An Application of Confirmation Sample Control Chart in the High-Yield Processes and Its Economic Design

Ohta, Hiroshi; Kusukawa, Etsuko

doi:10.1007/978-3-7908-2674-6_7

Cited by 5 publications

(8 citation statements)

References 4 publications

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“…Ohta and Kusukawa developed a confirmation sample CCC‐ r chart when interval sampling is used. If a CCC‐ r value Z is outside of the control limits, then an additional sample is drawn to obtain a second CCC‐ r value.…”

Section: Control Charts Based On the Negative Binomial Distributionmentioning

confidence: 99%

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A Review and perspective on surveillance of Bernoulli processes

Szarka

Woodall

2011

Quality & Reliability Eng

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Bernoulli processes have been monitored using a wide variety of techniques in statistical process control. The data consist of information on successive items classified as conforming (nondefective) or nonconforming (defective). In some cases, the probability of obtaining a nonconforming item is very small; this is known as a high quality process. This area of statistical process control is also applied to health-related monitoring, where the incidence rate of a medical problem such as a congenital malformation is of interest. In these applications, standard Shewhart control charts based on the binomial distribution are no longer useful. In our expository paper, we review the methods implemented for these scenarios and present ideas for future work in this area. We offer advice to practitioners and present a comprehensive literature review for researchers.

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Section: Control Charts Based On the Negative Binomial Distributionmentioning

confidence: 99%

“…The results were applied for r = 2 and 5. Kotani et al 69 compared the EWMA CCC-r chart with the confirmation sample CCC-r chart developed by Ohta and Kusukawa 49 . The EWMA chart was found to be superior on the basis of zero-state ARL performance for several simulation studies.…”

Section: Applications Of Exponentially Weighted Moving Average Chartsmentioning

confidence: 99%

A Review and perspective on surveillance of Bernoulli processes

Szarka

Woodall

2011

Quality & Reliability Eng

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“…The ARL values for this chart were derived using a Markov chain approach. They compared the EWMA CCC‐ r chart with the confirmation sample CCC‐ r chart developed by Ohta and Kusukawa . The EWMA chart was found to be superior on the basis of zero‐state ARL performance for several simulation studies.…”

Section: Exponentially Weighted Moving Average Control Chartsmentioning

confidence: 99%

Control Charts for Dispersed Count Data: An Overview

Saghir

Lin

2014

Qual. Reliab. Engng. Int.

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Control charts based on the Poisson distribution are commonly used to monitor count data in attributes. However, the Poisson distribution is based on the underlying equidispersion assumption that is limiting as discussed by different researchers in the literature. Therefore, a generalized control chart is required that can be used to monitor both overdispersed and underdispersed count data. This article reviews the methods to implement for dispersed count data and present ideas for future work in this area. A comprehensive literature review for researchers and practitioners is presented in this article. Copyright © 2014 John Wiley & Sons, Ltd.

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“…is more efficient than the Gan control chart in minimizing the ARL values for the shifted process. For more details reader may refer to Sun and Zhang, Chan et al ., Somerville et al ., Kotani et al ., Ohta and Kusukawa, Testik et al . and Ahmad et al…”

Section: Introductionmentioning

confidence: 99%

A Control Chart for COM-Poisson Distribution Using Resampling and Exponentially Weighted Moving Average

Aslam

Azam

2015

Qual. Reliab. Engng. Int.

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In this paper, we propose a control chart using exponentially weighted moving average (EWMA) statistic for count data based on the Conway-Maxwell-Poisson (called COM-Poisson) distribution. Repetitive sampling is considered by constructing two pairs of control limits for the proposed control chart. The performance of the proposed control chart is evaluated using the average run length (ARL) for various values of specified parameters. It has been observed that the proposed control chart is more efficient in terms of ARLs as compared to the existing control charts. The tables are provided and explained with the help of example.

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An Application of Confirmation Sample Control Chart in the High-Yield Processes and Its Economic Design

Cited by 5 publications

References 4 publications

A Review and perspective on surveillance of Bernoulli processes

A Review and perspective on surveillance of Bernoulli processes

Control Charts for Dispersed Count Data: An Overview

A Control Chart for COM-Poisson Distribution Using Resampling and Exponentially Weighted Moving Average

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