• Industrial Engineering and Management Systems
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• v.8 no.2
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• pp.72-79
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• 2009

The ultimate goal of inventory management is to decide the timing and the quantity of ordering in response to uncertain demands. Recently, some researchers have focused upon an impact of distortions in the information, e.g., customer order cancellation, on an economical inventory policy. The customer order cancellation is considered a kind of distortions in demands, because a demand that is eventually cancelled is equivalent to a phony demand. Also, there are some additional distortions in the inventory information. For instance, the procurement of suppliers may include some nonconforming items as a result of imperfect production and inspection by the suppliers, and/or damage in transit. The nonconforming item should be considered a kind of distortions in the inventory information, because the nonconforming item is equivalent to a phony stock. In this article, we consider an inventory model under the situation that customers can cancel their orders and the procurement of suppliers may include some nonconforming items. Then, we introduce the customer order reservation into the inventory model for the purpose of avoiding the costly backlogs, because the customer order reservation gives retailers a period to fulfill customer's requests. We formulate a periodic review (s, S) inventory model and investigate the economical operation under the situation mentioned above. Further, through the sensitivity analysis, we show the impact of these distortions and the effect of the customer order reservation on the inventory policy.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.519-530
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• 2015

The geometric chart has proven more effective than Shewhart p or np charts to monitor the proportion nonconforming in high-quality processes. Implementing a geometric chart commonly requires the assumption that the 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 in high-quality process where the proportion of nonconforming items is very small. Thus, the error in the parameter estimation increases and may lead to deterioration in the performance of the control chart if a sample size is inadequate. We suggest adjusting the control limits in order to improve the performance when a sample size is insufficient to estimate the parameter. We propose a linear function for the adjustment constant, which is a function of the sample size, the number of nonconforming items in a sample, and the false alarm rate. We also compare the performance of the geometric charts without and with adjustment using the expected value of the average run length (ARL) and the standard deviation of the ARL (SDARL).

• 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.

• Journal of Korean Society for Quality Management
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• v.30 no.1
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• pp.74-96
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• 2002

The objectives of this study is to suggest the rectifying sampling inspection plan considering quality cost. Limiting quality level(LQL) plans(also called LTPD plans) and outgoing quality(OQ) plans are considered. The Hald's linear cost model is discussed with and without a beta prior for the distribution of the fraction of nonconforming items in a lot. It is assumed that the sampling inspection is error free. We consider the design of reliability acceptance sampling plan (RASP) for failure rate level qualification at selected confidence level. The lifetime distribution of products is assumed to be exponential. MIL-STD-690C and K C 6032 standards provide this procedures. But these procedures have some questions to apply in the field. The cost of test and confidence level(1-$\beta$ risk) are the problem between supplier and user. So, we suggest that the optimal life test sampling inspection plans using simple linear cost model considering product cost, capability of environment chamber, environmental test cost, and etc. Especially, we consider a reliability of lots that contain some nonconforming items. In this case we assumed that a nonconforming item fail after environmental life test. Finally, we develope the algorithm of the optimal sampling inspection plan based on minimum costs for rectifying inspection and RASP. And computer application programs are developed So, it is shown how the desired sampling plan can be easily found.

• Journal of Korean Institute of Industrial Engineers
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• v.11 no.2
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• pp.19-27
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• 1985

Optimal numbers of repeat inspections are obtained for a single inspector who has a fixed probability of detecting a nonconforming item on each inspection and will continue to inspect until further inspection is not warranted when comparing the expected increase of total gain with the inspection cost. It is assumed that the detection probability decreases as the number of repeat inspections increases, and that the lot to be inspected contains an unknown but Poisson distributed number of nonconforming items.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.103-122
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• 2000

Process capability indices (PCI) $C_p\;C_{pk},\;C_m,\;and\;C_{pmk}$ are widely used to evaluate the process performance. The PCI's have been evolved to consider the 'off targetness' more adequately. However, all of these indices are found to be inconsistent with the proportion of nonconforming items, in some cases. That is, the PCI for a process may result in higher value even when the proportion of defectives increases. For these reasons, we propose a new capability index, $c_{pd}$, which is consistent with the defect rate. The characteristics of the new PCI, $c_{pd}$ are investigated with respect to the existing PCI's. Some statistical properties of an estimator for $c_{pd}$ are also investigated by a Monte Carlo simulation. Sensitivity study under minor deviation from normality is also performed to show the robustness of $c_{pd}$. A good estimator for $c_{pd}$ is under study.

• 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.35 no.4
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• pp.147-158
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• 2007

Owing to the rapid growth in automated manufacturing systems, screening inspection becomes an attractive practice for removing nonconforming items, and it has been suggested that inspection will essentially become an inherent part of modem manufacturing processes. In this paper, we propose rectifying screening inspections which allow quality practitioners to use performance and surrogate variables interchangeably in real-time applications. Two screening inspections are considered; a statistically-based screening inspection to reduce the current proportion of nonconforming items to a specified AOQ(average outgoing quality) after screening, and an economically-based screening inspection where the tolerance limit is determined so that the expected total cost is minimized. It is assumed that the performance variable and the surrogate variable are jointly normally distributed. For two screening inspections, methods of finding the optimal solutions are presented and numerical examples are also given.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.279-285
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• 1998

A quality inspection procedure interchangeably using the performance and screening variables is proposed. At the start of the inspection, the performance variable is measured for all items. As soon as i consecutive items are found to be free of defects, the screening variable is measured instead of the performance variable. If an item is rejected, the inspection based on the performance variable is resumed. All nonconforming items found in the inspection are either reworked or replaced with conforming items. It is assumed that the performance variable is dichotomous, and the screening variable given the performance variable is normally distributed with known mean and variance. The average outgoing quality (AOQ) expression is derived, and the methods of finding the inspection procedure with a specified average outgoing quality limit (AOQL) are presented.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.25-35
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• 1993

Economic screening procedures using a correlated variable are proposed to assure that the proportion of conforming items is above a desired level after screening. It is assumed that the performance variable and the screening variable are jointly normally distributed. Two screening procedures are considered. In the first screening procedure, all of the items are inspected on the screening variable. If an item fails to meet the screening specifications, it is rejected and excluded from shipment without inspection of the performance variable. In the second screening procedure, the item which fails to meet the screening specifications is inspected on the performance variable. If the value of the performance variable is within specifications the item is accepted, and the item is rejected otherwise. Cost models are constructed which involve cast from an accepted nonconforming item, cost from a rejected item, and quality inspection cost. Methods of finding optimal cutoff value on a screening variable are presented and numerical examples are given.