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The special causes of variations, which is also known as a shift, can occur in a single or more than one related process characteristics. Statistical process control tools such as control charts are useful to monitor shifts in the process parameters (location and/or dispersion). In real-life situation, the shift is emerging in different sizes, and it is hard to identify it with classical control charts. Moreover, more than one process of characteristics required special attention because they must monitor jointly due to the association among them. This study offers two adaptive control charts to monitor the different sizes of a shift in the process mean vector. The novelty behind this study is to use dimensionally reduction techniques such as principal component analysis (PCA) and an adaptive method such as Huber and Bi-square functions. In brief, the multivariate cumulative sum control chart based on PCA is designed, and its plotting statistic is utilized as an input in the classical exponentially weighted moving average (EWMA) control chart. The run length (RL) properties of the proposed and other control charts are obtained by designing algorithms in MATLAB through a Monte Carlo simulation. For a single shift, the performance of the control charts is assessed through an average of RL, standard deviation of RL, and standard error of RL. Likewise, overall performance measures such as extra quadratic loss, relative ARL, and the performance comparison index are also used. The comparison reveals the superiority over other control charts. Furthermore, to emphasize the application process and benefits of the proposed control charts, a real-life example of the wind turbine process is included. [ABSTRACT FROM AUTHOR]

Abbas1 showed mathematically that progressive mean statistic can be viewed as exponentially weighted moving average statistic with varying sensitivity parameter. A recently published study claims to invalidate the result using the justification that the variance of progressive mean (PM) statistic is different from the variance of exponentially weighted moving average statistic. This study reiterates and extends the initial result established by Abbas1 and proves mathematically that not only the statistics but also the sampling variance of PM becomes a special case of exponentially weighted moving average with varying sensitivity parameter. Moreover, the study discusses that the performance of PM chart declines for steady state process and how it can be recovered by combining it with Shewhart chart. [ABSTRACT FROM AUTHOR]

The effect of parameter estimation emanating from the retrospective stage on the monitoring stage of control charts cannot be overemphasized. These effects are born of but are not limited to the practitioner‐to‐practitioner variations in the amount and type of samples employed to estimate the process parameters. Another major factor to this effect is outlying errors in phase‐I data. This study evaluates the performance of the exponentially weighted moving average (EWMA) control chart, based on outlying values and practitioner‐to‐practitioner's variation in the phase‐I preliminary samples. Furthermore, the study proposes a sensitive EWMA control chart with Tukey's and median absolute deviation (MAD) outlier detectors. We study the proposed EWMA chart's estimation effect based on the outlier detector models compared to the default EWMA chart through the Monte‐Carlo simulation approach. By studying the run length properties of the proposed schemes, the study's findings prove that the outlier detectors‐based models are more stable in the presence of outliers and require less observation in the retrospective stage. The study concludes by implementing the results on a real‐life dataset extracted from the semiconductor manufacturing industry. [ABSTRACT FROM AUTHOR]

The process monitoring techniques play an essential role to improve the overall performance of processes. The control chart is an essential monitoring tool used to detect changes in the process parameters. The Shewhart charts are famous for detecting larger shifts, while exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are famous for detecting small‐to‐moderate shifts. The separate control schemes are required for the quick identification of the changes in the process parameters because sometimes testing is too expensive or time taking and a practitioner may not afford any kind of defects or loss. With this motivation, the dynamic feature of this article is to introduce an efficient sequential probability ratio test (SPRT) decision‐based Tukey CUSUM design. The performance of the proposal is judged by using several run lengths (RLs) performance measures such as average, median, standard deviation, and percentile RLs. Based on the comparative analysis, it is revealed that the proposed chart offers more sensitivity towards the changes in process location than its competitor's charts for several probability models. The study proposal may find applications in packaging, manufacturing, decision‐making, finance and economics modeling, image processing and automation. A case study from steel rods manufacturing is included to demonstrate the application of the proposed design. [ABSTRACT FROM AUTHOR]

Existing multivariate cumulative sum (MCUSUM) control charts involve entire associated variables of a process to monitor variations in the mean vector. In this study, we have offered MCUSUM control charts with principal component method (PCM). The proposed MCUSUM control charts with PCM capture the whole process variations using fewer latent variables (principal components) while preserving as much data variability as possible. To show the significance of proposed MCUSUM control charts with PCM, various performance measures are considered including average run length, extra quadratic loss, relative average run length, and performance comparison index. Furthermore, performance measures are calculated through advanced Monte Carlo simulation method to explore the behavior of proposed MCUSUM control charts and to conduct comparative analysis with existing models. Results revealed that proposed MCUSUM control charts with PCM are efficient to detect variations timely by involving smaller number of principal components instead of considering entire associated variables. Also, proposed MCUSUM control charts have the ability to accommodate the features of existing control charts, which are illustrated as the special cases. Besides, to highlight the implementation mechanism and advantages of proposed MCUSUM control charts with PCM, a real‐life example from wind turbine process is included. [ABSTRACT FROM AUTHOR]

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]

Control chart is a well‐known tool for monitoring the performance of an ongoing process. The variability of a process is an important parameter that may deteriorate the process performance if it is not taken care on time. In this study, we have proposed some new auxiliary information‐based exponentially weighted moving average (EWMA) charts for improved monitoring of process variability. We employed auxiliary information in some useful forms including ratio, regression, power ratio, ratio exponential, ratio regression, power ratio regression, and ratio exponential regression estimators. The performance of the newly developed charts is evaluated and compared with some existing charts (viz., the NEWMA, the Improved R, the Synthetic R, and the classical R charts), using some useful measures such as average run length (ARL), extra quadratic loss, and relative ARL. The comparative analysis revealed that the proposed charts outperform their counterparts, especially when there is a strong relationship between the study and the auxiliary variables. Finally, an illustrative example is provided for the monitoring of air quality data. [ABSTRACT FROM AUTHOR]

The memory‐type control charts are quite efficient in monitoring small and moderate shifts in process parameters. These charts, such as exponentially weighted moving average (EWMA), face the problem of inertia due to the time dependence of statistics. This issue could be solved to some extent using a break at regular/irregular intervals in plotting statistics. We have proposed this method by introducing a break feature at regular intervals, to handle the inertia problem, and found it quite useful for memory control charts. A comparative analysis of the proposal with some existing charts is also part of the study. [ABSTRACT FROM AUTHOR]

Statistical quality control is an integral part of modern manufacturing, and control charts are widely used to improve and control the performance of a process. However, much of the work related to process monitoring has been done using frequentist approaches, whereas the Bayesian methodology has a practical advantage in process monitoring; that is, it can be used with a small phase-I dataset. In this article, we considered different symmetric and asymmetric loss functions for designing Bayesian control charts and noticed that the performance of the Bayesian charts is biased, i.e., in-control performance of the charts is not equal to the desired value for some loss functions and comparison among different Bayesian charts can be misleading. Therefore, to get the desired in-control performance under different loss functions, we propose a corrected design of the Bayesian control charts. In addition to posterior charts, we also discuss a predictive control chart to monitor the future observations in this study. Besides a simulation study, we also present two real data examples: the first example is to monitor the surface roughness of reamed holes in a particular metal part while the second is about monitoring the measurements of a diameter. [ABSTRACT FROM AUTHOR]

Control charts are often used for the monitoring of quality characteristics of interest. There may exist some other characteristics that are associated with the main quality characteristic. A model that quantifies the relationship between them is termed as a profile, for instance, the relation between charge and capacitance. The monitoring of the main variable that is linearly related with an associated explanatory variable is termed as simple linear profiles' monitoring. It is a common practice to use simple random sampling (SRS) for profile monitoring. This study intends to enhance profile monitoring by considering modified successive sampling (MSS) approach. The performance of the existing and the proposed schemes are evaluated using the well‐known metrics average run length. The comparative analysis revealed that the proposed structure outperforms the existing ones in terms of efficiency. A real application from electrical engineering is used to show the implementation of our proposal in practice. [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]

Using traditional control charts to monitor autocorrelated processes is not beneficial, because it will lead us to misleading detections in the processes. One of the methods used to deal with the control charts for autocorrelated process is the model-based approach. It uses an adequate time series model that fits the process and uses the residuals as monitoring statistics. For the said purpose, it is important to pick a suitable model that can adequately be used for different designs of control charts under specific time series model. This study intends to do the same for three popular types of charts namely Shewhart, exponentially weighted moving average, and cumulative sum. The models covered in this study include AR(1), MA(1), and ARMA(1,1) as the potential models to fit the process of interest. We have focused on two performance aspects namely efficiency and robustness. Average run length is used as a performance measure for different in-control and out-of-control states of the autocorrelated processes under varying levels of autocorrelation. An application example based on a real data set is also included in the study to highlight the importance of the study proposals. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A single control chart is very famous to control assignable causes that shift the process because of variations in parameters (e.g., location and dispersion). Simultaneous monitoring of processes is another popular approach used for the bilateral processes. In this study, we have proposed the mixed control charts for simultaneously monitoring of process location and dispersion parameters. We have used the idea of mixed exponential weighted moving average and cumulative sum charts and designed the charting structures for simultaneous monitoring. The proposals are compared with several existing counterparts. The comparisons reveal numerous advantages of the proposed charts over the other existing scheme. The practical application of the proposed charts is also highlighted using an illustrative example based on a real dataset. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical situations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed scheme is compared with its recent existing counterparts, namely the nonparametric EWMA and the nonparametric CUSUM schemes. The performance mea- sures used are the average run length (ARL), standard deviation of the run length (SDRL) and extra quadratic loss (EQL). We observed that the pro- posed chart outperforms the said existing schemes to detect shifts in the process mean level. We also provide an illustrative example for practical considerations. [ABSTRACT FROM AUTHOR]

Control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger disturbances in the process parameters, whereas cumulative sum (CUSUM) and exponential weighted moving average (EWMA) are meant for smaller and moderate changes. In this study, we enhanced mixed EWMA–CUSUM control charts with varying fast initial response (FIR) features and also with a runs rule of two out of three successive points that fall above the upper control limit. We investigate their run-length properties. The proposed control charting schemes are compared with the existing counterparts including classical CUSUM, classical EWMA, FIR CUSUM, FIR EWMA, mixed EWMA–CUSUM, 2/3 modified EWMA, and 2/3 CUSUM control charting schemes. A case study is presented for practical considerations using a real data set. [ABSTRACT FROM PUBLISHER]

Shewhart control charts are very popular in a variety of disciplines such as industry, agriculture and medical science. The design structure of the usual Shewhart charts depends on normality and one point decision rule. This makes the scope of these charts quite limited and not very efficient for small shifts. This study comes up with an intermediate solution by implementing runs rules schemes and adjusting the limits' coefficients for non-normality using the idea of skewness correction. We have covered some commonly used location and dispersion charts, namely [ABSTRACT FROM AUTHOR]

In this study, a new idea has been proposed in statistical process control by making dual use of auxiliary information, that is, for ranking as well as at the estimation stage. The control charts are proposed based on regression estimator in three basic ranked set schemes, that is, ranked-set sampling (RSS), median RSS, and extreme RSS. The power of detection is used as a performance measure for evaluation and comparison of control charts. Comparisons revealed that the newly proposed charts (making dual use of auxiliary information) performed significantly better compared with the existing location charts. An illustrative example is also provided for the application of the proposed charts. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Process monitoring through control charts is a quite popular practice in statistical process control. This study is planned for monitoring the process dispersion parameter using exponentially weighted moving average (EWMA) control chart scheme. Most of the EWMA dispersion charts that have been proposed are based on the assumption that the parent distribution of the quality characteristic is normal, which is not always the case. In this study, we develop new EWMA charts based on a wide range of dispersion estimates for processes following normal and non-normal parent distributions. The performance of all the charts is evaluated and compared using run length characteristics (such as the average run length). Extra quadratic loss, relative average run length, and performance comparison index measures are also used to examine the overall effectiveness of the EWMA dispersion charts. [ABSTRACT FROM AUTHOR]

Control charts are usually implemented in two phases: the retrospective phase (phase I) and the monitoring phase (phase II). The performance of any phase II control chart structure depends on the preciseness of the control limits obtained from the phase I analysis. In statistical process control, the performance of phase I dispersion charts has mainly been investigated for normal or contaminated normal distributions of the quality characteristic of interest. Little work has been carried out to investigate the performance of a wide range of possible phase I dispersion charts for processes following non-normal distributions. The current study deals with the proper choice of a control chart for the evaluation of process dispersion in phase I. We have analyzed the performance of a wide range of dispersion control charts, including two distribution-free structures. The performance of the control charts is evaluated in terms of probability to signal, under normal and non-normal process setups. These results will be useful for quality control practitioners in their selection of a phase I control chart. [ABSTRACT FROM AUTHOR]

This study presents a relationship between two well-known distributions, namely, geometric and binomial distributions. We will provide the statement of our result supported by its precise proof. In addition, we will give an illustrative example to explain the suggested relationship. We will indicate some industrial applications where the derived communicative property may be used. Moreover, the use of this linkage property will be shown in control charting setup. [ABSTRACT FROM AUTHOR]

A careful monitoring of process dispersion is necessary to get optimum output from any process. Control charts are very important process monitoring tools of Statistical Process Control toolkit. Interquartile range ( IQR) is a famous dispersion measure that is used to monitor process dispersion in quality control literature. In this study, a set of auxiliary information-based Shewhart-type IQR charts are proposed for efficient monitoring of process dispersion under bivariate normal, t and gamma distributed processes. The control structures of the proposed charts are developed, and their performances are evaluated in terms of Average Time to Signal ( ATS), Average Extra Quadratic Loss and Average Ratio to ATS. Comparisons are made among different charts to establish their superiorities for a quicker detection of process changes. An illustrative example is also provided to elaborate the procedural details of the proposed IQR charts using a real data set. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) is an important application of statistics in which the outputs of production processes are monitored. Control charts are an important tool of SPC. A very popular category is the Shewhart's-chart used to monitor the mean of a process characteristic. Two alternatives to the Shewhart's-chart are the cumulative sum and exponentially weighted moving average (EWMA) charts which are designed to detect moderate and small shifts in the process mean. Targeting on small and moderate shifts in the process mean, we propose an EWMA-type control chart which utilizes a single auxiliary variable. The regression estimation technique for the mean is used in defining the control structure of the proposed chart. It is shown that the proposed chart is performing better than its univariate and bivariate competitors which are also designed for detecting small shifts. [ABSTRACT FROM PUBLISHER]

Control charts act as the most effective statistical process control (SPC) tools for the monitoring of manufacturing processes. In this study, we propose and investigate a set Shewhart-type variability control chart based on the utilization of auxiliary information for efficient phase II process monitoring. The design parameters of the proposals are derived under correlated setups for the monitoring of variability parameter. The properties of these charting structures are evaluated in terms of average run length and some other related measures. The performance abilities of these charts are compared with each other and also with some existing counterparts. The comparisons revealed that the proposed charts are very efficient at detecting shifts in the variability parameter and have the ability to perform better than the competing charts in terms of run length characteristics. We have also used real datasets to illustrate the application of the proposed structures in practical situations. [ABSTRACT FROM AUTHOR]

The control chart is a very popular tool of statistical process control. It is used to determine the existence of special cause variation to remove it so that the process may be brought in statistical control. Shewhart-type control charts are sensitive for large disturbances in the process, whereas cumulative sum (CUSUM)-type and exponentially weighted moving average (EWMA)-type control charts are intended to spot small and moderate disturbances. In this article, we proposed a mixed EWMA-CUSUM control chart for detecting a shift in the process mean and evaluated its average run lengths. Comparisons of the proposed control chart were made with some representative control charts including the classical CUSUM, classical EWMA, fast initial response CUSUM, fast initial response EWMA, adaptive CUSUM with EWMA-based shift estimator, weighted CUSUM and runs rules-based CUSUM and EWMA. The comparisons revealed that mixing the two charts makes the proposed scheme even more sensitive to the small shifts in the process mean than the other schemes designed for detecting small shifts. Copyright © 2012 John Wiley & Sons, Ltd. [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]

Control charts are the most important statistical process control tool for monitoring variations in a process. A number of articles are available in the literature for theX̄control chart based on simple random sampling, ranked set sampling, median-ranked set sampling (MRSS), extreme-ranked set sampling, double-ranked set sampling, double median-ranked set sampling and median double-ranked set sampling. In this study, we highlight some limitations of the existing ranked set charting structures. Besides, we propose different runs rules-based control charting structures under a variety of sampling strategies. We evaluate the performance of the control charting structures using power curves as a performance criterion. We observe that the proposed merger of varying runs rules schemes with different sampling strategies improve significantly the detection ability of location control charting structures. More specifically, the MRSS performs the best under both single- and double-ranked set strategies with varying runs rules schemes. We also include a real-life example to explain the proposal and highlight its significance for practical data sets. [ABSTRACT FROM AUTHOR]

The control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger disturbances in the process parameters, whereas CUSUM and EWMA charts are meant for smaller and moderate changes. Runs rules schemes are generally used to enhance the performance of Shewhart control charts. In this study, we propose two runs rules schemes for the CUSUM charts. The performance of these two schemes is compared with the usual CUSUM, the weighted CUSUM, the fast initial response CUSUM and the usual EWMA schemes. The comparisons revealed that the proposed schemes perform better for small and moderate shifts, whereas they reasonably maintain their efficiency for large shifts as well. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The study proposes a Shewhart-type control chart, namely an MD chart, based on average absolute deviations taken from the median, for monitoring changes (especially moderate and large changes - a major concern of Shewhart control charts) in process dispersion assuming normality of the quality characteristic to be monitored. The design structure of the proposed MD chart is developed and its comparison is made with those of two well-known dispersion control charts, namely the R and S charts. Using power curves as a performance measure, it has been observed that the design structure of the proposed MD chart is more powerful than that of the R chart and is very close competitor to that of the S chart, in terms of discriminatory power for detecting shifts in the process dispersion. The non-normality effect is also examined on design structures of the three charts, and it has been observed that the design structure of the proposed MD chart is least affected by departure from normality. [ABSTRACT FROM AUTHOR]

In this study, a Shewhart-type control chart is proposed for the improved monitoring of process mean level (targeting both moderate and large shifts which is the major concern of Shewhart-type control charts) of a quality characteristic of interest Y. The proposed control chart, namely the M r chart, is based on the regression estimator of mean using a single auxiliary variable X. Assuming bivariate normality of ( Y, X), the design structure of M r chart is developed for phase I quality control. The comparison of the proposed chart is made with some existing control charts used for the same purpose. Using power curves as a performance measure, better performance of the proposed M r chart is observed for detecting the shifts in mean level of the characteristic of interest. [ABSTRACT FROM AUTHOR]

The study proposes a Shewhart-type control chart, namely Q chart, based on inter-quartile range, for monitoring changes (especially of moderate and large amounts which is major concern of Shewhart-type control charts) in process dispersion assuming normality of quality characteristic to be monitored. The design structure of Q chart is developed and its comparison is made with those of well-known R and S charts and RQ chart proposed by Rocke (1992). The comparisons have revealed superiority of the proposed Q chart as compared to R and S charts in terms of discriminatory power and resistance against non normality and as compared to RQ chart in terms of relaxing the restriction for 25th and 75th ordered positions in the data to be integer values and in terms of detecting special causes in the absence and presence of outliers in the data. [ABSTRACT FROM AUTHOR]

Lately, the multivariate setup of control charts, especially the memory‐less chart has received less attention of researchers as compared to the univariate setup. However, the multivariate setup is of paramount importance in this big‐data era. In this research work, we study the multivariate Shewhart chart for monitoring location parameter by examining the robustness of this scheme with the mean estimator. We also explored the scheme with some other robust parametric estimators in different process environments. The multivariate estimators such as median, midrange, tri‐mean (TM), and Hodges–Lehmann (HL) estimators were examined under uncontaminated, location contaminated, variance contaminated, and both location–variance contaminated normal environments. Through a synthetic Monte Carlo simulation and application of the schemes on a real‐life dataset, the findings suggest that the proposed estimators outperform the default estimator of the multivariate scheme (mean). The performance measures of comparing these estimators through the charts are the average run length, standard deviation run length, extra‐quadratic loss, and relative average run length. The charts resulting from applying the schemes on real‐life dataset recorded from glass manufacturing process also buttresses the simulation findings. [ABSTRACT FROM AUTHOR]

Control charts are popular monitoring tools in statistical process control toolkit. These are used to identify assignable causes in the process parameters (location and/or dispersion). These assignable causes result in a shift in the process parameter(s). The shift can be categorized into three sizes (small, moderate, and large). Memory control charts such as the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts are effective for identifying small‐to‐moderate shift(s) in the process. Likewise, mixed memory control charts are useful for efficient process monitoring. In this study, we have proposed two new mixed memory control charts based on auxiliary information named MxMEC and MxMCE control charts to improve the efficiency of these mixed charts. The MxMEC chart is a merger of the auxiliary information based MxEWMA chart and the classical CUSUM chart. Likewise, the MxMCE chart integrates the auxiliary information based MxCUSUM with the classical EWMA chart. The proposed MxMEC and MxMCE charts are evaluated through famous performance measures including average run length, extra quadratic loss, relative average run length, and performance comparison index. The performance of the study proposals is compared with the existing counterparts such as the classical CUSUM and EWMA, MxCUSUM, MxEWMA, MEC, MCE, and runs rules‐based CUSUM charts. The comparisons revealed the superiority of the proposed charts against other competing charts particularly for small‐to‐moderate shifts in the process location. Finally, a real‐life data is used to show the implementation procedure of the proposed charts in practical situations. [ABSTRACT FROM AUTHOR]

Linear profiles are quite popular in establishing relationships among different variables associated with each other in an ongoing process. Control charting methodologies for these linear profiles are used to monitor and improve the performance of a process. The commonly used Phase I methodologies of linear profiles are mostly based on simple random selection procedures. In this study, we intend to improve the existing Phase I profile methods by considering different ranked set strategies including ranked set sampling (RSS), median RSS (MRSS) and extreme RSS (ERSS). The profile monitoring is considered in terms of three main parameters namely slope, intercept and error variance for efficient detection of any assignable cause(s). We have used probability to signal as a performance measure in our study. A real-life application of the proposed methods is also presented in this study using real data from electrical engineering related to a grid-connected photovoltaic system. [ABSTRACT FROM AUTHOR]

Random causes are vital part of every process in manufacturing and nonmanufacturing environments, and these do not affect the product features. Special causes, on the other hand, come because of some burden(s) in a process and requires special attention; otherwise, it ruins the products excellence. Special causes are categorized into small, moderate, and large shifts and are handled by statistical quality control charts. The Shewhart control chart is well known for large shifts, while the cumulative sum and exponentially weighted moving average are more effective in detecting small to moderate shifts. However, in practice, many processes require the simultaneous monitoring of both the small to the large shifts. In this study, we have designed an adaptive EWMA for dispersion parameter in connection with Huber and Tukey's bisquare functions. The performance measures used in this study include average run length, extra quadratic loss, relative average run length, and performance‐comparison index. We have observed that the study proposals are good competitors to the other counter parts for an efficient monitoring of shifts of varying amounts. An illustrative example using real data is given to demonstrate the implementation of the study proposal. [ABSTRACT FROM AUTHOR]

The examination of product characteristics using a statistical tool is an important step in a manufacturing environment to ensure product quality. Several methods are employed for maintaining product quality assurance. Quality control charts, which utilize statistical methods, are normally used to detect special causes. Shewhart control charts are popular; their only limitation is that they are effective in handling only large shifts. For handling small shifts, the cumulative sum (CUSUM) and the exponential weighted moving average (EWMA) are more practical. For handling both small and large shifts, adaptive control charts are used. In this study, we proposed a new adaptive EWMA scheme. This scheme is based on CUSUM accumulation error for detection of wide range of shifts in the process location. The CUSUM features in the proposed scheme help with identification of prior shifts. The proposed scheme uses Huber and Tukey bisquare functions for an efficient shift detection. We have used average run length (ARL) as performance indicator for comparison, and our proposed scheme outperformed some of the existing schemes. An example that uses real-life data is also provided to demonstrate the implementation of the proposed scheme. [ABSTRACT FROM AUTHOR]

The traditional control charts produce frequent false alarm signals in the presence of autocorrelation. The implementation of the modified chart scheme is a way of handling the problem of autocorrelation in control charts. In modified charts, the standard control limits of the traditional charts are adjusted to offset the influence because of the autocorrelation. The exponentially weighted moving average- and cumulative sum-modified charts are 2 widely used charts for monitoring autocorrelated data. These charts have design parameters in their formulation, and the choice of these parameters play significant roles in the detection of out-of-control situations. In reality, the magnitude of the mean shift is uncertain, and this leads to a difficulty in the choice of design parameters by the practitioner. The use of optimal parameters can enhance process performance in such situations. In this paper, we determine optimal design parameters for the charts using an exhaustive search procedure. In the optimization process, we determine the parameters that produce the smallest extra quadratic loss (EQL) value for each autocorrelation coefficient. This criterion measures the anticipated loss attributed to poor quality in the process. The loss in quality is lowered by minimizing the EQL and the combination of parameters in the chart that yields the smallest EQL has a better detection ability over the entire shift range. For the purpose of this work, we concentrate on autocorrelation that can be specifically modelled with autoregressive models. This article provides the practitioner with optimal parameters that can be used to enhance the overall effectiveness of the charts over an entire shift range. [ABSTRACT FROM AUTHOR]

Multivariate control charts are well known to be more sensitive to the occurrence of variation in processes with two or more correlated quality variables than univariate charts. The use of separate univariate control charts to monitor multivariate process can be misleading as it ignores the correlation between the quality characteristics. The application of multivariate control charts allows for the simultaneous monitoring of the quality characteristics by forming a single chart. The charts operate on the assumption that process observations are normally distributed, but in practice this is not always the case. In this study, we examine and present multivariate dispersion control charts for detecting shifts in the covariance matrix of normal and non-normal bivariate processes. These control charts, referred to as SMAX, QMAX, MDMAX and MADMAX, rely on dispersion estimates, such as the sample standard deviation ( S), interquartile range ( Q), average absolute deviation from median ( MD) and median absolute deviation ( MAD), respectively. We compare the performances of these charts to the existing multivariate generalized variance | S| and RMAX charts for bivariate processes using normal and non-normal parent distributions. The average run length ( ARL) measure is used for the evaluation and comparison of the charts. A real life and simulated datasets are used to demonstrate the application of the charts. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In a process, the deviation from location or scale parameters affects the quality of the process and waste resources. So it is essential to monitor such processes for possible changes due to any assignable causes. Control charts are the most famous tool used to meet this intention. It is useless to monitor process location until the assurance that process dispersion is in-control. This study proposes some new two-sided memory control charts named as progressive variance (PV) control charts which are based on sample variance to monitor changes in process dispersion assuming normality of quality characteristic to be monitored. Simulation studies are made, and an example is discussed to evaluate the performance of the proposed charts. The comparison of the proposed chart is made with exponentially weighted moving average- and cumulative sum-type charts for process dispersion. The study shows that performance of the proposed charts are uniformly better than its competitors for detecting positive shifts while for detecting negative shift in the variance their performance is better for small shifts and reasonably good for moderated shifts. [ABSTRACT FROM PUBLISHER]

A profile is a functional relationship, between two or more variables, used to monitor the process performance and its quality. The relationship may be linear or nonlinear depending upon the situation. Linear profiling methods with a fixed-effect model are commonly used under simple random sampling (SRS). In this article, we propose linear profiles monitoring methods under a new ranked set sampling (RSS) scheme named as Neoteric RSS (NRSS). The new profiling methods are proposed under all the three popular structures, namely Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA). The study proposal considers both classical and Bayesian setups. We have investigated the detection ability of newly proposed classical charts (i.e., Shewhart_NRSS(C), CUSUM_NRSS(C), EWMA_NRSS(C) charts) and Bayesian charts (i.e., Shewhart_NRSS(B), CUSUM_NRSS(B) and EWMA_NRSS(B) charts). An extensive simulation study showed that the proposed charts have better detection ability for perfect NRSS scheme, while Bayesian control charts showed superiority over its classical counterpart under both perfect and imperfect NRSS. The significance of the proposed study is further highlighted using the real data study of chemical gas sensors from the chemical industry. • Designing of charts for the monitoring of linear profile parameters using Neoteric RSS under classical and Bayesian setups. • Bayesian approach efficiently handles the parametric uncertainty of processes. • The proposed study effectively applied upon chemical gas sensors from the chemical industry. • The proposed Bayesian charts efficiently identify the out-of-control profiles of the MOX sensor. [ABSTRACT FROM AUTHOR]

Process control measures are mostly applied in production and manufacturing industries. The most important tool used in these disciplines is control chart. In manufacturing and production processes, when the quality characteristic of interest cannot be directly measured, it becomes essential to apply attribute control charts. To monitor fraction nonconforming of the output, quality practitioners mostly prefer p‐chart. In this article, a new progressive mean (PM) control chart is being proposed for monitoring drift in proportion of nonconforming products. The design evaluations of the proposed chart are made and compared through different properties of run length distribution, such as average run length (ARL), standard deviation of run length (SDRL), and some percentile points. The performance of the proposed chart is assessed under zero‐state and steady‐state scenarios. The proposed PM chart is compared with p‐chart, moving average (MA) chart, optimal CUSUM chart, modified exponentially weighted moving average (EWMA) chart, and runs rules p‐charts for monitoring fraction nonconforming. The proposed chart spots efficiently sustained disturbances in the process as compared with their existing counterparts. Two illustrative examples are also provided; one from real‐life application of nonconforming bearing and seal assemblies data and the other from simulated data for the implementation of PM chart. [ABSTRACT FROM AUTHOR]

Control chart is a popular technique that is widely used in statistical process control to identify any possible deviations from a stable state of a process. Shewhart charts are famous for identifying larger shifts, while cumulative sum and exponentially weighted moving average control charts are well known for identifying smaller shifts in process parameters. This study examines the performance of a sequential-based EWMA (namely S E W M A ) chart for observing the location of a normally distributed process. The performance of S E W M A is oberved by using several overall run length properties (like average, median and standard deviation). The comparative study reveals that the overall performance of the proposed design is better than the existing counterparts. Moreover, the superiority depends on the choice of the design parameters of the proposed chart. A real-life data set from a steel rod manufacturing industry were considered to show the real-life illustration of the proposed design. [ABSTRACT FROM AUTHOR]

Primarily, three types of control charts are employed to observe the disturbances in the process parameters. The large shifts are detected efficiently by Shewhart's control chart, whereas for small and medium instabilities, cumulative sum and exponentially weighted moving average control charts are used. This study proposes an assorted approach to monitor small, medium, and large disturbances in a single control charting procedure. For the evaluation of the proposed assorted control chart, we have used various measures like average run length, standard deviation run length, extra quadratic loss, relative average run length, and performance comparison index. A comparison of the proposed assorted control chart is presented with some typical charts including the Shewhart, cumulative sum, exponentially weighted moving average control charts, and some advanced modification such as combined Shewhart‐EWMA (CSE), combined Shewhart‐CUSUM (CSC), mixed EWMA‐CUSUM (MEC), FIR EWMA, and adaptive EWMA (AEWMA). Finally, a real‐life application example is presented to monitor the pH level of water in an ecotoxicology lab. This study proposes an assorted approach to monitor small, medium and large disturbances in a single control charting procedure. For the evaluation of the proposed assorted control chart, we have used various measures like average run length, standard deviation run length, extra quadratic loss, relative average run length, and performance comparison index. Finally, a real‐life application example is presented to monitor the pH level of water in an ecotoxicology lab. [ABSTRACT FROM AUTHOR]

In manufacturing industries, control charts are the promising statistical tools used for an efficient monitoring of processes. These charts enhance the product quality by timely signaling for special variations at any stage of the process. There are two common concerns in statistical process monitoring, location and variability of the quality characteristic of interest. Besides location parameter, the monitoring of process dispersion remained a matter of concern for researchers. The conventional simple random sampling (SRS) is a usual practice; however, ranked set sampling (RSS) schemes are very effective methods of choosing sample values. This study intends to design and investigate dispersion control charts under different RSS strategies for normal and non‐normal processes. We have considered RSS, median ranked set sampling (MRSS), and extreme ranked set sampling (ERSS) schemes to design dispersion control charts. The performance of the existing and the proposed control charts is evaluated in terms of relative efficiency and power for normal and a variety of non‐normal distributions. The comparative analysis revealed that the proposed structures outperform the existing charts. The application of the proposed procedures is also shown for a bottles filling process for an efficient and timely signaling of any special causes in the process. [ABSTRACT FROM AUTHOR]