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Statistical process control provides package of statistical tools applied for improving quality of manufacturing, production and services processes. Cumulative sum, exponentially weighted moving average (EWMA) and progressive mean charts belong to family of memory-type charts which effectively spot small and persistent shifts in the process parameter(s). EWMA chart requires normality and a proper choice of smoothing parameter to perform efficiently. Any deviation from these conditions affects its performance in terms of efficacy and robustness. For the said concerns, progressive exponentially weighted moving average (PEWMA) chart is developed to monitor the shifts in the process location. The performance of the proposed PEWMA chart is evaluated in terms of average run length and some other metrics of run length distribution. The assessment of the proposed chart has been made under standard normal, Student's t, gamma, Laplace, logistic, exponential, contaminated normal and lognormal distributions. The performance of the proposed PEWMA chart is also compared with some existing competitors including the classical EWMA, classical CUSUM, HWMA, MEC, MCE and DEWMA charts. The analysis reveals that the proposal offers a design structure which has high sensitivity to small and persistent drifts in the process mean and has advantage of robustness under non-normal scenarios. An application from substrates manufacturing process (in which flow width of the resist is the key quality characteristic) is also provided for practical implementations. [ABSTRACT FROM AUTHOR]

Multivariate autoregressive (MAR) models are an attractive choice for applications in the processes related to finance, medical, and industry. For the monitoring of such processes, control chart is the most important and widely used tool of statistical process control tool kit. Moreover, the presence of auxiliary information helps in better estimation of different process parameters. The literature on use of auxiliary variables in control charts assumes independence of observations. In practice, we may come across processes dealing with autocorrelated outcomes. In such situations, a control chart usually produces high false alarms and exhibits slow detection of shifts when the process is out-of-control. This study intends to suggest some auxiliary information-based Shewhart charts for autocorrelated univariate and bivariate AR(1) processes. The proposed structures take into account the autocorrelation structure and offer more effective designs of control charts for efficient process monitoring. The performance measures used in this study are based on run length measures such as average run length, extra quadratic loss, relative average run length and performance comparison index. A detailed performance analysis is carried out to sort out the best performing charts. In addition, we have considered an application from a manufacturing process to demonstrate the implementation of the proposed charting structures in real scenario. [ABSTRACT FROM AUTHOR]

Control charts are popular tools in statistical process control (SPC) and artificial neural network (ANN) technique is an attractive alternative for efficient monitoring of process parameters. This study uses the artificial neural network technique with back propagation method to process control system for dispersion parameter. We have trained an artificial neural network to be used in statistical control charts using varying runs rules schemes. By investigating the performance of trained artificial neural network under normal and bootstrapping environments we have made comparisons of the usual ANN and three runs rules-based schemes for ANN to gain the precision of process. We have used average run length (ARL), extra quadratic loss (EQL), relative ARL (RARL), and performance comparison index (PCI) measures and explored the said structures of trained artificial neural network under bootstrapping by implementing runs rules schemes. We have also suggested a modification in the trained ANN for variance change detection. An example with real data is also given for practical considerations. [ABSTRACT FROM AUTHOR]

In this article we develop a power computation code in the R language which provides an easy to use tool to researchers in designing Shewhart control charts. It enables researchers to use different existing and newly introduced sensitizing rules and runs rules schemes designed for Shewhart-type control charts for location and spread. The code provides researchers to compute the power for different options of r out of m rules/schemes. The code is flexible to apply for any sample size, false alarm rate, type of control limits (one- or two-sided), amount of shift in the process parameters and a variety of popular distributions for commonly used Shewhart-type control charts (i.e. $${\bar{{X}} ,R ,S}$$ and S charts). These mentioned benefits of the developed functional code are only partially found in features of the existing software packages and these programs may be enhanced by adding the features of the developed code as a function in their libraries dealing with quality control charting. [ABSTRACT FROM AUTHOR]

In many manufacturing industries, quality of an item depends on different characteristics and their relationship may be modeled by profiles. For instance, in photo-voltaic (PV) system, output voltage is dependent on the capacitance level of capacitors, used in the Z-source inverter. Control chart is a dynamic tool that works under two different phases namely Phase I and Phase II. The unknown parameters are estimated in Phase I, while Phase II focuses on monitoring of the process using estimated control limits from Phase I. In this study, we have investigated Phase I monitoring of linear profile parameters under several sampling approaches including ranked set sampling (RSS), double RSS, median RSS (MRSS), double MRSS, extreme RSS (ERSS) and double ERSS. A comparative analysis on the performance of existing and proposed schemes is performed in terms of probability to signal. The results advocate that the proposed methods under different ranked set schemes offer superior detection abilities as compared to the existing schemes for varying shifts in profile parameters (intercept, slope and error variance). Moreover, a real application related to PV system is included to show the practical demonstration of the profile monitoring (for the output voltage in relation to capacitance level) under proposed schemes. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) has its own importance in the field of quality control. In SPC, control charts are significant tools to monitor process parameters, and exponentially weighted moving average (EWMA) control chart is one such tool. It is a memory-type chart, which is used to target mainly the smaller shifts in the process parameters. Adaptive EWMA (AEWMA) scheme is used to identify small as well as large shifts. EWMA and AEWMA are based on the assumption of normality, which is quite hard to find in practice, and there are many situations where outliers are occasionally present. In the current study, we have proposed four robust adaptive EWMA schemes for monitoring process location parameter. We have investigated their performance under uncontaminated normal and contaminated normal environments. We have carried out comparisons amongst different competing charts based on average run length (ARL), standard deviation of run length (SDRL) and different percentiles of run length distribution. Two examples related to manufacturing processes are also provided for practical implementation of the proposed schemes. [ABSTRACT FROM AUTHOR]

Monitoring of process parameters via control charts is exceptionally common exercise in statistical process control (SPC). The exponentially weighted moving average (EWMA) control chart is one of an outstanding powerful tool of the SPC for monitoring of process parameters. The performance of any process can be improved by the careful monitoring and removing of assignable causes of variation. The existence of outliers may influence the performance of traditional charts in the monitoring of a process. This study is planned for the monitoring of dispersion parameter through the interquartile range (IQR). We proposed EWMA-type IQR charts based on auxiliary information for the efficient monitoring of process dispersion for the process following bivariate normal distribution. The comparison of these charts is made with usual IQR chart as well as traditional R and S charts. We evaluate the efficiency of charts by some commonly used run length measures. Both contaminated and uncontaminated processes are considered in this study to examine the robustness of charts. The results revealed that our proposed control charts exhibit superior performance in the presence of outliers. A real application of our study is also provided based on non-iso-thermal continuous stirred tank chemical reactor process. [ABSTRACT FROM AUTHOR]