Charts With Variable Sampling Intervals

Reynolds, Marion R.; Amin, Raid; Arnold, Jesse C.; Nachlas, Joel A.

doi:10.1080/00401706.1988.10488366

Cited by 135 publications

(130 citation statements)

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“…In this paper we consider ASSI charts, without varying the control limits. In the literature, it is common to use the term 'variable' to denote these adaptive control charts (see, for example, Reynolds et al 2 and Costa [3][4][5] ). The term adaptive is used here as in Runger and Pignatiello 6 , Prabhu et al 7 , and Tagaras 1 in order to avoid confusion with control charts for variables data.…”

Section: Adaptive Schemementioning

confidence: 99%

“…The standard approach to comparing adaptive charts with standard charts, first used by Reynolds et al 2 , consisted of matching the in-control performance measures with those of a standard chart. Then the out-of-control performance measures are calculated and the superior efficiency of the adaptive chart is thus demonstrated.…”

Section: Choice Of Adaptive Control Chart Parameter Settingsmentioning

confidence: 99%

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Design issues for adaptive control charts

Jensen

Bryce

Reynolds

2008

Quality & Reliability Eng

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Adaptive control charts allow the components of the quality-monitoring scheme to vary in order to obtain improved performance over non-adaptive control charts. Research has centered on components such as the sample size, time between samples, warning limits, and control limits and has recommended a variety of schemes, many of which are optimal in some sense. In practice, there are many other adaptive schemes that are near optimal, which will still yield considerable improvement over non-adaptive control charts. In addition, the impact of parameter estimation on adaptive control chart performance must be taken into consideration. Based on the simulation results shown here, adaptive control charts should only be used for mature processes, where a sufficient amount of Phase I data have been obtained to ensure that the estimated control limits are accurate. When evaluating control chart performance, we consider initial state performance measures for simplicity and note that the conclusions obtained here apply to steady-state performance measures. The evaluation of performance measures is easily handled by the Markov chain approach detailed in the Appendix.

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Section: Adaptive Schemementioning

confidence: 99%

Section: Choice Of Adaptive Control Chart Parameter Settingsmentioning

confidence: 99%

Design issues for adaptive control charts

Jensen

Bryce

Reynolds

2008

Quality & Reliability Eng

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“…A idéia de variar o intervalo entre retirada de amostras foi primeiro explorada com os gráficos X (REYNOLDS, et al, 1988;RUNGER & PIGNATIELLO, 1991;AMIN & MILLER, 1993;RUNGER & MONTGOMERY, 1993;BAXLEY, 1995;REYNOLDS, ARNOLD & BAIK, 1996;, mas logo se estendeu para outros gráficos (REYNOLDS, AMIN & ARNOLD, 1990;SACCUCCI, AMIN & LUCAS, 1992). Por sua vez, PRABHU, RUNGER & KEATS (1993) e COSTA, (1994 estudaram os gráficos X com o tamanho das amostras variável.…”

Section: Introductionunclassified

Gráficos de controle X para processos robustos

Costa

1998

Gest. Prod.

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Resumo O avanço tecnológico tem tornado os processos produtivos cada vez mais robustos (robustos no sentido de produzirem itens cada vez mais iguais). Neste contexto, pequenas alterações no processo podem ser críticas, devendo portanto ser eliminadas com rapidez. Tradicionalmente, são utilizados os gráficos das somas acumuladas ou das médias móveis para controlar tais processos. O objetivo deste trabalho é mostrar que os gráficos de Shewhart também podem ser utilizados para controlar processos robustos, bastando variar seus parâmetros de projeto de maneira apropriada.Palavras-chave: processos robustos, controle de processos, gráficos de Shewhart com parâmetros variáveis.

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“…Significant advances can also be seen in the literature regarding the development of new methods and strategies for SPC, such as models for auto-correlated data and multivariate processes (Costa & Castagliola, 2011;Costa & Machado, 2011;Dokouhaki & Noorossana, 2013;Franco et al, 2014;Leoni et al, 2015), control charts allowing for varying the sampling interval and the sample size (Reynolds et al, 1988;Reynolds, 1996 Yang et al, 2012;Mahadik, 2013), improvement in the performance of control charts by double sampling (Costa & Castagliola, 2011;Teoh et al, 2015;Inghilleri et al, 2015), the design of control charts that minimize operational costs (Michel & Fogliatto, 2002;Celano et al, 2011;Lupo, 2014;Franco et al, 2014), the integration of statistical process control and automatic/engineering process control, avoiding over adjustement of the process (Holmes & Mergen, 2011;Siddiqui et al, 2015); application of the Bernoulli control charts in the field of medicine (Szarka & Woodall, 2011), application of SPC to image data (Megahed et al, 2011;Wells et al, 2013), and strategies for monitoring the variability of small batches (Celano et al, 2012;Castagliola et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of 50 years of worldwide research on statistical process control

et al. 2016

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Resumo: Um crescente número de trabalhos sobre controle estatístico do processo (CEP) emergiu nos últimos cinquentaAbstract: An increasing number of papers on statistical process control (SPC) has emerged in the last fifty years, especially in the last fifteen years. This may be attributed to the increased global competitiveness generated by innovation and the continuous improvement of products and processes. In this sense, SPC has a fundamentally important role in quality and production systems. The research in this paper considers the context of technological improvement and innovation of products and processes to increase corporate competitiveness. There are several other statistical technics and tools for assisting continuous improvement and innovation of products and processes but, despite the limitations in their use in the improvement projects, there is growing concern about the use of SPC. A gap between the SPC technics taught in engineering courses and their practical applications to industrial problems is observed in empirical research; thus, it is important to understand what has been done and identify the trends in SPC research. The bibliometric study in this paper is proposed in this direction and uses the Web of Science (WoS) database. Data analysis indicates that there was a growth rate of more than 90% in the number of publications on SPC after 1990. Our results reveal the countries where these publications have come from, the authors with the highest number of papers and their networks. Main sources of publications are also identified; it is observed that the publications of SPC papers are concentrated in some of the international research journals, not necessarily those with the major high-impact factors. Furthermore, the papers are focused on industrial engineering, operations research and management science fields. The most common term found in the papers was cumulative sum control charts, but new topics have emerged and have been researched in the past ten years, such as multivariate methods for process monitoring and nonparametric methods.

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Charts With Variable Sampling Intervals

Cited by 135 publications

References 6 publications

Design issues for adaptive control charts

Design issues for adaptive control charts

Gráficos de controle X para processos robustos

A bibliometric analysis of 50 years of worldwide research on statistical process control

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