Monitoring the coefficient of variation using a variable sample size EWMA chart

Muhammad, Anis Nabila Binti; Yeong, Wai Chung; Chong, Zhi Lin; Lim, Sok Li; Khoo, Michael B. C.

doi:10.1016/j.cie.2018.09.045

Cited by 44 publications

(34 citation statements)

References 37 publications

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“…The selection of methods was preceded by a review of the professional literature and the results of other research in this field. The use of coefficient of variation is different in academia, starting by moment characteristics [57,58] or control CV charts [59][60][61]. Its practical use was part of the research of [62,63].…”

Section: Topsis From the View Of Indicator Importancementioning

confidence: 99%

Efficiency Evaluation of Cultural Services in the Czech Republic via Multi-Criteria Decision Analysis

Vavrek

Bečica

2020

Sustainability

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Culture is an irreplaceable means of transferring information between generations. The development of culture, the cultural environment, and the effect of culture exist over the long term. The results are not shown immediately, whereas the economics of subjects of this area (including theatres) are subject to public control. Public control forces these entities to use public finances efficiently, economically, and effectively. A sample of 11 indicators showing techniques and financial efficiency was chosen to conduct a complex evaluation of the economy of Czech theatres. The importance of the indicators was set by three objective methods used in combination by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the evaluation of management. We show significant differences among the results; in our opinion, it is not possible to exactly set the best method that could be applied in general. Every method can potentially serve as a tool for complex comparison among homogeneous groups of theatres of the Czech Republic, depending on the preferences of their founder, in the public sector such as a municipality, region, or state.

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Section: Topsis From the View Of Indicator Importancementioning

confidence: 99%

Efficiency Evaluation of Cultural Services in the Czech Republic via Multi-Criteria Decision Analysis

Vavrek

Bečica

2020

Sustainability

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“…Meanwhile, the reason for setting t 1 = 0.1 is because a practitioner needs to take into consideration the fact that a minimum time period between samples is required, in order for enough units/observations to be produced by the process to meet the sample size requirement; thus, it is not practical if t 1 < 0.1 (Aparisi and Haro). Meanwhile, ATS 0 = 370.4 will ensure a low false alarm rate (Muhammad et al). The optimization procedure is employed to minimize the objective function

{Min}_{\{n_{1}, n_{2}, t_{2}, α_{1}, α_{2}, α_{1}^{'}, α_{2}^{'}\}} {ATS}_{1} ((), τ)

, subject to the constraints ATS 0 = 370.4, ASS 0 = n 0 , ASI 0 = t 0 , and

{UCL}_{0} = F_{\overset{falsê}{γ}}^{- 1} (()|,,, 1 - α_{0}, n_{0}, p, δ_{0})

, where τ > 1, and

{Min}_{\{n_{1}, n_{2}, t_{2}, α_{1}, α_{2}, α_{1}^{'}, α_{2}^{'}\}} {EATS}_{1} ((),, τ_{\min}, τ_{\max})

, subject to the constraints EATS 0 = 370.4, ASS 0 = n 0 , ASI 0 = t 0 , and

{UCL}_{0} = F_{\overset{falsê}{γ}}^{- 1} (()|,,, 1 - α_{0}, n_{0}, p, δ_{0})

, where τ max > τ min > 1.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…Meanwhile, the reason for setting t 1 = 0.1 is because a practitioner needs to take into consideration the fact that a minimum time period between samples is required, in order for enough units/observations to be produced by the process to meet the sample size requirement; thus, it is not practical if t 1 < 0.1 (Aparisi and Haro 47 ). Meanwhile, ATS 0 = 370.4 will ensure a low false alarm rate (Muhammad et al 22 ). The optimization procedure is employed to minimize the objective function (1) Min n 1 ;n 2 ;t 2 ;α 1 ;α 2 ;α 0…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…The standard CV chart is only efficient in the detection of large process shifts. Hence, numerous research works on univariate CV charts have been made over the years to improve the sensitivity of the standard CV chart in detecting small and moderate CV shifts (see, for example, Castagliola et al, Calzada and Scariano, Teoh et al, Yeong et al, Tran and Tran, Muhammad et al, to name a few). In a contrary, research works on monitoring the MCV arise.…”

Section: Introductionmentioning

confidence: 99%

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A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Chew

Khoo

Khaw

et al. 2019

Quality & Reliability Eng

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An efficient process monitoring system is important for achieving sustainable manufacturing. The control charting technique is one of the most effective techniques to monitor process quality. In certain processes where the process mean and variance are not independent of one another, the coefficient of variation (CV), which measures the ratio of the standard deviation to the mean, should be monitored. In line with industrial settings, where at least two or more variables are monitored simultaneously in most processes, this paper proposes a variable parameter (VP) chart to monitor the multivariate CV (MCV). Formulae and algorithms to optimize the various performance measures are discussed. The proposed VP MCV chart is designed based on a Markov chain approach. The performance comparison shows that the proposed VP MCV chart prevails over the existing MCV charts, in terms of the average time to signal (ATS), standard deviation of the time of signal (SDTS), and expected average time to signal (EATS) criteria. An example is presented to illustrate the implementation of the proposed VP MCV chart.

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“…In addition, the process may not become out-of-control whenever a shift in or occurs. 1 In those cases, it is important to monitor the ratio or inverse ratio of to or simply monitor the coefficient of variation (CV). The process CV, denoted by = ∕ , is the ratio of the process standard deviation to the process mean.…”

Section: Introductionmentioning

confidence: 99%

Enhanced EWMA charts for monitoring the process coefficient of variation

Haq

Bibi

Khoo

2020

Quality & Reliability Eng

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The coefficient of variation (CV) is an important quality characteristic when the process variance is a function of the process mean for a production process. In this paper, we develop an auxiliary information-based (AIB) estimator for estimating the squared CV, along with its approximated mean and variance. This estimator is then used to devise new one-sided EWMA charts for monitoring the increases or decreases in the squared CV of a normal process, named the AIB-EWMA CV charts. In addition, the sensitivities of these control charts are also enhanced with the fast initial response feature. The Monte Carlo simulation method is used to compute the run length characteristics of the proposed CV charts. Based on detailed run length comparisons, it is found that the proposed AIB-EWMA CV charts are uniformly and substantially better than the existing EWMA CV charts when detecting different kinds of upward/downward shifts in the squared CV. The proposed charts are also applied to a real dataset to support the proposed theory.

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Monitoring the coefficient of variation using a variable sample size EWMA chart

Cited by 44 publications

References 37 publications

Efficiency Evaluation of Cultural Services in the Czech Republic via Multi-Criteria Decision Analysis

Efficiency Evaluation of Cultural Services in the Czech Republic via Multi-Criteria Decision Analysis

A proposed variable parameter control chart for monitoring the multivariate coefficient of variation

Enhanced EWMA charts for monitoring the process coefficient of variation

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