• 경영과학
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• 제31권3호
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• pp.27-39
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• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

• 대한안전경영과학회지
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• 제8권6호
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• pp.109-118
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• 2006

This paper proposes service layout strategy considering service characteristics by the use of benchmarking production system such as layout by P-Q chart, improvement tool, automated system, Toyota production system and lean production system. This paper represents operation methodology of statistical process control using control chart for service performance outcomes.

• 품질경영학회지
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• 제35권2호
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• pp.37-44
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• 2007

Today, the statistical process control (SPC) in manufacture environment is an important role at the process by the productivity improvement of the manufacturing systems. The control chart in this statistical method is widely used as an important statistical tool to find the assignable cause that provoke the change of the process parameters such as the mean of interest or standard deviation. But the traditional SPC don't grasp the change of process according to the points fallen the near control limits because of monitoring the variance of process such as the fixed sampling interval and the sample size and handle the cost of the aspect of these sample point. The control chart can be divided into the statistical and economic design. Generally, the economic design considers the cost that maintains the quality level of process. But it is necessary to consider the cost of the process improvement by the learning effects. This study does the economic design in the VSI $\bar X$ control chart and added the concept of loss function of Taguchi in the cost model. Also, we preyed that the VSI $\bar X$ control chart is better than the FSI $\bar X$ in terms of the economic aspects and proposed the standard of the process improvement using the VSI $\bar X$ control chart.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.723-729
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• 2005

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 variable sampling interval(VSI) to change sampling intervals in a predetermined fashion on the predicted process levels for 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. The 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.

• 응용통계연구
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• 제11권1호
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• pp.13-28
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• 1998

품질향상을 위한 통계적 공정관리 추진시 데이터의 신뢰성 확보는 무엇보다 중요하다. 그런데 측정치는 계측기 뿐만 아니라 측정자, 측정방법, 측정재료 등 보다 많은 요인들에 의해 영향을 받는다. 본 논문에서는 고전적인 측정시스템 평가에서 주로 관리하는 정확도, 정밀도 및 안정도를 실제 데이터를 이용하여 어떻게 평가하는지 알아보기로 한다.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.381-399
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2006년도 추계 학술대회
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• pp.211-216
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• 2006

Monitoring variability is a vital part of modem statistical process control. The conventional Shewhart Rand S charts address the setting where the in-control process readings have a constant variance. In some settings, however, it is the coefficient of variation, rather than the variance, that should be constant. This paper develops a chart, equivalent to the S chart, for monitoring the coefficient of variation using rational groups of observations.

• 품질경영학회지
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• 제44권1호
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• pp.1-16
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• 2016

Purpose: This paper reviews the papers on statistical quality control issues which are published in Journal of the Korean Society for Quality Management (KSQM) since 1965. The literature review is purposed to survey a variety of statistical quality control issues. Methods: By grouping all of statistical quality control issues into 3 categories:; quality inspections, control charts, and process capability analysis. Results: Grouping all of papers on statistical quality control published in journal of the KSQM for 50 years into 3 categories, we provide a chronological roadmap for individual categories, and summarize the contents and contributions of surveyed papers. Conclusion: The review paper is expected to provide future direction to improve statistical quality control theories and applications in manufacturing and service industries.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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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.

• 한국산업정보학회논문지
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• 제8권1호
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• pp.1-8
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• 2003

There are many statistical evaluation methods, A more technical Perspective is needed in estimating the effect of the Manufacturing Process for improving the Productivity, Process network analysis is a technique which has the potentiality for a wire use to improve the manufacturing process which other techniques can't be used to analyze effectively. The concept of six sigma plan was developed and pursued by Motorola to improve the process control. The goals of six sigma plan are established on the foundation of customer satisfaction such as Quality, Cost Delivery and Service This paper presents how to improve the manufacturing process by statistical process control for the evaluation of six sigma level.