• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.503-503
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• 1994

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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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.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.63
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• pp.65-77
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• 2001

A more technical perspective is needed in estimating the effect of the Manufacturing Process for improving the Productivity, there are many statistical evaluation methods, convenience sampling, frequencies, histogram, QC seven tools, control chart etc. It is more important for the companies to use six sigma to reduce defective and improve the process control than the technical definition as a disciplined quantitative approach for improvement of process control and a new way of quality innovation. Process network analysis is a technique which has the potentiality for a wide use to improve the manufacturing process which other techniques can't be used to analyze effectively. It has some problems to analyze the process with feedback loops. The branch probabilities during quality inspections depend upon the number of times the product has been rejected. This paper presents how to improve the manufacturing process by statistical process control using branch probabilities, Moment Generating Function(MGF) and Sensitivity Equation.

• Journal of Korean Society for Quality Management
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• v.44 no.1
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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.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.753-763
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• 2002

When there is one change-point in the hazard rate model, a change-point estimator with the partial score process is suggested and compared with the previously developed estimators. The limiting distribution of the partial score process we used is a function of the Brownian bridge. Simulation study gives the comparison of change-point estimators.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.504-512
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• 1994

A simple proof for the random central limit theorem is given for a family of stationary linear lattice processes, which belogn to a class of 2 dimensional random fields, applying the Beveridge and Nelson decomposition in time series context. The result is an extension of Fakhre-Zakeri and Fershidi (1993) dealing with the linear process in time series to the case of the linear lattice process with 2 dimensional indices.

• Journal of the Korean Statistical Society
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• v.26 no.1
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• pp.75-88
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• 1997

In this paper, a two-parameter version of Ikeda-Watanabe's mollifiers approximation of the Brownian motion is considered, and an approximation theorem corresponding to the one parameter case is proved. Using this approximation, we formulate Wong-Zakai type theorem is a Stochastic Differential Equation (SDE) driven by a two-parameter Wiener process.

• Journal of the Korean Statistical Society
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• v.26 no.1
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• pp.117-130
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• 1997

We study the asymptotic behavior of the maximum partial likelihood estimator in the Cox proportional hazards model in the presence of nuisance parameters when the entry of patients is staggered. When entry of patients is simultaneous and there is only one regression parameter in the Cox model, the efficient score process of the partial likelihood is martingale and converges weakly to a time-chnaged Brownian motion. Our problem is to get a similar result in the presence of nuisance parameters when entry of patient is staggered.

• Journal of Korean Society for Quality Management
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• v.27 no.4
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• pp.256-265
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• 1999

In this paper, we Present the implementation of statistical process control software by using XLISP-STAT which is a kind of object oriented language under Windows environment. This software can be used to generate the graphic objects for various control charts, histogram and plots using the full-down menu system. This software can also be used to calculate control limits, process capability indices and test procedures for normality.