Control charts for simultaneously monitoring of process mean and coefficient of variation with and without auxiliary information

Noor‐ul‐Amin, Muhammad; Tariq, Saadia; Hanif, Muhammad

doi:10.1002/qre.2546

Cited by 20 publications

(11 citation statements)

References 52 publications

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“…Subsequently, numerous new studies were done on γ‐type charts. Among them are the Exponentially Weighted Moving Average (EWMA) charts, 1,10,13–17 run rules chart, 18 run sum chart, 19 side sensitive group runs chart, 20 charts with variable charting parameters, 11,21–24 charts with measurement errors, 25,26 joint monitoring of μ and γ, 27 and the Phase I performance of γ‐type charts 28 …”

Section: Introductionmentioning

confidence: 99%

A side‐sensitive synthetic coefficient of variation chart

Yeong

Lee

Lim

et al. 2021

Quality & Reliability Eng

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Control charts for monitoring the coefficient of variation (γ) are useful for processes with an inconsistent mean (μ) and a standard deviation (σ) which changes with μ, by monitoring the consistency in the ratio σ over μ. The synthetic‐γ chart is one of the charts proposed to monitor γ, and its attractiveness lie in waiting until a second point to fall outside the control limits before a decision is made. However, existing synthetic‐γ charts do not differentiate between the points falling outside the upper control limit (UCL) and lower control limit (LCL). Hence, this paper proposes a side‐sensitive synthetic‐γ chart, where successive nonconforming samples must either fall above the UCL or below the LCL. Formulae to compute the average run length (ARL), the standard deviation of the run length (SDRL) and expected average run length (EARL) are derived using the Markov chain approach, and the algorithms to obtain the optimal charting parameters are proposed. Subsequently, the optimal charting parameters, ARL, SDRL and EARL values for various numerical examples are shown. Comparisons show that the side‐sensitive synthetic‐γ chart consistently outperforms the existing synthetic‐γ chart, especially for small shifts. The proposed chart also consistently outperforms the Shewhart‐γ chart, while showing comparable or better performance than the Exponentially Weighted Moving Average (EWMA) chart for most shift sizes, except for very small shifts. Finally, this paper shows the implementation of the proposed chart on an industrial example.

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Section: Introductionmentioning

confidence: 99%

A side‐sensitive synthetic coefficient of variation chart

Yeong

Lee

Lim

et al. 2021

Quality & Reliability Eng

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“…Recently, Haq et al [22] suggested AIB adaptive Crosier CUSUM (ACC AIB ) under fixed and variable sampling intervals and AIB adaptive EWMA (AE AIB ) control charts for the monitoring of PLOS ONE process mean. For more details about adaptive EWMA, hybrid EWMA, and AIB memory control charts, see [23][24][25][26][27] and reference therein.…”

Section: : Introductionmentioning

confidence: 99%

Improved adaptive EWMA control chart for process location with applications in groundwater physicochemical parameters and glass manufacturing industry

et al. 2022

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The adaptive exponentially weighted moving average (AEWMA) control charts are the advanced form of classical memory control charts used for efficiently monitoring small-to-large shifts in the process parameters (location and/or dispersion). These AEWMA control charts estimate the unknown shifts using exponentially weighted moving average (EWMA) or cumulative sum (CUSUM) control charts statistics. The hybrid EWMA (HEWMA) control chart is preferred over classical memory control charts to detect early shifts in process parameters. So, this study presents a new auxiliary information-based (AIB) AEWMA (IAEWMAAIB) control chart for process location that estimates the unknown location shift using HEWMA statistic. The objective is to develop an unbiased location shift estimator using HEWMA statistic and then adaptively update the smoothing constant. The shift estimation using HEWMA statistic instead of EWMA or CUSUM statistics boosts the performance of the proposed IAEWMAAIB control chart. The Monte Carlo simulation technique is used to get the numerical results. Famous performance evaluation measures like average run length, extra quadratic loss, relative average run length, and performance comparison index are used to evaluate the performance of the proposed chart with existing counterparts. The comparison reveals the superiority of the proposed control chart. Finally, two real-life applications from the glass manufacturing industry and physicochemical parameters of groundwater are considered to show the proposed control chart’s implementation procedure and dominance.

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“…Recently, Muhammad et al 14 proposed a variable sample size EWMA CV chart, and Yeong et al 15 proposed the variable parameter CV monitoring chart. Noor-ul-Amin et al 16 suggested a simultaneous process monitoring process mean and CV in the presence of auxiliary and non-auxiliary data, and the Riaz and Noor-ul-Amin 17 proposed the simultaneous scheme for the process mean and CV under ranked set sampling.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of coefficient of variation with function‐based adaptive exponentially weighted moving average control chart

Arshad

Noor‐ul‐Amin

Hanif

2021

Quality & Reliability Eng

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In this paper, a new adaptive control chart is presented to study the relative process variability, which is called the coefficient of variation (CV). The CV study is the monitoring of a process with an unstable mean value over time. So, a function-based adaptive exponentially weighted moving average (FAEWMA) CV control chart is proposed to detect the infrequent changes in the process CV, named as "FAEWMA CV" control chart. The function is suggested to adapt the smoothing constant (SC) of the plotting statistic as per arising process shift size in CV. The efficiency is examined through the run-length values, which are computed through the Monte Carlo (MC) simulation method. The FAEWMA CV chart outperformed the existing AEWMA CV charts. A real-life example is studied to explain implementation details in the example section.

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Control charts for simultaneously monitoring of process mean and coefficient of variation with and without auxiliary information

Cited by 20 publications

References 52 publications

A side‐sensitive synthetic coefficient of variation chart

A side‐sensitive synthetic coefficient of variation chart

Improved adaptive EWMA control chart for process location with applications in groundwater physicochemical parameters and glass manufacturing industry

Monitoring of coefficient of variation with function‐based adaptive exponentially weighted moving average control chart

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