Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Abbasi, Saddam Akber; Miller, Arden; Riaz, Muhammad

doi:10.1002/qre.1458

Cited by 46 publications

(40 citation statements)

References 22 publications

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“…In order to illustrate the application of under-study charts, the same example is used here as was used earlier by [42] and [18] from [19]. The lling volume of soft-drink beverage bottles is an important quality Table 9 and plotted in Figure 6.…”

Section: Illustrative Examplementioning

confidence: 99%

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Ahmad

Riaz

2017

Scientia Iranica

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Abstract. Control charts are popular tounderols for process monitoring and are mainly classi ed into memory and memory-less structures. Exponentially Weighted Moving Average control charts (EWMA) are widely used for the detection of small shifts in process parameters. The traditional design structures of these types of charts rely on the stringent assumption of normality. In practice, quality characteristics generally do not follow normality; hence, alternative charting structures are needed. Nonparametric control chart is one such alternative that do not depend on the mentioned distributional assumption(s). This study deals with nonparametric EWMA charts for an e cient detection of smaller shifts in location. We have investigated asymptotic, time-varying, and Fast Initial Response (FIR) based time varying control limits for nonparametric EWMA charts. We have considered two variants of FIR with time varying limits for nonparametric EWMA structure. A variety of run length properties have been investigated for performance evaluations. It is observed that FIR-based nonparametric EWMA charts with time-varying limits are quite sensitive to detecting smaller shifts, and they o er attractive run length properties. A real-life example is also provided to illustrate the application of the time-varying and FIR-based nonparametric EWMA control charts.

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Section: Illustrative Examplementioning

confidence: 99%

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Ahmad

Riaz

2017

Scientia Iranica

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“…The progressive mean (PM) control chart suggested by Abbas et al has earned a lot of attention from quality researchers and practitioners due to its efficient detection of shifts ability from process parameter(s). The PM chart has been applied by Abbasi et al to monitor nonparametric process target and by Zafar et al to monitor drifts in process dispersion. For monitoring Poisson process parameter, Abbasi used PM chart and PM performed efficiently to monitor the number of nonconformities per unit.…”

Section: Introductionmentioning

confidence: 99%

On designing an efficient control chart to monitor fraction nonconforming

Abbas

Nazir

Akhtar

et al. 2019

Quality & Reliability Eng

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Process control measures are mostly applied in production and manufacturing industries. The most important tool used in these disciplines is control chart. In manufacturing and production processes, when the quality characteristic of interest cannot be directly measured, it becomes essential to apply attribute control charts. To monitor fraction nonconforming of the output, quality practitioners mostly prefer p‐chart. In this article, a new progressive mean (PM) control chart is being proposed for monitoring drift in proportion of nonconforming products. The design evaluations of the proposed chart are made and compared through different properties of run length distribution, such as average run length (ARL), standard deviation of run length (SDRL), and some percentile points. The performance of the proposed chart is assessed under zero‐state and steady‐state scenarios. The proposed PM chart is compared with p‐chart, moving average (MA) chart, optimal CUSUM chart, modified exponentially weighted moving average (EWMA) chart, and runs rules p‐charts for monitoring fraction nonconforming. The proposed chart spots efficiently sustained disturbances in the process as compared with their existing counterparts. Two illustrative examples are also provided; one from real‐life application of nonconforming bearing and seal assemblies data and the other from simulated data for the implementation of PM chart.

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“…They showed an improved performance of PM based charts compared to the Shewhart, EWMA, and CUSUM charts. Moreover, SA Abbasi proposed a nonparametric control chart based on the PM statistic. The use of progressive mean statistic has not been investigated for attribute charts, so far in SPC literature.…”

Section: Introductionmentioning

confidence: 99%

Poisson progressive mean control chart

Abbasi

2017

Quality & Reliability Eng

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In real life applications, many process‐monitoring problems in statistical process control are based on attribute data resulting from quality characteristics that cannot be measured on numerical or quantitative scales. For the monitoring of such data, a new attribute control chart has been proposed in this study, namely, the Poisson progressive Mean (PPM) control chart. The performance of the PPM chart is compared with the existing charts used for the monitoring of Poisson processes such as the Shewhart c‐chart, Poisson Exponentially Weighted Moving Average chart, Poisson double Exponentially Weighted Moving Average chart and the Poisson Cumulative Sum charts. The average run length comparison indicated the superior performance of the PPM chart in terms of shift detection ability. This study will help quality practitioners to choose an efficient attribute control chart.

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Nonparametric Progressive Mean Control Chart for Monitoring Process Target

Cited by 46 publications

References 22 publications

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

On designing an efficient control chart to monitor fraction nonconforming

Poisson progressive mean control chart

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