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Statistical Process Control (statistical + process_control)
Kinds of Statistical Process Control Selected AbstractsA MASTER CLASS IN UNDERSTANDING VARIATIONS IN HEALTHCARECYTOPATHOLOGY, Issue 2006M. Mohammed That there is wide-spread variation in healthcare outcomes cannot be denied. The question is what does the variation mean and what can we do about it? Using a series of well-known case-studies, which include data from the Bristol and Shipman Inquiries, fundamental limitations of traditional methods of understanding variation will be highlighted. These methods, which include comparison with standards, league tables and statistical testing, have flaws and they offer little or no guidance on how to re-act to the variation. Fortunately, there is a theory of variation that overcomes these limitations and provides useful guidance on re-acting to variation, which was developed by Walter Shewhart in the 1920s in an industrial setting. Shewhart's theory of variation found widespread application and won him the accolade ,Father of modern quality control'. His work is central to philosophies of continual improvement. Application of Shewhart's theory of variation, also known as Statistical Process Control (SPC), to case-studies from healthcare will be demonstrated, whilst highlighting the implications and challenges for performance management/monitoring and continual improvement in the healthcare. References:, 1. M A Mohammed, KK Cheng, A Rouse, T Marshall. "Bristol, Shipman and clinical governance: Shewhart's forgotten lessons" The Lancet 2001; 357: 463,7. 2. P Adab, A Rouse, M A Mohammed, T Marshall. "Performance league tables: the NHS deserves better" British Medical Journal 2002; 324: 95,98 [source] SPC with Applications to Churn ManagementQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2004Magnus Pettersson Abstract The process of a customer replacing one provider of a service or merchandise for another is called a churn. In competitive business environments, such as telecommunications, insurance, banking, hotels and mail order, customers can easily leave one company,and they really do. Since the cost of recruiting new customers is higher than the cost of retaining them, it is crucial for companies in these trades to monitor their customer population in order to keep churn rates low. Statistical process control (SPC) methods are developed to cover the needs of monitoring industrial processes and intensive care patients. They are based on procedures where data are analysed automatically and on-line. When results indicate that the process is out of control, an alarm alerts an engineer or physician, who can take corrective action in order to get the process back under control. This paper discusses the use of SPC methods as a means to enhance precision in detecting increasing churn rates. We show that SPC methods can give market analysts a powerful tool for tracking customer movements and churn. An early warning system (EWS), based on the same ideas as used in process industries, will give foresight and a longer time to react against churn, hence providing an advantage over competitors. In the examples discussed in this paper we monitor usage in order to detect decreasing volumes that indicate churn. Data were extracted from internal databases, and analysed and reported on-line. We conclude that the potential improvement by using SPC methods in churn management is high. Copyright © 2004 John Wiley & Sons, Ltd. [source] Integrating artificial intelligence into on-line statistical process controlQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 1 2003Ruey-Shiang Guh Abstract Statistical process control (SPC) is one of the most effective tools of total quality management, the main function of which is to monitor and minimize process variations. Typically, SPC applications involve three major tasks in sequence: (1) monitoring the process, (2) diagnosing the deviated process and (3) taking corrective action. With the movement towards a computer integrated manufacturing environment, computer based applications need to be developed to implement the various SPC tasks automatically. However, the pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process. The remaining two tasks still need to be carried out by quality practitioners. This project aims to apply a hybrid artificial intelligence technique in building a real time SPC system, in which an artificial neural network based control chart monitoring sub-system and an expert system based control chart alarm interpretation sub-system are integrated for automatically implementing the SPC tasks comprehensively. This system was designed to provide the quality practitioner with three kinds of information related to the current status of the process: (1) status of the process (in-control or out-of-control). If out-of-control, an alarm will be signaled, (2) plausible causes for the out-of-control situation and (3) effective actions against the out-of-control situation. An example is provided to demonstrate that hybrid intelligence can be usefully applied for solving the problems in a real time SPC system. Copyright © 2003 John Wiley & Sons, Ltd. [source] Application of latent variable methods to process control and multivariate statistical process control in industryINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2005Theodora Kourti Abstract Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re-starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations. Copyright © 2005 John Wiley & Sons, Ltd. [source] Integration of colour and textural information in multivariate image analysis: defect detection and classification issuesJOURNAL OF CHEMOMETRICS, Issue 1-2 2007J. M. Prats-Montalbán Abstract In industrial processes, the detection and visualisation of defects and the development of efficient automated classification tools are strategic issues, especially when dealing with random colour textures (RCTs). This paper discusses the benefits of integrating colour and spatial (i.e. textural) information of digital RGB colour images in multivariate image analysis (MIA) to deal with these topics. Regarding the first one, a simple and computational cost-effective monitoring procedure based on colour-textural MIA merged with multivariate statistical process control (MSPC) ideas is outlined. Two novel computed images: T2 and RSS Images are proposed. The procedure is applied on digital RGB colour images from artificial stone plates. With respect to the second issue, when colour-textural MIA is used for image classification a lot of factors (e.g. pre-processing, modelling,,,) likely affecting the success rate in the classification (SRC) show up. This paper presents a methodology based on the combination of experimental design and logistic regression for choosing the best combination of factors to maximise the SRC of different types of images. Digital RGB colour images from ceramic tiles and orange fruits are used to illustrate the potential of the proposed methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source] Framework for regression-based missing data imputation methods in on-line MSPCJOURNAL OF CHEMOMETRICS, Issue 8 2005Francisco Arteaga Abstract Missing data are a critical issue in on-line multivariate statistical process control (MSPC). Among the different scores estimation methods for future multivariate incomplete observations from an existing principal component analysis (PCA) model, the most statistical efficient ones are those that estimate the scores for the new incomplete observation as the prediction from a regression model. We have called them regression-based methods. Several approximations have been proposed in the literature to overcome the singularity or ill-conditioning problems that some of the mentioned methods can suffer due to missing data. This is particularly acute in on-line batch process monitoring. In order to ease the comparison of the statistical performance of these methods and to improve the understanding of their relationships, in this paper we propose a framework that allows to write these regression-based methods by an unique expression, function of a key matrix. From this framework a statistical performance index (PRESV) is introduced as a way to compare the statistical efficiency of the different framework members and to predict the impact of specific missing data combinations on scores estimation without requiring real data. The results are illustrated by application to several continuous and batch industrial data sets. Copyright © 2005 John Wiley & Sons, Ltd. [source] Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of RatingsJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 1 2010M. Hafidz Omar Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation charts are introduced as techniques for ensuring quality in a measurement process for rating performance items in operational assessments. Several strengths and weaknesses of the procedures are explored with illustrative real and simulated rating data. Further research directions are also suggested. [source] Implementing a Total Quality Management Approach in the Design, Delivery, and Redesign of a Statistical Process Control CourseJOURNAL OF FOOD SCIENCE EDUCATION, Issue 3 2003L.J. Mauer ABSTRACT: The benefit of implementing total quality management(TQM)into university classrooms has been demonstrated. The objective of this work was to develop a TQM project to use as a teaching tool for TQM and statistical process control (SPC) concepts, an assessment tool for the course, and as a means for involving students in improving the course in progress. A plan-do-study-act (PDSA) assignment was developed to use SPC tools and the 4 concepts of TQM. The implementation of recommendations developed as part of the PDSA project significantly (p < 0.05) improved student performance on quizzes, student satisfaction with the course, student perception of instructor performance, as well as instructor satisfaction with the course. [source] Principal-component analysis of multiscale data for process monitoring and fault diagnosisAICHE JOURNAL, Issue 11 2004Seongkyu Yoon Abstract An approach is presented to multivariate statistical process control (MSPC) for process monitoring and fault diagnosis based on principal-component analysis (PCA) models of multiscale data. Process measurements, representing the cumulative effects of many underlying process phenomena, are decomposed by applying multiresolution analysis (MRA) by wavelet transformations. The decomposed process measurements are rearranged according to their scales, and PCA is applied to these multiscale data to capture process variable correlations occurring at different scales. Choosing an orthonormal mother wavelet allows each principal component to be a function of the process variables at only one scale level. The proposed method is discussed in the context of other multiscale approaches, and illustrated in detail using simulated data from a continuous stirred tank reactor (CSTR) system. A major contribution of the paper is to extend fault isolation methods based on contribution plots to multiscale approaches. In particular, once a fault is detected, the contributions of the variations at each scale to the fault are computed. These scale contributions can be very helpful in isolating faults that occur mainly at a single scale. For those scales having large contributions to the fault, one can further compute the variable contributions to those scales, thereby making fault diagnosis much easier. A comparison study is done through Monte Carlo simulation. The proposed method can enhance fault detection and isolation (FDI) performance when the frequency content of a fault effect is confined to a narrow-frequency band. However, when the fault frequency content is not localized, the multiscale approaches perform very comparably to the standard single-scale approaches, and offer no real advantage. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2891,2903, 2004 [source] Statistical process monitoring based on dissimilarity of process dataAICHE JOURNAL, Issue 6 2002Manabu Kano Multivariate statistical process control (MSPC) has been widely used for monitoring chemical processes with highly correlated variables. In this work, a novel statistical process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time-window size is appropriately selected. [source] The effects of model parameter deviations on the variance of a linearly filtered time seriesNAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 5 2010Daniel W. Apley Abstract We consider a general linear filtering operation on an autoregressive moving average (ARMA) time series. The variance of the filter output, which is an important quantity in many applications, is not known with certainty because it depends on the true ARMA parameters. We derive an expression for the sensitivity (i.e., the partial derivative) of the output variance with respect to deviations in the model parameters. The results provide insight into the robustness of many common statistical methods that are based on linear filtering and also yield approximate confidence intervals for the output variance. We discuss applications to time series forecasting, statistical process control, and automatic feedback control of industrial processes. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010 [source] The funnel experiment: The Markov-based SPC approachQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2007Gonen Singer Abstract The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov-based SPC, which is a simplified version of the context-based SPC method, is shown to monitor the modified process well. Copyright © 2007 John Wiley & Sons, Ltd. [source] Multivariate statistical process control charts: an overviewQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 5 2007S. Bersimis Abstract In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal. Copyright © 2006 John Wiley & Sons, Ltd. [source] Relationships Among Control Charts Used with Feedback ControlQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 8 2006George Runger Abstract Feedback control is common in modern manufacturing processes and there is a need to combine statistical process control in such systems. Typical types of assignable causes are described and fault signatures are calculated. A fault signature can be attenuated by the controller and an implicit confounding among faults of different types is discussed. Furthermore, the relationships between various control statistics are developed. Control charts have been proposed previously for deviations from target and for control adjustments. We describe why one or the other can be effective in some cases, but that neither directly incorporates the magnitude (or signature) of an assignable cause. Various disturbance models and control schemes, both optimal and non-optimal, are included in a mathematically simple model that obtains results through properties of linear filters. We provide analytical results for a widely-used model of feedback control. Copyright © 2006 John Wiley & Sons, Ltd. [source] A Hybrid SPC Method with the Chi-Square Distance Monitoring Procedure for Large-scale, Complex Process DataQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 4 2006Nong Ye Abstract Standard multivariate statistical process control (SPC) techniques, such as Hotelling's T2, cannot easily handle large-scale, complex process data and often fail to detect out-of-control anomalies for such data. We develop a computationally efficient and scalable Chi-Square () Distance Monitoring (CSDM) procedure for monitoring large-scale, complex process data to detect out-of-control anomalies, and test the performance of the CSDM procedure using various kinds of process data involving uncorrelated, correlated, auto-correlated, normally distributed, and non-normally distributed data variables. Based on advantages and disadvantages of the CSDM procedure in comparison with Hotelling's T2 for various kinds of process data, we design a hybrid SPC method with the CSDM procedure for monitoring large-scale, complex process data. Copyright © 2005 John Wiley & Sons, Ltd. [source] Integrating artificial intelligence into on-line statistical process controlQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 1 2003Ruey-Shiang Guh Abstract Statistical process control (SPC) is one of the most effective tools of total quality management, the main function of which is to monitor and minimize process variations. Typically, SPC applications involve three major tasks in sequence: (1) monitoring the process, (2) diagnosing the deviated process and (3) taking corrective action. With the movement towards a computer integrated manufacturing environment, computer based applications need to be developed to implement the various SPC tasks automatically. However, the pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process. The remaining two tasks still need to be carried out by quality practitioners. This project aims to apply a hybrid artificial intelligence technique in building a real time SPC system, in which an artificial neural network based control chart monitoring sub-system and an expert system based control chart alarm interpretation sub-system are integrated for automatically implementing the SPC tasks comprehensively. This system was designed to provide the quality practitioner with three kinds of information related to the current status of the process: (1) status of the process (in-control or out-of-control). If out-of-control, an alarm will be signaled, (2) plausible causes for the out-of-control situation and (3) effective actions against the out-of-control situation. An example is provided to demonstrate that hybrid intelligence can be usefully applied for solving the problems in a real time SPC system. Copyright © 2003 John Wiley & Sons, Ltd. [source] | |||||||||||||