Bayesian estimation of the rate at which a process, monitored by an<i>X̄</i>chart, goes out of control

Silver, Edward A.; Bischak, Diane P.

doi:10.1080/00207540310001614141

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Some works have been devoted to this issue, e.g. Bischak and Silver (2001), and Silver and Bischak (2004). The Bayesian approach by Silver and Bischak (2004) can be adapted for estimating the occurrence rate p that requires less data resource.…”

Section: Model Development 31 Probabilistic Modelmentioning

confidence: 99%

Economic modelling for statistical process control subject to a general quality deterioration

Li¹,

Xie

2015

International Journal of Production Research

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The applications of control chart have traditionally focused on the detection of step shifts in process mean. However, changes are usually gradual, not as perfect step shifts. The common consideration of a shift as a step function does not always adequately describe what actually happens in practice. Hence, there is a need for more realistic assumptions to be incorporated. This paper employs a Markov chain approach and provides a way to quantitatively measure the economic performance of control charts in the presence of a more general quality deterioration mechanism. The finite production run is considered in the model as it has become a very important production mode at present and the process failure mechanism is described by geometric distribution. The chart properties, particularly on the issues of the quality deterioration mechanism, are investigated. The findings provide critical insights on the use of step shift assumption when designing control charts.

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Section: Model Development 31 Probabilistic Modelmentioning

confidence: 99%

Economic modelling for statistical process control subject to a general quality deterioration

Li¹,

Xie

2015

International Journal of Production Research

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“…Lin and Wang (2012) employed Bayesian framework for statistical process control under the lack of accurate readings. Silver and Bischak (2004) used Bayesian technique in statistical process control to estimate the unknown distributional parameters for occurrence of assignable causes. Bouaziz, Zamaï, and Duvivier (2013) proposed a Bayesian model to predict the equipment health factor to improve risk assessment.…”

Section: Fuzzy Multi-attribute Utilitymentioning

confidence: 99%

Focused factories: a Bayesian framework for estimating non-product related investment

Wang

Chinnam

Doğan

et al. 2014

International Journal of Production Research

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Focused factories are one of the new manufacturing trends for automotive suppliers. A key requirement for these suppliers is the ability to accurately estimate both product and non-product related investment in these facilities to quote profitable business. We propose a systematic Bayesian framework to estimate non-product related investment in focused factories. Our approach incorporates uncertainty in the activity-based costing method and applies Monte Carlo simulation process to generate distributions of investment for the cost centres, and for the different project phases in setting up a facility. A Bayesian-updating procedure is introduced to improve parameter estimations as new information becomes available with experience in setting up these facilities. Our approach is deployed at a leading global automotive tier-one supplier, Visteon Corporation. The efficacy of the Visteon-focused factory cost model is validated using subject matter experts as well as by comparing the model results with estimates from the typical current process.

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“…This assumption of identical distribution may be hard to meet in practice. Silver and Bischak proposed a control chart where the distribution of an assignable cause is assumed to be exponential with unknown parameter, not necessarily the same of other causes. They considered the Bayesian approach to deal with random causes of failure.…”

Section: Bayesian Control Chartsmentioning

confidence: 99%

An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

Quality & Reliability Eng

115

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Major difficulties in the study of high‐quality processes with traditional process monitoring techniques are a high false alarm rate and a negative lower control limit. The purpose of time‐between‐events control charts is to overcome existing problems in the high‐quality process monitoring setup. Time‐between‐events charts detect an out‐of‐control situation without great loss of sensitivity as compared with existing charts. High‐quality control charts gained much attention over the last decade because of the technological revolution. This article is dedicated to providing an overview of recent research and presenting it in a unifying framework. To summarize results and draw a precise conclusion from the statistical point of view, cross‐tabulations are also given in this article. Copyright © 2016 John Wiley & Sons, Ltd.

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Bayesian estimation of the rate at which a process, monitored by anX̄chart, goes out of control

Cited by 5 publications

References 24 publications

Economic modelling for statistical process control subject to a general quality deterioration

Economic modelling for statistical process control subject to a general quality deterioration

Focused factories: a Bayesian framework for estimating non-product related investment

An Overview of Control Charts for High‐quality Processes

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