The design of a new repetitive sampling control chart based on process capability index

Ahmad, Liaquat; Aslam, Muhammad

doi:10.1177/0142331215571120

Cited by 38 publications

(24 citation statements)

References 26 publications

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“…Under a fixed ARL 0 , the chart having the smaller ARL 1 s is considered to be superior in detecting process shifts. Many authors discussed ARL as a performance metric for the detection of process shift for a specific magnitude. To measure the overall effectiveness of the proposed control chart, extra quadratic loss (EQL) is used.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

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In this article, a new t-chart based on generalized multiple dependent state (GMDS) sampling is proposed for efficient monitoring of a process by assuming that the time between events follows the exponential distribution. The proposed t-chart has two pair of control limits and utilizes the past sample information with the current sample information. The control chart coefficients are estimated by considering different values of the in-control average run lengths. The proposed t-chart is compared with the existing chart by using the out-of-control average run length and extra quadratic loss function. The comparison reveals that the proposed charting strategy has better shift detection ability in process mean. An illustrative example is given for the practical implementation of the proposed chart.

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Section: Performance Evaluationmentioning

confidence: 99%

“…Average run length (ARL) is used to evaluate the performance of any control chart scheme . The ARL measure for the chart performance is studied by many authors including . The MDS sampling scheme has been utilized in the SPC by many authors including Ahmad et al, Aslam et al, Azam et al, Khan et al, and Knoth …”

Section: Introductionmentioning

confidence: 99%

Design of a t‐chart using generalized multiple dependent state sampling

Aslam

Raza

Ahmad

2019

Quality & Reliability Eng

Self Cite

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“…Costa and Machado (2015) studied the steady-state behavior of the synthetic and side-sensitive synthetic DS charts to detect shifts in the process mean. More recent works on the DS and repetitive sampling charts were conducted by Teoh et al (2016), Castagliola et al (2017), Saha et al (2017) and Ahmad et al (2016).…”

Section: Introductionmentioning

confidence: 99%

A combined variable sampling interval and double sampling control chart with auxiliary information for the process mean

Umar

Khoo

Saha

et al. 2019

Transactions of the Institute of Measurement and Control

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In recent years, the suitable use of auxiliary information technique in control charts has shown an improved run length performance compared to control charts that do not have this feature. This article proposes a combined variable sampling interval (VSI) and double sampling (DS) chart using the auxiliary information (AI) technique (called VSIDS-AI chart, hereafter). The plotting-statistic of the VSIDS-AI chart requires information from both the study and auxiliary variables to efficiently detect process mean shifts. The charting statistics, optimal design and performance assessment of the VSIDS-AI chart are discussed. The steady-state average time to signal (ssATS) and steady-state expected average time to signal (ssEATS) are considered as the performance measures. The ssATS and ssEATS results of the VSIDS-AI chart are compared with those of the DS AI, variable sample size and sampling interval AI, exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The results of comparison show that the VSIDS-AI chart outperforms the charts under comparison for all shift sizes, except the EWMA-AI and RS-AI charts for small shift sizes. An illustrative example is provided to demonstrate the implementation of the VSIDS-AI chart.

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“…Similarly, Alkahtani (2013) wrote of robustness of DEWMA against EWMA control charts under non-normal processes. On the other hand, Hybrid EWMA (HEWMA) is another terminology to the DEWMA (see Ahmad et al, 2016; Aslam et al, 2015; Aslam et al, 2016; Azam et al, 2014; Haq, 2012).…”

Section: Introductionmentioning

confidence: 99%

On designing a robust double exponentially weighted moving average control chart for process monitoring

Raji

Abbas

2018

Transactions of the Institute of Measurement and Control

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A double exponentially weighted moving average chart has been proven more efficient for monitoring process mean in comparison to the classical exponentially weighted moving average chart. We, in this article, made a careful investigation on how well this scheme performs with the presence of disturbances in the process under consideration. This investigation was motivated in exploring the scheme with some robust statistic, as the mean estimator performs woefully. We also evaluated the effects of parameter estimation on the phase II assuming the parameters are unknown. Adopting a 20% trimmed mean of trimeans (robust) reveals the effect of parameter estimations. We substantiated these claims by applying the scheme on a real-life data set. The findings of the study pronounced the trimean estimator to be the best of all the five estimators used, including the mean.

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The design of a new repetitive sampling control chart based on process capability index

Cited by 38 publications

References 26 publications

Design of a t‐chart using generalized multiple dependent state sampling

Design of a t‐chart using generalized multiple dependent state sampling

A combined variable sampling interval and double sampling control chart with auxiliary information for the process mean

On designing a robust double exponentially weighted moving average control chart for process monitoring

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