• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.65-77
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• 2020

Geometric charts are effective in monitoring the fraction nonconforming in high-quality processes. The in-control fraction nonconforming is unknown in most actual processes; therefore, it should be estimated using the Phase I sample. However, if the Phase I sample size is small the practitioner may not achieve the desired in-control performance because estimation errors can occur when the parameters are estimated. Therefore, in this paper, we adjust the control limits of geometric charts with the bootstrap algorithm to improve the in-control performance of charts with smaller sample sizes. The simulation results show that the adjustment with the bootstrap algorithm improves the in-control performance of geometric charts by controlling the probability that the in-control average run length has a value greater than the desired one. The out-of-control performance of geometric charts with adjusted limits is also discussed.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.71-85
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• 2003

In the economic design of the process control procedure, where quality is measured at certain time intervals, its properties are difficult to derive due to the discreteness of the measurement intervals. In this paper a Markov chain representation of the process monitoring procedure is developed and used to derive its properties when the process follows an ARIMA(0,1,1) model, which is designed to describe the effect of the noise and the special cause in the process cycle. The properties of the Markov chain depend on the transition matrix, which is determined by the control procedure and the process distribution. The derived representation of the Markov chain can be adapted to most different types of control procedures and different kinds of process distributions by obtaining the corresponding transition matrix.

• Industrial Engineering and Management Systems
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• v.8 no.3
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• pp.139-147
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• 2009

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

• Proceedings of the Safety Management and Science Conference
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• 2010.04a
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• pp.509-517
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• 2010

The paper proposes the misuse types of statistical quality tools according to the kind of data and the number of population in DMAIC process of six sigma. The result presented in this paper can be extended to the QC story 15 steps of QC circle. The study also provides the improvement methods about control chart, measurement system analysis, statistical difference, and practical equivalence.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.33-39
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• 2006

These days manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. In order to win international competition, it is important for companies how fast get the useful information from vast data. Statistical process control(SPC) techniques have been used as a problem solution tool at manufacturing process until present. However, these statistical methods are not applied more extensively because it has much restrictions in realistic problems. These statistical techniques have lots of problems when much data and factors are analyzed. In this paper, we proposed more practical and efficient a new statistical design technique which integrated data mining (DM) and statistical methods as alternative of problems. First step is selecting significant factor using DM feature selection algorithm from data of manufacturing process including many factors. Second step is finding optimum of process after estimating response function through response surface methodology(RSM) that is a statistical techniques

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.167-174
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• 2012

In the manufacturing process the most widely used $\bar{X}$ chart has been applied to control the process mean. Also, Accelerated Life Test(ALT) is commonly used for efficient assurance of product life in development phases, which can be applied in production reliability acceptance test. When life data has lognormal distribution, through censored ALT design so that censored ALT data has asymptotic normal distribution, $ALT\bar{X}$ control chart integrating $\bar{X}$ chart and ALT procedure could be applied to control the mean of process in the manufacturing process. In the situation that process variation is controlled, $Z_p$ control chart is an effective method for the very small fraction nonconforming of quality characteristic. A simultaneous control scheme with $ALT\bar{X}$ control chart and $Z_p$ control chart is designed for the very small fraction nonconforming of product lifetime.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.773-782
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• 2007

The statistical process control charts are very extensively used for monitoring of process mean, deviation, defect rate or reject rate. In this paper we consider a control chart to monitor the process reject rate in the high yield process, which is based on the observed cumulative probability of the number of items inspected until r defective items are observed. We first propose selection of the optimal value of r in the CPC-r charts, and also consider the usefulness of the chart in high yield process such as semiconductor or TFT-LCD manufacturing process.

• 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.

• Journal of Korean Society for Quality Management
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• v.31 no.3
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• pp.126-135
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• 2003

A basic assumption in standard applications of control charts is that the observations are statistically independent. However, this assumption is often violated from processes in many industries. The presence of autocorrelation has a serious impact on the performance of control charts, causing a dramatic increase in the frequency of false alarms. This paper considers a process in which the observations can be modeled as a first order autoregressive(AR(1)) process, and develops (equation omitted) charts with the variable sample size(VSS) scheme for monitoring the mean of this process.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.395-403
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• 2012

Process adjustment, also called engineering process control(EPC), is applied to maintain process output close to a target value by manipulating controllable variables, but special causes may still make the process deviate from the target and result in significant costs. Thus, it is important to detect and mediate deviations as early as possible. We propose a one-step detection method, the moving search block(MSB), with which the time and type of a special cause can be identified in short periods. A modified control rule that can entertain the effects of the special cause is proposed. A numerical example is presented to evaluate the performance of the proposed scheme.