) run rules charts for wide range of process mean shifts. A major advantage of the VSI run rules chart over other charts such as CUSUM, EWMA, and adaptive charts is it's simplicity in implementation. Some useful guidelines are proposed based on the sensitivity study.

• Proceedings of the Safety Management and Science Conference
• /
• 2002.05a
• /
• pp.81-89
• /
• 2002

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. In the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressive disturbance. We compare three control systems； EPC, EPC combined with EWMA, and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

• Proceedings of the Safety Management and Science Conference
• /
• 2003.05a
• /
• pp.277-283
• /
• 2003

EPC seeks to minimize variability by transferring the output variable to a related process input(controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. Tn the case of product control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We consider an alternative EPC model with second-order autoregressed disturbance. We compare three control systems; EPC, EPC combined system with EWMA, CUSUM and Shewhart. This paper shows through simulation that the performance of the integrated model of EPC and SPC is more preferable than that of EPC.

• Journal of Korea Society of Industrial Information Systems
• /
• v.6 no.3
• /
• pp.65-70
• /
• 2001

This Paper Propose the control chart Pattern to provide a more comprehensive scheme for detecting process shifts using individual observations in start-up process. In this paper, which uses the backpropagation algorithm two samples are fed into the trained neural network to provide outputs ranging from 0 to 1. The main advantage of using neural networks approach with a control chart is that the neural network has almost no delay in detecting small shift. This paper illustrates how neural networks can provide a useful method for optimizing parameter(connection weights) that affect process control. Simulation results show that the performance of the proposed control chart using the neural network (NNCC) is quite promising.

• Progress in Medical Physics
• /
• v.25 no.3
• /
• pp.185-192
• /
• 2014

The purpose of this study is to evaluate the results for the quality assurance through a statistical analysis on the output characteristics of linear accelerators belonging to Yeungnam University Medical Center by using the Shewhart-type chart, Exponentially weighted moving average chart (EWMA) chart, and process capability indices $C_p$ and $C_{pk}$. To achieve this, we used the output values measured using respective treatment devices (21EX, 21EX-S, and Novalis Tx) by medical physicists every month from September, 2012 to April, 2014. The output characteristics of treatment devices followed the IAEA TRS-398 guidelines, and the measurements included photon beams of 6 MV, 10 MV, and 15 MV and electron beams of 4 MeV, 6 MeV, 9 MeV, 12 MeV, 16MeV, and 20 MeV. The statistical analysis was done for the output characteristics measured, and was corrected every month. The width of control limit of weighting factors and measurement values were calculated as ${\lambda}=0.10$ and L=2.703, respectively; and the process capability indices $C_p$ and $C_{pk}$ were greater than or equal to 1 for all energies of the linear accelerators (21EX, 21EX-S, and Novalis Tx). Measured values of output doses with drastic and minor changes were found through the Shewhart-type chart and EWMA chart, respectively. The process capability indices $C_p$ and $C_{pk}$ of the treatment devices in our institution were, respectively, 2.384 and 2.136 for 21EX, 1.917 and 1.682 for 21EX-S, and 2.895 and 2.473 for Novalis Tx, proving that Novalis Tx has the most stable and accurate output characteristics.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.451-459
• /
• 2017

There are many researches showing that when a process change has occurred, variable sampling intervals (VSI) control chart is better than the fixed sampling interval (FSI) control chart in terms of reducing the required time to signal. When the process engineers use VSI control procedure, frequent switching between different sampling intervals can be a complicating factor. However, average number of samples to signal (ANSS), which is the amount of required samples to signal, and average time to signal (ATS) do not provide any control statistics about switching performances of VSI charts. In this study, we evaluate numerical switching performances of multivariate VSI EWMA chart including average number of switches to signal (ANSW) and average switching rate (ASWR). In addition, numerical study has been carried out to examine how to improve the performance of considered chart with accumulate-combine approach under several different smoothing constant and sample size. In conclusion, process engineers, who want to manage the correlation coefficients of related quality variables, are recommended to make sample size as large and smoothing constant as small as possible under permission of process conditions.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.16 no.27
• /
• pp.51-71
• /
• 1993

본 논문은 새로운 3차원 분류체계를 이용해서 관리도의 문헌을 고찰하고 검토하는데 연구 목적이 있다. 1차원 분류체계는 시간에 따른 연속된 관측치의 관계가 독립인가 자동상판인가로 나누어지며 2차원 분류체계는 독립관측치인 경우 가중치 방법에 따라 Shewart, MA EWMA, CUSUM Charts로 분류되며 자동상관된 관측치인 경우 모델링 방법에 따라 ARIMA, Spectral Charts로 분류된다. 3차원 분류체계는 품질특성인 변수의 수와 종속관계에 따라 일변량과 다변량으로 나누어 진다. 재래식 생산, 자동화 생산, 혹은 장치산업에 적용될 수 있는 관리도가 이 분류체계에 따라 장으로 구분되어 고찰된다. 이는 실무진들의 이해를 돕기 위한 지침으로 활용될 수 있다.

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