• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.165-172
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• 2017

Machines are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. Under the process mean shift, production cost, failure cost and quality loss function cost are increasing continuously. Therefore a periodic preventive resetting the process is necessary. We suppose that the wear level is observable. In this case, process mean shift problem has similar characteristics to the maintenance policy model. In the previous studies, process mean shift problem has been studied in several fields such as 'Tool wear limit', 'Canning Process' and 'Quality Loss Function' separately or partially integrated form. This paper proposes an integrated cost model which involves production cost by the material, failure cost by the nonconforming items, quality loss function cost by the deviation between the quality characteristics from the target value and resetting the process cost. We expand this process mean shift problem a little more by dealing the process variance as a function, not a constant value. We suggested a multiplier function model to the process variance according to the analysis result with practical data. We adopted two-side specification to our model. The initial process mean is generally set somewhat above the lower specification. The objective function is total integrated costs per unit wear and independent variables are wear limit and initial setting process mean. The optimum is derived from numerical analysis because the integral form of the objective function is not possible. A numerical example is presented.

• 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 the Korean Statistical Society
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• v.32 no.1
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• pp.25-32
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• 2003

In this paper, we define the mean life process for the right censored data and show the asymptotic equivalence between two kinds of the mean life processes. We use the Kaplan-Meier and Susarla-Van Ryzin estimates as the estimates of survival function for the construction of the mean life processes. Also we show the asymptotic equivalence between two mean residual life processes as an application and finally discuss some difficulties caused by the censoring mechanism.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.110-117
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• 2018

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. By the result of degeneration, non-conforming products and malfunction of machine occur. Therefore a periodic preventive resetting the process is necessary. This type of preventive action is called 'preventive maintenance policy.' Preventive maintenance presupposes that the preventive (resetting the process) cost is smaller than the cost of failure caused by the malfunction of machine. The process mean shift problem is a field of preventive maintenance. This field deals the interrelationship between the quality cost and the process resetting cost before machine breaks down. Quality cost is the sum of the non-conforming item cost and quality loss cost. Quality loss cost is due to the deviation between the quality characteristics from the target value. Under the process mean shift, the quality cost is increasing continuously whereas the process resetting cost is constant value. The objective function is total costs per unit wear, the decision variables are the wear limit (resetting period) and the initial process mean. Comparing the previous studies, we set the process variance as an increasing concave function and set the quality loss function as Cpm+ simultaneously. In the Cpm+, loss function has different cost coefficients according to the direction of the quality characteristics from target value. A numerical example is presented.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.20-32
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• 1995

This paper is concerned with the economic selection of both the lower limit and the process mean for a continuous production process. Consider a production process where items are produced continuously. All of the items are subject to acceptance inspection. The items for which the measured values of the quality characteristic are larger than the lower limit are accepted, and those smaller than the lower limit are rejected and excluded from shipment. The process mean may be set higher to reduce the costs incurred by imperfect quality. Using a higher process mean, however, results in a higher production cost when production cost is an increasing function of the quality characteristic. Assuming that the quality characteristic is normally distributed with known variability, cost models are constructed which involve production cost, cost incurred by imperfect quality, rejection cost, and inspection cost. Methods of finding optimal values of the lower limit and the process mean are presented and numerical examples are given.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.111-116
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• 2005

This paper considers the problem of optimally choosing the sub-process means of a mixture production process where two important ingredients are mixed. The quantity of each ingredient is controlled through each corresponding sub-process. The values of the sub-process mean directly affect the defective rate, production, scrap and reprocessing costs for the mixture production process. After inspecting every incoming item, each conforming item is sold in a regular market for a fixed price and any nonconforming item is scraped. A model is constructed on the basis of the selling price, production, inspection, and scrap and reprocessing costs. The goal is to determine the optimum sub-process mean values based on maximizing expected profit function relating selling price and cost components. A method of finding the optimum sub-process means is presented when the quantities of the two ingredients are assumed to be normally distributed with known variances. A numerical example is given and numerical studies are performed.

• Korean Management Science Review
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• v.31 no.3
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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.

• 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 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.29 no.3
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• pp.79-85
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• 2001

Estimation of the process deviation is an important problem in statistical process control, especially in the control chart, process capability analysis or measurement system analysis. In this paper we suggest the use of the Gini's mean difference for the estimation of the process deviation when we design the control limits in construction of the control charts. The efficiency of the Gini's mean difference was well explained in Nam, Lee and Jung(2000). In this paper we propose an $\overline{X}$ control chart which use the control limits based on the Gini's mean difference. In various classes of distributions, the proposed control chart shows food performance.