• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.907-920
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• 2015

Charts based on geometric distribution are effective to monitor the proportion of nonconforming items in high-quality processes where the in-control proportion nonconforming is low. The implementation of this chart is often based on the assumption that in-control proportion nonconforming is known or accurately estimated. However, accurate parameter estimation is very difficult and may require a larger sample size than that available in practice for high-quality process where the proportion of nonconforming items is very small. An inaccurate estimate of the parameter can result in estimated control limits that cause unreliability in the monitoring process. The maximum likelihood estimator (MLE) is often used to estimate in-control proportion nonconforming. In this paper, we recommend a Bayes estimator for the in-control proportion nonconforming to incorporate practitioner knowledge and avoid estimation issues when no nonconforming items are observed in the Phase I sample. The effects of parameter estimation on the geometric chart and the geometric CUSUM chart are considered when the MLE and the Bayes estimator are used. The results show that chart performance with estimated control limits based on the Bayes estimator is generally better than that based on the MLE.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.379-386
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• 2002

As the value and relative importance of service industry boosts in modern society, to strengthen competitiveness companies starts to recognize the quality of service as a key component in their business operations. Furthermore, companies that apply the quality of services as their competitive method are concentrating more on the area of quality control. Nevertheless, the previous researches related to this area were more focused on the aspect of measure rather than the control. In this paper, each fractionated customer group in consideration of its characteristics is made in use of estimating the quality of service using the conjoint analysis. The principal variable selective method which a small number of variables is used to control a large number of variables is applied. Hotelling's $T^2$ control chart is applied to administer many relative variables of the quality of service because it is a Multi-item. If abnormal indications appear by adding new data, the method to find and improve variables using MYT decomposition is suggested to apply.

• Journal of Sericultural and Entomological Science
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• v.17 no.1
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• pp.69-74
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• 1975

This report was aimed to examine the status of raw silk quality and to come up with means of the good quality silk production by use of the statistical quality control method. The results obtained are as follow ： 1) The raw silk quality of size 20/22 denier was found to be 2A grade during the last five years. 2) In order to level up the silk grade, the better quality control is required especially in the average evenness and cleanness. 3) It was found that winding brakes and elongation of silk threads have been versed gradually through the period. 4) Larger variation was observed in cleanness control chart, but low evenness have stayed with in the desirable control range.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.189-196
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• 1994

Quality assurance in the process of manufacturing high strengh bolt sets used in the frictional joints of structures in one of important concern to users as well as to manufactures. In case of occurrences of either defective or low-quality products, even if they are quite rare, some systematic means of localizing the cause-characteristics and matching to corresponding production process is necessary. Control chart of torque factor is the primary indicatir in finding defectiveness of the products. Use of correlation diagrams ofnhardness of the bolt set presents in part a way of screening the cause. Retest data of the bolt set provide additional ideas of localizing the cause, for which theoretical background is presented in this regard. A process-characteristics matrix relating the causes of low quality to the corresponding process of manufacturing, which is of prime importance for the feedback control of production, is also proposed. Finally general features of control to assure quality of the set is described.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.21-29
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• 2002

Several different control statistics to simultaneously monitor dispersion matrix of several quality variables are presented since different control statistics can be used to describe variability. Multivariare cumulative sum (CUSUM) control charts are proposed and the performances of the proposed CUSUM charts are evaluated in terms of average run length (ARL). Multivariate Shewhart charts are also proposed to compare the properties of the proposed CUSUM charts. The numerical results show that multivariate CUSUM charts are more efficient than multivariate Shewhart charts for small or moderate shifts. And we also found that small reference value of the CUSUM chart is more efficient for small shift.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.673-679
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• 1998

The paper aims to apply a model of continuous quality improvement (CQI) to Taiwan provincial Pin Ton hospital. Tools of quality control were used both total quality management (TQM) and quality control circle (QCC) in order to approach the objective of ${\ulcorner}$higher quality, higher efficiency, lower cost${\lrcorner}$. During two years, this hospital has organized thirteen QC circles that were classified three categories according to that objective: four circles work for higher quality; the other four circles work for higher efficiency; five circles for lower cost. After implementation, several results were found from two viewpoints. From viewpoint of tangibility, one of the most outcomes is to reduce labor cost from 56 persons to 28 persons of temporary employees. From viewpoint of intangibility, the result was evaluated using radar chart to compare the difference after improvement. This paper is going to report the process of CQI for the hospital management because it is a new issue in Taiwan since 1997

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.1-8
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• 2002

At present, company has to produce a product that consumer like with a competitive price, a good quality, and a fitting time to supply. Process control and qualify control are very important to supply with a product uniformly and inexpensively In this paper, the characteristic of product quality is monitored by control chart during the machining process and construction of quality control cycle is considered to divide into two types in this case that different assignable causes lead to shifts having different magnitudes. Then we are intended to find a process shift magnitude which has economical quality cost policy and are considered to quality cost functions to find a process shift magnitude. Those costs are categorized into the well-known categories of prevention, appraisal, and internal failure and external failure. This paper ends with numerical examples that demonstrate the usefulness of the model.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.22-24
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• 2006

The installations of power quality monitoring system have increased drastically over the past several decades. These systems have been effectively used to monitor, analyze and diagnose the conditions of power system, and furthermore can be used to improve the present asset maintenance policy, scheduled (time-based) method, into the advanced, cost-effective and labor-effective maintenance methods, such as condition-based maintenance, predictive maintenance and reliability centered maintenance. As an approach to this, this paper introduces the statistical methods, three kinds of control charts (Shewhart chart, CUSUM chart and EWMA chart), and discusses the applicability of these methods to recognize the changing trends of power quality indices and to estimate the system's condition, using Matlab.

• Korean Journal of Digital Imaging in Medicine
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• v.3 no.1
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• pp.126-132
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• 1997

Purpose : To apply to Program of Auto processor quality control after comparison of Film density variations with amendments to Auto density by using Check density program and Adjust density program of calibration mode into the Laser imager. Methods : Observe Check and Adjust density variations on the Control chart with standard step and value during seven months from December, 1995 to June, 1956 extending twice a week. (1) Measure density value on the steps after printing out 17-step sensitometric pattern of the Check density program. (2) In the same way, measure density values after amending density by using Adjust density program If they are exceeding allowable error limit. Results : In case of Check density program, the exceeding limit rates of Density difference(DD) and Middle density(MD) are: FL-IM3543 DD=75%. MD=72.5%, FL-IMD DD=0%. MD=30.8%(14.5%) After amending density by using Adjust density program, the exceeding limit rates of all both Laser imager were zero percent. The standard deviations are show lower FL-IM D than FL-IM3543 on the Check density control chart, but higher on the Adjust density control chart. Conclusion : (1) Check density variations by printingout sensitometric pattern extending once a week at least for quality control of the Laser imager. (2) In case of a dusty place, check the Laser beam transmission after cleaning Laser optical unit extending once a month. (3) Be sure to measure and check density values by using adjust density program if they are exceeding allowable error limit. (4) Maintain much better film density by performing the adjust density program even if check density values are existed within normal limit.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.134-144
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• 2014

With the advent of lean-six sigma era, an extensive use of analytic tools such as control charts is required in the field of manufacturing. In relation to statistical quality control (SQC) or process control (SPC), the Korean standards have undergone a meaningful change. In this study, the theoretic backgrounds for evaluating the control limits in connection with the variable control charts are examined in view of better understanding the related constants and coefficients. This paper is intended to help the quality control practitioners understand the mathematical backgrounds by comparing related quality control constants and also to encourage them to make use of and to take the advantage of the variable control charts which are very useful for implementing the concept of lean-six sigma in many industrial sites.