One-Sided Shewhart-type Charts for Monitoring the Coefficient of Variation in Short Production Runs

Castagliola, Philippe; Amdouni, Asma; Taleb, Hassen; Celano, Giovanni

doi:10.1080/16843703.2015.11673366

Cited by 54 publications

(39 citation statements)

References 27 publications

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“…Accordingly, the problem of control chart design for finite or short production runs has attracted more and more attention -see, e.g. Makis (2009), Li andPu (2012), Li et al (2014), Nenes et al (2014), Zhang et al (2014) and Castagliola et al (2015). In this paper, we develop a flexible economic model with regard to the production lot, suitable for both short-run and long-run productions, and we pay particular attention to the design issues associated with charts for finite production runs.…”

Section: Problem Descriptionmentioning

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: Problem Descriptionmentioning

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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“…Table 4 shows the values of the ETARL of the VSS−γ chart for n(1) = n S (left part) and n(1) = n L (right part) for I ∈ {10, 30, 50}, γ 0 ∈ {0.05, 0.2}, ASS 0 = {5, 10, 15}, for = [0.5, 1) (decreasing case, denoted as τ < 1) and = (1, 2] (increasing case, denoted as τ > 1). As far as we know, the one-sided Shewhart-type chart, SH γ , monitoring the CV in a short run context, discussed by Castagliola et al [20], is the only available alternative to the VSS−γ chart presented in this paper. As a matter of comparison, Table 5 shows the values of the ETARL of the SH γ chart for I = {10, 30, 50}, γ 0 = {0.05, 0.2}, n = {5, 10, 15} for Ω = [0.5, 1) (decreasing case, denoted as τ < 1) and Ω = (1, 2] (increasing case, denoted as τ > 1).…”

Section: Numerical Analysismentioning

confidence: 99%

“…If severalX type control charts have been proposed for short run processes, as far as we know, only Castagliola et al [20] investigated one-sided Shewhart-type charts monitoring the CV in a short run context. Shewhart-type charts are known to be effective in detecting large changes in processes but poor in detecting small ones.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the coefficient of variation using a variable sample size control chart in short production runs

Amdouni

Castagliola

Taleb

et al. 2015

Int J Adv Manuf Technol

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Monitoring the coefficient of variation (CV) is an effective approach to monitor a process when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few contributions have investigated the monitoring of the CV for short production runs. This paper proposes an adaptive Shewhart control chart implementing a variable sample size (VSS) strategy in order to monitor the coefficient of variation in a short production run context. Formulas for the truncated average run length are derived. Moreover, a comparison is performed with a Fixed Sampling Rate Shewhart chart for the CV in order to evaluate the performance of each chart in a short run context. An example illustrates the use of this chart on real data.

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“…In the fields of materials engineering and manufacturing, Castagliola et al (2015) have stated that some quality characteristics related to the physical properties of products often have a standard deviation that is proportional to their population mean. Tool cutting life and several properties of sintered materials are some typical examples.…”

Section: Introductionmentioning

confidence: 99%

Designing a multiple dependent state sampling plan based on the coefficient of variation

2016

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A multiple dependent state (MDS) sampling plan is developed based on the coefficient of variation of the quality characteristic which follows a normal distribution with unknown mean and variance. The optimal plan parameters of the proposed plan are solved by a nonlinear optimization model, which satisfies the given producer’s risk and consumer’s risk at the same time and minimizes the sample size required for inspection. The advantages of the proposed MDS sampling plan over the existing single sampling plan are discussed. Finally an example is given to illustrate the proposed plan.

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One-Sided Shewhart-type Charts for Monitoring the Coefficient of Variation in Short Production Runs

Cited by 54 publications

References 27 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

Monitoring the coefficient of variation using a variable sample size control chart in short production runs

Designing a multiple dependent state sampling plan based on the coefficient of variation

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