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Variance dispersion graphs are useful tools for evaluating various types of designs, including mixture and mixture-process designs. They allow an experimenter to see patterns of scaled prediction variances (SPV) throughout a design space. We introduce a complementary fraction of design space (FDS) plot that provides additional information on the distribution of the SPV throughout a design space. These plots display the fraction of design space where the SPV is less than or equal to specific values. The FDS plots for combined mixture-process experiments also show which of the two types of variables has more influence on the SPV. The FDS plots for mixture and mixture-process experiments are developed and then demonstrated with several examples. [ABSTRACT FROM AUTHOR]

Feedforward control is a particular form of engineering process control. If a known input variable zt can be measured and appropriate relationships built between the input variable, a compensatory variable xt, and the desired output yt, then a feedforward control system can be developed. Using feedforward control, we show under both sudden jump shifts and trends in the mean of the input and compensatory variables that the use of statistical process monitoring tools, such as the exponentially-weighted moving average and the cumulative sum (CUSUM), significantly reduces variability in both the output variable and the controller relative to the use of feedforward control alone. Copyright © 2000 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A quality control chart for monitoring a short run process during the start-up phase is presented in this article. The chart is based on the Kalman filter recursive equations being applied to a stable process where the process variance is unknown prior to the start of the production run. The run length properties of this control scheme are discussed. It is shown that for the proposed scheme the run length properties are independent of the unknown process variance and that these properties are appropriate for monitoring a stable process during start-up. An economic model for the optimal design of the control scheme is presented and illustrated with a wet etching process used in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

In the use of statistical control charts, violating the basic assumption of independent or uncorrelated data results in a chart that exhibits poor statistical performance, resulting in an increased number of false alarms. Autocorrelated data requires modifications to traditional control chart techniques. A method based on the exponentially weighted moving average that uses variable control limits is presented. Using simulation, we explore the shift detection properties of this moving centre-line technique and will show how the detection capability of the procedure can be enhanced using supplemental tracking signal tests. Guidelines and conditions for use are also presented. [ABSTRACT FROM AUTHOR]

In processes where the length of the production run is short, data to estimate the process parameters and control limits may not be available prior to the start of production, and because of the short run time, traditional methods for establishing control charts cannot be easily applied. Recently, Q charts have been proposed to address this problem. We study the average run length (ARL) of Q charts for a normally distributed variable assuming that a sustained shift occurs in the quality characteristic. It is shown that in some cases Q charts do not exhibit adequate ARL performance. Modifications that enhance the ARL properties of Q charts are presented. Some alternatives to Q charts are also discussed. For the case of a known process target two alternative methods are presented: an exponentially weighted moving average (EWMA) method and an adaptive Kalman filtering method. It is shown that both methods have better ARL performance than Q charts for that case. For the case of both process parameters unknown, an adaptive Kalman filtering method used with a tracking signal provides an ARL performance that improves as better estimates of the process mean and variance are given. A practical example illustrates the tracking signal method for the case when the process parameters are unknown. [ABSTRACT FROM AUTHOR]

This paper presents an application of the sample autocorrelation function to statistical process control where the process data are serially correlated. Two innovative control charts are illustrated: the sample autocorrelation control chart and the group autocorrelation control chart. The important feature is that these control charts will detect shifts in the autocorrelative structure as well as shifts in the mean of the process. The sample autocorrelation function is typically used to identify an appropriate ARIMA model for a time series. The sample autocorrelation function may also be used as the basis of control charts to detect process upsets. Two unique features distingush this application of the sample autocorrelation function to statistical process control. First, the sample autocorrelations are exponentially smoothed estimates. This allows the user to control the sensitivity of the sample autocorrelation control chart. Secondly, the sample autocorrelation control chart is applied to a continuous stream of data-rather than to a static set of data that has been used to fit an ARIMA model. [ABSTRACT FROM AUTHOR]

Statistical process control methods are usually applied in an environment when periodic sampling and rational subgrouping of process output is appropriate. The resulting summary statistics can be graphically displayed and analysed using either traditional Shewhart control charts or other charts such as those based on the cumulative sum. This article presents an alternative. approach, based on time series analysis of all the real-time process data. The time series approach is employed because the sequence of process observations may not be statistically independent. The autocorrelative structure in the data may be captured using an ARIMA model, and the residuals from this model are shown to be an effective input signal for a variety of statistical process control procedures. [ABSTRACT FROM AUTHOR]

This paper describes the use of Shewhart U charts for controlling software quality while the software is under development. A parallel between the software development process and a complex manufacturing process is developed. This allows the use of statistical control charts to monitor the software development process. The general approach is to let observed defect data represent a sample of the true but unknown state of the entire software project. Software quality data so obtained can be used to derive a meaningful statistical interpretation of the state of the development project. Based upon this interpretation management can take action to improve the overall software quality while in the development stages. [ABSTRACT FROM AUTHOR]

Quality is an important business strategy in the economic and technological environment of today. To achieve high product quality, it is important to take explicit account of the cost of quality, and to use this cost as another management control. A new direction for achieving a cost-effective quality management system is to design statistical process controls so as to directly incorporate quality costs. This paper discusses the major approaches to the economic design of statistical process controls, and compares several different model formulations. The practical implication of these techniques is stressed. In particular, two economic models of the &xmacr; chart are presented: a full economic model requiring a user-specified process and nine cost parameters, and a semi-economic model using five user-specified parameters. Both of these could serve as approaches to reducing the total cost of process control. Because of its simplicity in application the semi-economic model should gain greater acceptance by practitioners for the design of process control techniques. [ABSTRACT FROM AUTHOR]

Most industrial processes are characterized by a system of several variables, all of which are subject to drifts, disturbances, and assignable causes of variation. In the chemical and process industries, there are often inertial forces arising from raw material streams, reactors and tanks that introduce serial correlation over time into these variables. This autocorrelation can have a profound impact on the effectiveness of the statistical monitoring methods used for such processes. This paper reviews some of the available methodology for multivariate process monitoring and shows the effectiveness of principal components in this context. An application of the principal components approach with con-elated observation vectors is presented. The effectiveness of this procedure to indicate process upsets is discussed. [ABSTRACT FROM AUTHOR]

Adaptive sampling is an enhancement to a classical statistical process control scheme in which the time between samples from the process is varied based on the available information. Unlike the classical Shewhart control schemes, the performance of an adaptive sampling scheme for detecting a process which is off-target before the first observation is selected can differ from the steady-state performance, which is the performance of the scheme for detecting a process shift after the control scheme has been operating for some time. Often steady-state performance is the more important criterion for evaluating statistical process control schemes. Previous work has considered initial performance and steady-state performance, but the charts examined were optimized for initial performance. The development of charts optimized for steady-state performance and a comparison to previously developed schemes are the topics of this paper. Tables providing performance information on adaptive schemes are included. [ABSTRACT FROM AUTHOR]

Quality and productivity improvement has become an essential element of the overall strategic plan for most organizations. This has sparked renewed interest in statistical methods for quality improvement. This paper reviews some recent developments in statistical methodology that have application in product and process improvement, concentrating on statistical process control and design of experiments. Some directions for future research are identified. [ABSTRACT FROM AUTHOR]

An approximate cost model for a quality control procedure for two or more related variables is investigated. A method is presented to determine the optimal sample size, interval between samples, and critical region parameter for the Hotelling T² control chart. This model is a multivariate analog of several well-known models for the univariate Arithmetic mean of sample-chart. It is assumed that only one assignable cause of variation exists and the time between occurrences is exponentially distributed. Numerical results are provided in a particular bivariate case for several values of the cost coefficients. The behavior of the model to variation of the model parameters is discussed. [ABSTRACT FROM AUTHOR]

An introduction to articles published within the issue is presented on topics including the effectiveness of popular techniques for analyzing the data from unreplicated factorial designs, designed experiments in processes that are opening under closed-loop engineering process control, and the use of risk adjustment in monitoring health care outcomes.