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Monitoring process mean with a new EWMA control chart
Su-Fen, Yang;Wen-Chi, Tsai;Tzee-Ming, Huang;Chi-Chin, Yang;Smiley, Cheng;
Produ??o , 2011, DOI: 10.1590/S0103-65132011005000026
Abstract: in practice, sometimes the process data did not come from a known population distribution. so the commonly used shewhart variables control charts are not suitable since their performance could not be properly evaluated. in this paper, we propose a new ewma control chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. the sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. furthermore, an arcsine ewma chart is proposed since the average run lengths of the arcsine ewma chart are more reasonable than those of the new ewma chart. the arcsine ewma chart is recommended if we are concerned with the proper values of the average run length.
Modified Simple Robust Control Chart Based on Median Absolute Deviation  [cached]
Kayode Samuel Adekeye
International Journal of Statistics and Probability , 2012, DOI: 10.5539/ijsp.v1n2p91
Abstract: The control limits derived for the Median Absolute Deviation (MAD) based Standard deviation (S) control chart proposed by Abu-Shawiesh was for monitoring quality characteristics when a standard value of sigma (${sigma}$) is known or given by the management/ engineers. When sigma (${sigma}$) is unknown and we are interested in monitoring past/non-normal data, then there is the need to modify the simple robust control limits. In this paper, the control limits for the Shewhart $ar{X}$ and S control chart based on median absolute deviation were modified using the concept of three sigma (3${sigma}$) limits. An evaluation performance tool was also developed to evaluate the efficiency of the modified control chart. An algorithm implemented on S-Plus programming language was developed to compute the two evaluation parameters used in this study. The results show that the control limits interval and the average run length for the modified control charts is smaller than that of the existing control charts. Therefore, the modified control limits is more efficient than the existing control limits. It is recommended that the modified control limits be used when monitoring past/non-normal data or when there is no standard value of sigma specify by the process engineer/ management.
Process Monitoring with Multivariate -Control Chart  [PDF]
Paolo C. Cozzucoli
International Journal of Quality, Statistics, and Reliability , 2009, DOI: 10.1155/2009/707583
Abstract: We assume that the operator is interested in monitoring a multinomial process. In this case the items are classified into (
A Suppliers Monitoring System Utilizing Control Chart
Chyuan Perng,Shui-Shun Lin,Ying-Wei Lai,Jen-Teng Tsai
International Journal of Electronic Business Management , 2004,
Abstract: Because of the networking technologies progress made, and the collaboration theories developed, the relationship of suppliers and buyers is changed to a collaborative one nowadays. The performance of each member in a supply chain will affect the market competitiveness of the chain. Therefore, how to choose an appropriate collaborative partner becomes an important issue for the initiator of the supply chain. The performance quality of the supplier is the key factor of wining the competition in a market. If the performance quality of the supplier is good and stable, it can supply its downstream with sufficient quality resources and thus build competitive advantages. There are enterprises using information technology to assist the monitoring of the supply chain, from material flow to customer’s relationship. Information technologies, such as Enterprise Resource Planning (ERP), are employed to deal with the problem. Business transaction data, such as delivered quality, price, date, quantity, etc., can be saved in a database and then analyzed to form production policies. The aims of the research are to construct a framework of supplier monitoring information system and to propose a control-chart-based mechanism for monitoring the performance of suppliers in a business-to-business (B2B) supply chain environment. The research uses the delivered quality, date, and quantity as the monitoring indexes, and adopts the concept of control chart to construct an information system that monitors business performances of suppliers in a real time manner.
Monitoring a wandering mean with an np chart
Ho, Linda Lee;Costa, Antonio Fernando Branco;
Produ??o , 2011, DOI: 10.1590/S0103-65132011005000027
Abstract: this article considers the npx chart proposed by wu et al. (2009) to control the process mean, as an alternative to the use of the chart. the distinctive feature of the npx chart is that sample units are classified as first-class or second-class units according to discriminating limits. the standard np chart is a particular case of the npx chart, where the discriminating limits coincide with the specification limits and the first (second) class unit is the conforming (nonconforming) one. following the work of reynolds junior, arnold and baik (1996), we assume that the process mean wanders even in the absence of any specific assignable cause. a markov chain approach is adopted to investigate the effect of the wandering behavior of the process mean on the performance of the npx chart. in general, the npx chart requires samples twice larger (the standard np chart requires samples five or six times larger) to outperform the chart.
Enhancements for the S2 Control Chart
Michael B.C. Khoo,J.N. Sim
Matematika , 2005,
Abstract: The S2 control chart is often used in the monitoring of shifts in the process variance. The S2 chart is quick in detecting big shifts but is less sensitive to small shifts. This paper aims at increasing the sensitivity of the S2 chart in the detection of small shifts while maintaining its sensitivity towards big shifts. Numerous runs rules schemes will be considered and incorporated into the conventional S2 chart. The suggested approch involves a simple transformation of the S2 statistics which does not complicate the application of the chart by industrial practitioners and engineers.
Monitoring process mean with a new EWMA control chart Um novo gráfico de controle EWMA para monitoramento da média de processo  [cached]
Yang Su-Fen,Tsai Wen-Chi,Huang Tzee-Ming,Yang Chi-Chin
Produ??o , 2011,
Abstract: In practice, sometimes the process data did not come from a known population distribution. So the commonly used Shewhart variables control charts are not suitable since their performance could not be properly evaluated. In this paper, we propose a new EWMA Control Chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. The sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. Furthermore, an Arcsine EWMA Chart is proposed since the average run lengths of the Arcsine EWMA Chart are more reasonable than those of the new EWMA Chart. The Arcsine EWMA Chart is recommended if we are concerned with the proper values of the average run length. Na prática a distribui o de probabilidade de muitas variáveis n o é conhecida e sabe-se que n o é proveniente de uma distribui o normal. Segue que o uso dos gráficos de controle Shewhart n o é conveniente e daí há necessidade de procurar outros gráficos de controle alternativos. Neste artigo um novo gráfico de controle do tipo EWMA é proposto. Ele utiliza uma estatística n o paramétrica para monitorar a média de um processo e observou-se que é ágil para detectar pequenos desvios da média. Propriedades amostrais da estatística s o exploradas e um exemplo ilustra a nova proposta. Além disto, outro gráfico do tipo EWMA é apresentado utilizando como estatística o arco-seno da estatística n o paramétrica. Os valores de ARL’s deste gráfico apresentaram melhor desempenho do que a proposta anterior. Desta forma o gráfico Arco-Seno EWMA é recomendado se o critério do ARL for empregado.
A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution  [PDF]
Saad T. Bakir
International Journal of Quality, Statistics, and Reliability , 2012, DOI: 10.1155/2012/147520
Abstract: This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.
Método para aplica o de gráficos de controle de regress o no monitoramento de processos Method for applying regression control charts to process monitoring  [cached]
Danilo Cuzzuol Pedrini,Carla Schwengber ten Caten
Produ??o , 2011,
Abstract: Este artigo prop e um método para a aplica o do gráfico de controle de regress o no monitoramento de processos industriais. Visando facilitar a aplica o do gráfico, o método é apresentado em duas fases: análise retrospectiva (Fase I) e monitoramento do processo (Fase II), além de incluir uma modifica o do gráfico de controle de regress o múltipla, permitindo o monitoramento direto da característica de qualidade do processo ao invés do monitoramento dos resíduos padronizados do modelo. Também é proposto o gráfico de controle de extrapola o, que verifica se as variáveis de controle extrapolam o conjunto de valores utilizado para estimar o modelo de regress o. O método foi aplicado em um processo de uma indústria de borrachas. O desempenho do gráfico de controle foi avaliado pelo Número Médio de Amostras (NMA) até o sinal através do método de Monte Carlo, mostrando a eficiência do gráfico em detectar algumas modifica es nos parametros do processo. This work proposes a method for the application of regression control charts in the monitoring of manufacturing processes. The proposed method is presented in two phases: retrospective analysis (Phase I) and process monitoring (Phase II). It includes a simple modification of the multiple regression control chart, allowing the monitoring of the values of quality characteristics of the process, instead of monitoring the regression standardized residuals. It also proposes an extrapolation control chart, which verifies whether the control variables extrapolate the set of data used in regression model estimation. The proposed method was successfully applied in a rubber manufacturing process. The Average Run Length (ARL) distribution was estimated using the Monte Carlo method, proving the efficiency of the proposed chart in detecting some alterations in process parameters.
A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution  [PDF]
Saad T. Bakir
Journal of Quality and Reliability Engineering , 2012, DOI: 10.1155/2012/147520
Abstract: This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution. 1. Introduction Most traditional statistical quality control charts assume that the monitored process has a prespecified known probability distribution (usually normal for continuous measurements). Consequently, the chart properties (control limits, false alarm rate, and the in-control average run length) would be in error if the process distribution were missspecified. To remedy this, a number of distribution-free (or nonparametric) schemes that maintain the same chart properties over a class of distributions have been proposed in the literature. For an overview of nonparametric control charts, see Chakraborti et al. [1, 2]. Another problem is that traditional control charts aim at monitoring a change in one parameter (usually a location or scale) of a process distribution. Realistically, however, when a special cause influences a process, it may cause a shift in more than one parameter (location, scale, skewness, etc.) of the process distribution. To remedy this, we need control charts designed to monitor a broad rather than a one-parameter change in a process distribution. To our knowledge, Bakir [3] was first to suggest such charts based on the two-sample Kolmogorov-Smirnov and the Cramer-von Mises statistics. Zou and Tsung [4] proposed a nonparametric likelihood ratio chart for monitoring broad changes in a process
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