The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Giovanni Celano - One of the best experts on this subject based on the ideXlab platform.
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a variable sampling interval Shewhart Control Chart for monitoring the coefficient of variation in short production runs
International Journal of Production Research, 2017Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract:Monitoring the coefficient of variation (CV) allows process monitoring to be performed when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few research papers have investigated the monitoring of the CV in a short production run context. This paper investigates the design and implementation of a Variable Sampling Interval Shewhart Control Chart to monitor the coefficient of variation in a short production run context. Formulas for the truncated average time to signal are derived and a performance comparison is carried out with a Fixed Sampling Rate Shewhart Chart monitoring the CV. An example illustrates the use of this Chart on real industrial data.
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monitoring the coefficient of variation using a variable sample size Control Chart in short production runs
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract: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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monitoring the coefficient of variation using a variable sample size Control Chart
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: Philippe Castagliola, Hassen Taleb, Ali Achouri, Giovanni Celano, Stelios PsarakisAbstract:This paper proposes an adaptive Shewhart Control Chart implementing a variable sample size strategy in order to monitor the coefficient of variation. The goals of this paper are as follows: (a) to propose an easy-to-use 3-parameter logarithmic transformation for the coefficient of variation in order to handle the variable sample size aspect; (b) to derive the formulas for computing the average run length, the standard deviation run length, and the average sample size and to evaluate the performance of the proposed Chart based on these criteria; (c) to present ready-to-use tables with optimal Chart parameters minimizing the out-of-Control average run length as well as the out-of-Control average sample size; and (d) to compare this Chart with the fixed sampling rate, variable sampling interval, and synthetic Control Charts. An example illustrates the use of the variable sample size Control Chart on real data gathered from a casting process.
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A new sampling strategy for the Shewhart Control Chart monitoring a process with wandering mean
International Journal of Production Research, 2014Co-Authors: Bruno Chaves Franco, Giovanni Celano, Philippe Castagliola, Antonio Fernando Branco Costa, Marcela Aparecida Guerreiro MachadoAbstract:In many processes, such as in chemical and process industries, the observations of a quality characteristic to be monitored may be correlated, if sampling intervals are short. Correlation can be modelled by considering the process mean as a random variable wandering according to an autoregressive model and the observations from the process modelled as the mean plus a random error due to short-term variability or measurement error. The sensitivity of the Shewhart Control Chart in the detection of a special cause is negatively affected by presence of correlation among observations. To overcome this problem, a new sampling strategy, denoted as ESSI (Equally Spaced Samples Items), is proposed to implement the Shewhart Control Chart as opposed to the traditional rational subgrouping approach. The ESSI sampling strategy allows observations belonging to the same sample to be collected from the process at equally spaced time intervals between two successive inspections. A numerical analysis shows that the impleme...
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economic design of Shewhart Control Charts for monitoring autocorrelated data with skip sampling strategies
International Journal of Production Economics, 2014Co-Authors: Giovanni Celano, Philippe Castagliola, Bruno Chaves Franco, Antonio Fernando Branco CostaAbstract:Abstract On-line monitoring of process variability is strategic to achieve high standards of quality and maintain at acceptable levels the number of nonconforming items. Shewhart Control Charts are the simplest Statistical Process Control (SPC) procedure to achieve this goal. An efficient implementation of a Control Chart requires the optimal selection of its design parameters. They can be selected according to an economic-statistical objective: an expected total cost per unit of time incurred during production is minimized subject to a statistical constraint limiting the number of false alarms issued by the Control Chart. This paper investigates the economic-statistical design of Shewhart Control Charts implementing skip sampling strategies for constructing subgroups and monitoring autocorrelated AR(1) processes. Implementing skip sampling strategies within a rational subgroup reduces the negative effects of autocorrelation on the statistical performance of the Shewhart Control Chart. A wide benchmark of examples has been generated as a screening experimental design to study the process and cost factors influencing the selection of the sampling strategy. Regression models have been fitted to the results to help practitioners in the selection of the most convenient sampling strategy. Finally, a sensitivity analysis has been performed to evaluate how the parameters misspecification biases the evaluation of the optimal cost per unit of time.
Philippe Castagliola - One of the best experts on this subject based on the ideXlab platform.
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a variable sampling interval Shewhart Control Chart for monitoring the coefficient of variation in short production runs
International Journal of Production Research, 2017Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract:Monitoring the coefficient of variation (CV) allows process monitoring to be performed when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few research papers have investigated the monitoring of the CV in a short production run context. This paper investigates the design and implementation of a Variable Sampling Interval Shewhart Control Chart to monitor the coefficient of variation in a short production run context. Formulas for the truncated average time to signal are derived and a performance comparison is carried out with a Fixed Sampling Rate Shewhart Chart monitoring the CV. An example illustrates the use of this Chart on real industrial data.
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monitoring the coefficient of variation using a variable sample size Control Chart in short production runs
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract: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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monitoring the coefficient of variation using a variable sample size Control Chart
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: Philippe Castagliola, Hassen Taleb, Ali Achouri, Giovanni Celano, Stelios PsarakisAbstract:This paper proposes an adaptive Shewhart Control Chart implementing a variable sample size strategy in order to monitor the coefficient of variation. The goals of this paper are as follows: (a) to propose an easy-to-use 3-parameter logarithmic transformation for the coefficient of variation in order to handle the variable sample size aspect; (b) to derive the formulas for computing the average run length, the standard deviation run length, and the average sample size and to evaluate the performance of the proposed Chart based on these criteria; (c) to present ready-to-use tables with optimal Chart parameters minimizing the out-of-Control average run length as well as the out-of-Control average sample size; and (d) to compare this Chart with the fixed sampling rate, variable sampling interval, and synthetic Control Charts. An example illustrates the use of the variable sample size Control Chart on real data gathered from a casting process.
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A new sampling strategy for the Shewhart Control Chart monitoring a process with wandering mean
International Journal of Production Research, 2014Co-Authors: Bruno Chaves Franco, Giovanni Celano, Philippe Castagliola, Antonio Fernando Branco Costa, Marcela Aparecida Guerreiro MachadoAbstract:In many processes, such as in chemical and process industries, the observations of a quality characteristic to be monitored may be correlated, if sampling intervals are short. Correlation can be modelled by considering the process mean as a random variable wandering according to an autoregressive model and the observations from the process modelled as the mean plus a random error due to short-term variability or measurement error. The sensitivity of the Shewhart Control Chart in the detection of a special cause is negatively affected by presence of correlation among observations. To overcome this problem, a new sampling strategy, denoted as ESSI (Equally Spaced Samples Items), is proposed to implement the Shewhart Control Chart as opposed to the traditional rational subgrouping approach. The ESSI sampling strategy allows observations belonging to the same sample to be collected from the process at equally spaced time intervals between two successive inspections. A numerical analysis shows that the impleme...
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economic design of Shewhart Control Charts for monitoring autocorrelated data with skip sampling strategies
International Journal of Production Economics, 2014Co-Authors: Giovanni Celano, Philippe Castagliola, Bruno Chaves Franco, Antonio Fernando Branco CostaAbstract:Abstract On-line monitoring of process variability is strategic to achieve high standards of quality and maintain at acceptable levels the number of nonconforming items. Shewhart Control Charts are the simplest Statistical Process Control (SPC) procedure to achieve this goal. An efficient implementation of a Control Chart requires the optimal selection of its design parameters. They can be selected according to an economic-statistical objective: an expected total cost per unit of time incurred during production is minimized subject to a statistical constraint limiting the number of false alarms issued by the Control Chart. This paper investigates the economic-statistical design of Shewhart Control Charts implementing skip sampling strategies for constructing subgroups and monitoring autocorrelated AR(1) processes. Implementing skip sampling strategies within a rational subgroup reduces the negative effects of autocorrelation on the statistical performance of the Shewhart Control Chart. A wide benchmark of examples has been generated as a screening experimental design to study the process and cost factors influencing the selection of the sampling strategy. Regression models have been fitted to the results to help practitioners in the selection of the most convenient sampling strategy. Finally, a sensitivity analysis has been performed to evaluate how the parameters misspecification biases the evaluation of the optimal cost per unit of time.
Ronald J. M. M. Does - One of the best experts on this subject based on the ideXlab platform.
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quality quandaries how to set up a robust Shewhart Control Chart for dispersion
Quality Engineering, 2014Co-Authors: Hafiz Zafar Nazir, Muhammad Riaz, Marit Schoonhoven, Ronald J. M. M. DoesAbstract:In this article, the authors have outlined procedures to construct Phase I and Phase II standard deviation Control Charts.
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Quality Quandaries: Interpretation of Signals from Runs Rules in Shewhart Control Charts
Quality Engineering, 2010Co-Authors: A. Trip, Ronald J. M. M. DoesAbstract:The Shewhart Control Chart, combined with appropriate runs rules, is a common form of statistical process Control. Runs rules aid troubleshooting, giving information about the state of the process when it goes out of Control. Proper use of a Shewhart co..
Hassen Taleb - One of the best experts on this subject based on the ideXlab platform.
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a variable sampling interval Shewhart Control Chart for monitoring the coefficient of variation in short production runs
International Journal of Production Research, 2017Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract:Monitoring the coefficient of variation (CV) allows process monitoring to be performed when both the process mean and the standard deviation are not constant but, nevertheless, proportional. Until now, few research papers have investigated the monitoring of the CV in a short production run context. This paper investigates the design and implementation of a Variable Sampling Interval Shewhart Control Chart to monitor the coefficient of variation in a short production run context. Formulas for the truncated average time to signal are derived and a performance comparison is carried out with a Fixed Sampling Rate Shewhart Chart monitoring the CV. An example illustrates the use of this Chart on real industrial data.
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monitoring the coefficient of variation using a variable sample size Control Chart in short production runs
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: Asma Amdouni, Hassen Taleb, Philippe Castagliola, Giovanni CelanoAbstract: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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monitoring the coefficient of variation using a variable sample size Control Chart
The International Journal of Advanced Manufacturing Technology, 2015Co-Authors: Philippe Castagliola, Hassen Taleb, Ali Achouri, Giovanni Celano, Stelios PsarakisAbstract:This paper proposes an adaptive Shewhart Control Chart implementing a variable sample size strategy in order to monitor the coefficient of variation. The goals of this paper are as follows: (a) to propose an easy-to-use 3-parameter logarithmic transformation for the coefficient of variation in order to handle the variable sample size aspect; (b) to derive the formulas for computing the average run length, the standard deviation run length, and the average sample size and to evaluate the performance of the proposed Chart based on these criteria; (c) to present ready-to-use tables with optimal Chart parameters minimizing the out-of-Control average run length as well as the out-of-Control average sample size; and (d) to compare this Chart with the fixed sampling rate, variable sampling interval, and synthetic Control Charts. An example illustrates the use of the variable sample size Control Chart on real data gathered from a casting process.
Muhammad Riaz - One of the best experts on this subject based on the ideXlab platform.
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quality quandaries how to set up a robust Shewhart Control Chart for dispersion
Quality Engineering, 2014Co-Authors: Hafiz Zafar Nazir, Muhammad Riaz, Marit Schoonhoven, Ronald J. M. M. DoesAbstract:In this article, the authors have outlined procedures to construct Phase I and Phase II standard deviation Control Charts.
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Monitoring process variability using auxiliary information
Computational Statistics, 2008Co-Authors: Muhammad RiazAbstract:In this study a Shewhart type Control Chart namely V _ r Chart is proposed for improved monitoring of process variability (targeting large shifts) of a quality characteristic of interest Y . The proposed Control Chart is based on regression type estimator of variance using a single auxiliary variable X . It is assumed that ( Y, X ) follow a bivariate normal distribution. The design structure of V _ r Chart is developed and its comparison is made with the well-known Shewhart Control Chart namely S ^2 Chart used for the same purpose. Using power curves as a performance measure it is observed that V _ r Chart outperforms the S ^2 Chart for detecting moderate to large shifts, which is main target of Shewhart type Control Charts, in process variability under certain conditions on ρ_ yx . These efficiency conditions on ρ_ yx are also obtained for V _ r Chart in this study.
