• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.239-246
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• 2003

Individual items are produced continuously from an industrial process. Each item is checked to determine whether it satisfies a lower screening limit for the quality characteristic which is the weight of an expensive ingredient. If it does, it is sold at a regular price; if it does not, it is reprocessed or sold at a reduced price. The process mean may be adjusted to a higher value in order to reduce the proportion of the nonconforming items. Using a higher process mean, however, may result in a higher production cost. In this paper, the optimal process mean and lower screening limit are determined in situations where the probability that an item functions well is given by a logistic function of the quality characteristic. Profit models are constructed which involve four price/cost components; selling prices, cost from an accepted nonconforming item, and reprocessing and inspection costs. Methods of finding the optimal process mean and lower screening limit are presented and numerical examples are given.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.41-50
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• 1994

Consider a canning process where cans are filled with an expensive ingredient. Cans weighting above the specified limit are sold in a regular market for a fixed price, and underfilled cans are emptied and refilled at the expense of a reprocessing cost. In this paper, the effect of measurement error on the determination of the optimal process mean for a canning process is examined. It is assumed that the quantity X of ingredient in a can is normally distributed with unknown mean and known variance, and the observed value Y of X is also normally distributed with known mean and variance. A profit model is constructed which involves selling price. cost of ingredients, reprocessing cost. and cost from an accepted nonconforming can, and methods of finding the optimal process mean and the cutoff value on Y are presented. It is shown that the optimal process mean increases. and the expected profit decreases when the measurement error is relatively large in comparison to the process variance.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.3-13
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• 1994

The problem of selecting the optimal target values in a canning process is considered for situations where there is a linear shift in the mean of the content of a can which is assumed to be normally distributed with known variance. The target values are initial process mean, length of resetting cycle and controllable upper limit. Profit models are constructed which involve give-away, rework, and resetting costs. Methods of finding the optimal target values are presented and a nemerical example is given.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.1-16
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• 2000

This paper considers the problem of determining the optimal process mean and screening specification limits of a surrogate variable associated with product quality under two-stage screening procedure. In two-stage screening, the surrogate variable is inspected first to decide whether an item should be accepted, rejected or additional observations should be taken. If additional observations are required, the performance variable of interest is then observed to classify the undecided items. Assuming that the performance variable and the surrogate variable are jointly normally distributed, the optimal process mean and the screening limits are obtained by maximizing the expected profit which includes selling price, production, reprocessing, inspection and penalty costs. A numerical example is presented and numerical studies are performed to compare the proposed two-stage screening procedure with single-stage screening procedures.

• Journal of Korean Society for Quality Management
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• v.34 no.2
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• pp.12-21
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• 2006

This paper considers the problem of selecting the most profitable process mean for production processes where measurement errors exist in inspection systems. For such situations, a sequential inspection procedure is proposed to reduce measurement errors. The decision to accept, reject, or take an additional inspection of an item is made at every measurement point until the number of repeated measurements reaches its upper bound. An expected profit model is constructed and the optimal process mean, the cut-off values, and the upper bound of the number of repeated measurements are obtained when accepted(rejected) items are sold at regular(reduced) price. A numerical study is performed to investigate the performance of the proposed procedure.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.61-73
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• 1998

The quality control literature contains a substantial number of articles concerned with how to optimally choose control limits in order to minimize production cost. The purpose of the this study is to determine the economic setting for the process mean of an industrial process. In this study it is assumed that the lower control limit is set by government regulations and the u, pp.r limit and process mean are chosen based on economic considerations. Much research has been conducted on this problem under the condition of the fixed rpm(rate per minute). However a variance can be increased in proportion to the level of rpm and the increase of the variance can change the optimal process mean. Therefore, it is desirable to determine both the process mean and the level of rpm simultaneously. In this paper, a mathematical model is presented which considers the u, pp.r limit and the rpm as variables.

• Journal of Korean Society for Quality Management
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• v.45 no.2
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• pp.293-306
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• 2017

Purpose: In this study, an optimal design level of influencing factors on semiconductor polishing process was determined to minimize flexion of both sides on wafers. Methods: First, significant interactions are determined by the stepwise regression method. ANOVA analysis on SN ratio and mean of dependent variable are performed to draw mean adjustment factors. In addition, the optimal levels of mean adjustment factors are decided by comparing means of each level of mean adjustment factors. Results: As a result of ANOVA, a mean adjustment factor was determined as a width of formed flexion on the plate. The mean of the difference has the nearest to 0 in the case when the width of formed flexion has level 2 (4mm). Conclusion: Optimal design levels of semiconductor polishing process are determined as follows; (i) load applied to the wafer carrier has a level 1 (3psi), (ii) load applied to the wafer has a level 1(3psi), (iii) the amount of slurry supplied during polishing has a level 3 (300 co/min), (iv) the width of formed flexion on the plate has level 2 (4mm).

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.465-470
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• 2012

EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

• Knowledge Management Research
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• v.20 no.3
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• pp.39-47
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• 2019

Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well-known multiresponse problem.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.46-59
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• 1998

The problem of selecting optimal target values for the mean of the quality characteristic of interest for a production process in which an item is sold in one of two market with different profit / cost structures or reworked. Two profit models are constructed which involve four profit / cost components: profit, production, inspection, and rework costs. Assumed that the quality characteristic of interest is normally distributed, methods of finding the most profitable process mean are presented and a numerical example is given.