The Effect of Inspector Errors on the True Fraction Non-Conforming: An Industrial Experiment

Burke, Richard J.; Davis, Robert D.; Kaminsky, Frank C.; Roberts, Arthur E. P.

doi:10.1080/08982119508918802

Cited by 24 publications

(9 citation statements)

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“…When inspection errors are present, the process's observed (estimated) non-conforming fraction will differ from the true value. Burke et al 12 showed that the observed (estimated) non-conforming fraction is…”

Section: The Inspection Error and Modification Of CCC Chartmentioning

confidence: 99%

“…Using Equations (6) and (8) gives the current control limits as 60 (LCL) and 5011 (UCL). Using Equations (11) and (12) gives the proposed limits as 83 (LCL) and 6905 (UCL). The data in Table VI represent the number of items inspected to observe one non-conforming item.…”

Section: An Application Examplementioning

confidence: 99%

“…Inspection errors and their impact on control charts have been discussed in Ryan 10 and Johnson et al 11 . Burke et al 12 showed that when the process's non-conforming fraction level is estimated from the sample, the estimated value might deviate from the true value due to the presence of inspection errors. Based on the relationship between the true and observed values of the process's non-conforming fraction, Lu et al 13 computed the adjusted control limits for the CCC chart in the presence of inspection errors.…”

Section: Introductionmentioning

confidence: 99%

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Optimal control limits for CCC charts in the presence of inspection errors

Ranjan

Xie

Goh

2003

Quality & Reliability Eng

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The control chart based on cumulative count of conforming (CCC) items between the occurrence of two non-conforming ones, or the CCC chart, has been shown to be very useful for monitoring high-quality processes. However, as in the implementation of other Shewhart-type control charts, it is usually assumed that the inspection is free of error. This assumption may not be valid and this may have a significant impact on the interpretation of the control chart and the setting of control limits. This paper first investigates the effect of inspection errors and discusses the setting of control limits in such cases. Even if inspection errors are considered, the average time to alarm increases in the beginning when the process deteriorates. Since this is undesirable, the control limits in the presence of inspection errors should be set so as to maximize the average run length when the process is at the normal level. A procedure is presented for solving this problem.

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Section: The Inspection Error and Modification Of CCC Chartmentioning

confidence: 99%

Section: An Application Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal control limits for CCC charts in the presence of inspection errors

Ranjan

Xie

Goh

2003

Quality & Reliability Eng

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“…In fact, a great deal of research e!ort is still dedicated to the development of statistical procedures to estimate error probabilities in inspection by attributes. We refer to Johnson et al [9] and Burke [10] for speci"c methods and concrete examples. Consequently, a natural setting for the Taguchi on-line process monitoring strategy for attributes should take into account the possibility of inspection errors.…”

Section: Introductionmentioning

confidence: 99%

An analysis of Taguchi's on‐line quality monitoring procedure for attributes with diagnosis errors

Borges

Turnes

2001

Appl Stoch Models Bus & Ind

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SUMMARYThe on-line quality monitoring procedure for attributes proposed by Taguchi, Elsayed and Hsiang for processes in which the fraction defective shifts from 0 at some point are examined under the assumption of diagnosis errors. The renewal reward (economic) approach of Nayebpour and Woodall is used to develop a mathematical expression for the long-run expected cost per item produced and assessment of the optimum inspection interval is discussed.

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“…O problema é que a inspeção é realizada supondo que o sistema de classificação é perfeito, apesar de nem sempre podermos considerar essa hipótese como verdadeira. Burke et al (1995), Gramopadhye et al (1996), Johnson et al (1991) e Kemp & Kemp (1988) mostram e exemplificam que os erros de classificação podem comprometer seriamente o processo de avaliação da qualidade de atributos.…”

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Número ótimo de classificações independentes com erro na avaliação da conformidade de produtos

Quinino

Suyama

2002

Pesqui. Oper.

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Neste trabalho estudamos o número de ótimo de classificações independentes por unidade produzida supondo que haja dois tipos de erro: o tipo I no qual classifica-se um produto como não-conforme quando ele na realidade não é e o tipo II no qual classifica-se um produto como conforme quando na realidade é não-conforme. Desenvolvemos um modelo econômico para minimizar o custo médio total em função dos erros de classificação e do custo de cada classificação.
In this paper we study the optimal number of independent classifications on each produced unit when there are two types of classification errors: the type I error where a product is classified as non-conform when actually it is conform, and the type II error where a product is classified as conform when actually it isn't. We develop an economic model to minimize the total expected cost as a function of the classification errors and the cost of each classification

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The Effect of Inspector Errors on the True Fraction Non-Conforming: An Industrial Experiment

Cited by 24 publications

References 1 publication

Optimal control limits for CCC charts in the presence of inspection errors

Optimal control limits for CCC charts in the presence of inspection errors

An analysis of Taguchi's on‐line quality monitoring procedure for attributes with diagnosis errors

Número ótimo de classificações independentes com erro na avaliação da conformidade de produtos

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