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AbstractMonitoring multivariate time between events (MTBE) data is critical in areas such as manufacturing and service operations. Monitoring of multiple dependent Weibull processes is often required in high quality processes, yet existing methods for monitoring Weibull time between events (TBE) data have been developed based on univariate processes. This paper develops multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts for monitoring the mean vector of multiple dependent Weibull processes from the Normal, Clayton, Frank, Gumbel and Joe copula models. The performance of the two charts is evaluated using Monte Carlo simulation based on the average time to signal (ATS) metric for in-control and out-of-control states. To further illustrate the use of the chart, two applications are illustrated. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, a one-sided adaptive exponentially weighted moving average X¯ scheme utilizing the truncation method (called the one-sided TAEWMA X¯ chart) is proposed for monitoring processes involving measurement errors. Leveraging the linear covariate error model, both the Markov chain model and the Monte Carlo simulation are established to evaluate the run length (RL) properties of the proposed chart. In the presence of measurement errors, an optimal search strategy is developed to identify the optimal design parameters of the scheme. Subsequently, the effects of measurement errors on both zero-state and steady-state average run length (ARL) performance of the proposed scheme are studied. Comparative studies with two other existing schemes reveal that, despite the significant effects of measurement errors on ARL performance, the proposed chart remains superior to the existing competing schemes in shift detection, especially when multiple measurement operations are involved. Finally, an illustrative example is provided to demonstrate the application of the suggested scheme. [ABSTRACT FROM AUTHOR]

AbstractA control chart is a crucially important tool in statistical process control that is essentially used to differentiate between assignable and chance causes of variation. With the advent of Six Sigma in the parlance of quality management, the control chart assumes more significance to hold the gain in the control phase after attaining process improvement in the improve phase. The sample size, sampling interval, and control limits’ multiplier are three important parameters required to design an effective as well as efficient control chart. Many approaches like economic design, statistical design, and economic statistical design of control charts have been studied by several researchers. All these approaches consider a single objective function. In this paper, we have proposed a multi-objective design of the p-chart, where we are minimizing both out-of-control Average Run Length (ARLδ) and the expected cost per cycle (CE). Non-dominated Sorting Genetic Algorithm II (NSGA II) is used to solve the proposed multi-objective model. The proposed approach is demonstrated with the help of two numerical examples and the corresponding results are found to be quite encouraging for minimizing both objectives in comparison to another pertinent model given in the literature. Sensitivity analysis has been carried out to give credence to the worth of the proposed approach. [ABSTRACT FROM AUTHOR]

Student evaluations on the teaching efficiency of faculty members are significant in Higher Education because they allow decisions makers to identify potential inefficient faculty members and by making appropriate decisions, to improve the quality of the teaching process of their Institution. In this paper, we revisit the methodology framework of the previous paper of Sivena and Nikolaidis in order to expand it in various ways. More specifically, we examine in detail and evaluate through simulation, several popular types of X ¯ and X ˜ control charts, identifying the most suitable among them, using various statistical properties of control charts as optimization criteria. Additionally, we present two more tools that will allow Higher Education Institutions if exploited in real life, to find differences in the teaching performance between evaluated faculty members, either the latter are compared in one or more pairs. All in all, we provide Higher Education decision-makers with easy-to-use and reliable tools for monitoring the teaching process of their Institutions. [ABSTRACT FROM AUTHOR]

In the application of quality control charts, most of the researches in profile monitoring are based on accurate measurements. Measurement errors, however, often exist in many manufacturing and service environment. In this paper, first, we investigate the effect of neglecting measurement errors on the performance of four common control charts for Phase II monitoring of polynomial profiles. Simulation studies in terms of average run length (ARL) metric show that neglecting the measurement errors adversely affects the capability of all mentioned control charts. Then, we reconstruct the control charts based on two consistent estimators and two remedial approaches including multiple measurement and increasing sample size are utilized to decrease the effect of measurement errors. The results show that the remedial approaches compensate for the mentioned effect to a certain extent. [ABSTRACT FROM AUTHOR]

Three-level control charts have been developed for monitoring processes where the quality of products is characterized by classifying the product characteristic into three discrete levels. In this paper, a double sampling (DS) attribute control chart is developed for monitoring three-level products. The statistical performance of the proposed chart is evaluated using the statistical measures average run length (ARL) and average number of inspected items to signal (ANI). A Markov chain approach is applied to derive expressions for ARL and ANI. Using numerical examples, we illustrate the performance of the proposed control scheme and compare its efficiency with the other control schemes. The results approve the statistical efficiency of the proposed control scheme compared with the other control schemes. [ABSTRACT FROM AUTHOR]

The monitoring and detection of network anomalies have become an interesting topic in social network analysis. One approach for detecting such anomalies is to apply conventional centrality measures, such as degree centrality, closeness centrality, and betweenness centrality, to control charts. Another approach involves the use of hybrid centrality measures, such as degree-degree, degree-closeness, and degree-betweenness, which are generated by combining traditional centrality measures and emphasize the importance of actors in the network. From another perspective, most studies on weighted networks have used centrality measures based on tie weights alone and have not accounted for the number of ties. In this paper, we propose exponentially weighted moving average (EWMA) charting procedures that use several types of centrality measures based on the number and weights of ties in undirected weighted networks. We then evaluate the anomaly-detection performance of these measures on weighted networks using EWMA charts. Simulation results indicate that degree and degree-degree centralities perform well for small changes, while betweenness and degree centralities perform well for large changes. In addition, centrality measures that consider both the number and weights of ties, with more importance given to the weights were determined to be better at detecting anomalies. [ABSTRACT FROM AUTHOR]

AbstractOccurrences of two (or more) types of defects are not uncommon in high quality (or zero-inflated) processes. Bivariate (or multivariate) attribute control charts are required to be developed for monitoring such processes. Fatahi et al. (2012) introduced the idea of applying copula function to derive joint distribution of two correlated zero-inflated Poisson distributions, which can be used to develop bivariate attribute control chart (BACC) for monitoring bivariate zero-inflated Poisson (BZIP) processes. However, the copula-based model often fails to represent bivariate zero-inflated data, which we come across in real-life situations. Li et al. (1999) model for BZIP distribution is quite flexible. Thus, it can overcome the limitations of Fatahi et al. (2012) model. In this paper, BACC is developed based on Li et al. (1999) model. The performances of the proposed BACC, in terms of in-control and out-of-control average run lengths, are evaluated extensively using simulations. The results show that the proposed BACC are well capable of detecting shifts in the parameters of a BZIP process. Further, two case studies on BZIP process data obtained by past researchers reveal that the BACC developed based on Li et al. (1999) model is more effective than the BACC developed based on copula-based model. [ABSTRACT FROM AUTHOR]

A nonparametric process monitoring scheme with Variable Sampling Intervals (VSI) is investigated to monitor the median in this paper. The proposed scheme is based on the sign test statistic and used a new adaptive-type exponentially weighted moving average control chart. A discrete-state Markov Chain method is presented, and the exact Average Time to Signal (ATS) properties are discussed. The comparative results indicate that the proposed chart can reduce the time in detecting shifts than the corresponding schemes for the fixed sampling interval and provide higher sensitivity for detecting shifts with various magnitudes. The proposed chart's in-control robustness is demonstrated for different continuous distributions using numerical results based on the Monte-Carlo simulation. The implementation of this VSI nonparametric chart is also explained through an illustrative example. [ABSTRACT FROM AUTHOR]

Recent researches have shown that a single control chart, Max chart, simplifies and performs as effectively as joint X( and S charts for monitoring both the process mean and standard deviation. This paper develops an economic design of Max chart based on a unified model and embellishes it with Taguchi’s quality loss function. A simplified algorithm is applied to determine the optimal settings of three control chart parameters: the sample size, the sampling interval and the control limit. A numerical example is presented to illustrate its application. A fractional factorial experiment is conducted to study the sensitivity of the input parameters on the optimal designs of Max chart and joint X( and S charts. Statistical and economical properties including in-control average time to signal (ATS), out-of-control ATS, adjusted average time to signal (AATS) and minimum expected cost per hour of the two charts are compared. The results show that the two charts operate with approximately equal cost but different false alarm rate and detecting speed for the assignable cause without and with consideration of process failure mechanism. Critical input parameters that cause these differences are then investigated. This paper concludes that Max chart can be a good alternative to joint X( and S charts from not only statistical and simplified properties but also economical considerations. [ABSTRACT FROM AUTHOR]

AbstractToday, manufacturers face two challenges: the financial limitation of sampling and the large number of study quality characteristics. In such situations, the problem dimension is usually larger than the sample size, which is known in the literature of statistical quality control as a high dimensional process. The use of conventional multivariate charts to monitor the covariance matrix of such processes is not possible for two reasons: (1) the non-reversibility of the sample covariance matrix, (2) the possibility of the occurrence of sparse shifts in which only a few elements of the covariance matrix are affected. This paper focuses on Phase II monitoring of the covariance matrix of high-dimensional processes based on integrating the ridge penalized likelihood ratio (RPLR) statistic and the combined DS-VSS sampling method, in which DS and VSS features are used concurrently. The performance of the proposed DS-VSS RPLR control chart is compared with the RPLR one in terms of three metrics of the average run length, standard deviation of the run length, and expected value of sample size under three types of joint diagonal/off-diagonal, diagonal and off-diagonal shift patterns. The results show that the DS-VSS scheme is more sensitive than the RPLR in detecting both sparse and non- sparse changes. [ABSTRACT FROM AUTHOR]

The coefficient of variation (CV) is one of the most important quality characteristics in industrial applications. There have been some control charts for monitoring this dimensionless and normalized characteristic. This paper presents three double exponentially weighted moving average (DEWMA) charts for monitoring the CV. Many numerical simulations are performed and the results are given in several tables to show the properties of the proposed DEWMA CV charts. Through comparisons with other existing exponentially weighted moving average (EWMA) CV charts using the average run length (ARL) metric, it is shown that the proposed DEWMA CV charts perform better than the existing EWMA CV charts for small shifts, especially when the smoothing parameter increases. In addition, a detailed comparison among the proposed three DEWMA CV charts is also presented. A real date example from industry is used to show the implementation of the proposed charts. [ABSTRACT FROM AUTHOR]

There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: "covariance matrix" and "multivariate coefficient of variation (MCV)". This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice. [ABSTRACT FROM AUTHOR]

Control charts are very popular quality tools used to detect and control industrial process deviations in Statistical Process Control. In the current paper, we propose a new single memory-type control chart, called the maximum triple exponentially weighted moving average chart (referred as Max-TEWMA chart), that simultaneously detects both upward and downward shifts in the process mean and/or process dispersion. The run length performance and the diagnostic ability of the Max-TEWMA control chart are compared with that of the Max-EWMA, Max-DEWMA and Max-GWMA charts, through Monte-Carlo simulations. The comparisons reveal that the proposed chart is more efficient, than the competing ones, in detecting shifts in the process mean and variability simultaneously. Furthermore, the Max-TEWMA chart provides a satisfactory overall performance for identifying a wide range of shifts in the process mean and variability. Finally, two illustrative examples are presented to explain the application of the Max-TEWMA control chart. [ABSTRACT FROM AUTHOR]

A Shewhart control chart is highly effective for detecting large process shifts, however, it is relatively less sensitive to the small shifts. A CUSUM chart is more efficient than a Shewhart chart for detecting small process shifts but it is inferior to that for detecting the large ones. This paper proposes the nonparametric combined Shewhart-CUSUM (CSC) control charts based on the sign statistic. By appropriately choosing the design parameters, a CSC chart exhibits a trade-off statistical performance between that of the Shewhart and CUSUM sign charts. That is, its performance lies between the performances of the Shewhart and CUSUM sign charts for detecting a process shift of any size. The expressions for the zero-state and steady-state average run lengths of the CSC chart are obtained using a Markov chain approach. An example illustrating the implementation of this chart is provided. [ABSTRACT FROM AUTHOR]

Multivariate statistical process control (MSPC) was developed for the monitoring of variables that are either all numerical or all categorical. In the present paper, we describe a nonparametric control scheme that can be used to monitor a mixture of numerical and categorical variables simultaneously. It integrates Principal Component Analysis Mix (PCA Mix), a multivariate statistical tool, with the conventional Hotelling T2 chart. To estimate the control limit for the PCA Mix-based T2 statistic, two nonparametric approaches – kernel density estimation (KDE) and bootstrap – are employed, because of the unknown nature of the underlying distribution. The simulation results demonstrate that with an appropriate number of principal components, both bootstrap and KDE exhibit convincing performance in terms of generating the same, or nearly the same, number of false alarms (ARL0) as expected, and being able to detect process shifts efficiently (ARL1). Compared with bootstrap, KDE is shown to work better with small sample sizes (n < 800) and to be slightly more sensitive to small shifts. However, the results also show the instability of the estimated nonparametric control limit when highly imbalanced categorical variables are included, which indicates the need for further research on this topic. [ABSTRACT FROM AUTHOR]

In the present paper, we have tried to discover a new calibration estimator of the population mean under stratified two-phase sampling design utilizing the information on two auxiliary variables. In order to develop the calibration estimator, we have obtained new optimum stratum weights so that the MSE/Variance of the estimator would be minimized. Moreover, we have compared the efficiency of the proposed calibration estimator with that of the calibration estimator considered under the stratified two-phase sampling scheme using single auxiliary variable. An empirical illustration along with a simulation analysis has also been presented to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

Coefficient of variation (CV) control charts are widely used to monitor processes in which the mean of process characteristic varies and its standard deviation changes with the mean. However, such charts are generally insensitive to small shifts. In the present paper, a double-sampling (DS) CV chart is proposed to effectively detect the CV shifts. An optimization model is developed to obtain the values of parameters in the DS CV chart. The performance of the DS CV chart is then compared with those of the variable parameter (VP) and the exponentially weighted moving average (EWMA) CV charts. Based on our study, the DS CV chart outperforms the VP CV chart in detecting all shift sizes. In addition, the EWMA CV chart outperforms the DS CV chart only for very small shifts, but the DS CV chart generally has better detection ability than the EWMA CV chart for small, medium and large shifts. A real example of integrated circuit lead electroplating is provided for illustration of applications and this example confirms that the DS CV chart has superior performance when compared with the other two charts. [ABSTRACT FROM AUTHOR]

Attribute control charts assuming a Poisson (c chart) or a binomial distribution (np chart) are usually used when the quality characteristic cannot be measured on a continuous scale. For equivalent sample sizes, Shewhart type attribute control charts are known to be less efficient than their measurement counterparts (like the X ¯ chart) and, for this reason, practitioners often compensate it by supplementing them with an EWMA (Exponentially Weighted Moving Average) scheme. However, because of the discrete nature of count data, it is unfortunately impossible to compute exactly and accurately (by means of Markov chain of integral equation methods) the run length (RL) properties, such as its mean (ARL) and its standard deviation (SDRL) of these EWMA attribute control charts and, consequently, it is impossible to efficiently design them in order to minimize some out-of-control characteristics. For this reason, we propose in this paper a dedicated approach called "continuousify" method which, coupled with a classical Markov chain technique, allows to compute the RL properties of any EWMA attribute control chart in a reliable way. A numerical comparison shows that the RL properties obtained by using the proposed "continuousify" approach are very much alike to the ones calculated via simulation and without the "continuousify" approach. Illustrative examples are also provided to show how the proposed method can be implemented in practice. [ABSTRACT FROM AUTHOR]

In the context of big data, multivariate processes must often be monitored in a timely and accurate manner. Usually, the distribution of process variables is unknown. This paper proposes a new strategy for multivariate process monitoring when the distribution of a process variable is unknown. We address monitoring by means of a rank-based method that is completely nonparametric. We also discuss the optimal strategy of parameters. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real data example is presented to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Adapted from the acceptance sampling field, the double sampling monitoring schemes implement a two-stage strategy to decide whether the process being monitored is in-control or out-of-control. That is, a master sample is split into two separate subgroup samples, with the first subgroup sample used in the first stage and, depending on which type of double sampling method is used, either only the second or the combined first and second, subgroup sample(s) are used in the second stage. This strategy has been proven to effectively decrease the sampling effort and, at the same time, to decrease the time to detect potential out-of-control situations. For these reasons, it has received some attention in the statistical process monitoring (SPM) literature and, in this review paper, all 92 existing publications on the basic double sampling monitoring schemes and other different schemes that are integrated with the basic double sampling schemes are reviewed. The double sampling schemes are categorized and summarized so that any research gaps in the SPM literature can easily be identified. Finally, concluding remarks and some directions for future research ideas are given. [ABSTRACT FROM AUTHOR]

Using control charts to monitor healthcare systems has gained particular attention. In this paper, a risk-adjusted cumulative sum control chart is designed to monitor surgery outputs. Before undergoing surgery, the patients have some unique risk factors which influence the surgery outputs. Thus, risk-adjustment is carried out with the purpose of taking these risks into account using an accelerated failure time model. But the technical implementation of the chart requires determining the design parameters which should be selected in an optimal way putting the desired statistical and economic considerations into service. To this end, a multi-objective model, considering multiple assignable causes with different occurrence rates and effects, has been proposed and the model is solved with the help of a 5-stage algorithm based on the data envelopment analysis method. Then, to show the performance of the proposed procedure, a real case study has been conducted in the cardiac surgery center. Finally, sensitivity analysis on some cost parameters has been conducted. Furthermore, comparisons of the proposed multi-objective model with the pure economic and pure statistical design models have been made. The results reveal that the multi-objective design of the risk-adjusted cumulative sum chart shows acceptable performance in the presence of multiple assignable. [ABSTRACT FROM AUTHOR]

Nowadays, data and information are recognized as a precious resource in an organization. Data quality indicators help organizations manage the quality of data quantitatively and improve organizational processes. Organizations manage data and information with the help of information systems and make decisions within the framework of data collected and analyzed. On the other hand, continuous evaluation of the quality of data flow in systems can lead to a preventive program to formulate strategies for improving performance. This can be done effectively and efficiently in the form of control charts. In this paper, control charts are employed to monitor the quality of data flow in information systems using data quality indicators such as Accuracy, Precision, Completeness, and Timeliness simultaneously. The results of the control chart deployment show data quality improvement. Developing solutions that rule out information systems errors, using ready-made lists when generating and entering data, providing timely feedback to users on data quality issues, as well as managing visual training and developing guidelines are suggested as remedial measures to improve data quality in the end. [ABSTRACT FROM AUTHOR]

Ridge estimator is one of the solutions for the multicollinearity problem caused by the absence of independency among predictors. Since the ridge estimator depends on the ridge biasing constant, a large number of techniques were studied in the literature to find a better way of calculating the ridge biasing constant. In this paper, we compare deviance residual-based control charts to ridge deviance residual-based control charts where Shewhart, EWMA, and CUSUM type control charts are constructed. We also compare several existing techniques used to calculate the ridge biasing constant and obtain the iterative ridge estimator for monitoring Poisson profiles under multicollinearity. The performance of each control chart is evaluated by using the average run length criterion and compared through a simulation study and a real data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control chart for monitoring non-normal environmental data. Particularly, we are interested in double bounded data, such as rates and proportions. Control charts based on the well-known beta distribution are usually considered when dealing with this type of data. However, environmental data may not be adequately modeled by this distribution. For double bounded environmental data the Kumaraswamy distribution is admittedly a better alternative than the beta distribution. In this sense, this paper proposes the Kumaraswamy control chart to monitor the behavior of environmental data, said as rates and proportions. The performance of the proposed chart is evaluated and compared with the beta control chart through an extensive Monte Carlo simulation study. The numerical results show that the proposed control chart outperforms the beta control chart in terms of run length analysis. Finally, an application to real data referring to the relative humidity in the city of Des Moines (USA) is presented, evidencing the practical applicability of the proposed chart. [ABSTRACT FROM AUTHOR]

The EWMA-t chart is used to monitor the process mean as it is robust against errors in estimating the process standard deviation or changing standard deviation. In this paper, we propose a double EWMA-t (DEWMA-t) chart for monitoring the process mean, which is a natural extension of the EWMA-t chart. The idea is to first construct the t chart using the EWMA statistic, and then put it into another EWMA statistic to devise the DEWMA-t chart. The DEWMA-t chart encompasses the EWMA-t chart. The Monte Carlo simulation method is used to compute the run length properties of the DEWMA-t chart. Based on detailed run length comparisons, it turns out that, with some reasonable assumptions, the DEWMA-t chart performs uniformly and substantially better than the EWMA-t chart when detecting different kinds of shift in the process mean. A similar trend is observed when these control charts are compared with their counterparts based on the variable sampling interval feature. In addition, real datasets are also used to support the theory. [ABSTRACT FROM AUTHOR]

In some quality control applications, quality of a product or process may be characterized effectively by a functional relationship between one or more variables that is typically referred to as a profile. In this paper, we propose four control charts, namely the maximum CUSUM (MaxCUSUM), maximum Crosier CUSUM (MaxCCUSUM), maximum EWMA (MaxEWMA) and maximum double EWMA (MaxDEWMA) charts, for monitoring the parameters of a Phase II simple linear profile using the individual observations. In addition, the sensitivities of these control charts are also enhanced with the variable sampling interval feature. The Monte Carlo simulation method is used to compute the zero-state and steady-state run length characteristics of the proposed charts. The run length comparisons reveal that the MaxCCUSUM (MaxDEWMA) chart is slightly more sensitive than the MaxCUSUM (MaxEWMA) chart. A real dataset is also considered to support the theory. [ABSTRACT FROM AUTHOR]

The t chart is commonly used as an alternative to the Shewhart mean chart when the process standard deviation is not known in advance. In this paper, we develop variable sampling interval (VSI) based run sum (RS) t charts for monitoring the mean of a normal process when the process mean is either known or estimated, namely the VSI RS t charts. The sensitivity of the proposed chart with known process mean is evaluated and compared with the existing RS t and VSI exponentially weighted moving average t charts, in terms of average time to signal (ATS) and standard deviation of the time to signal (SDTS) criteria, while average of the ATSs (AATS) criterion is used when the process mean is estimated. Quantitative results lead us to conclude that the proposed chart with known process mean generally outperforms the existing charts in detecting the mean shift. Additionally, the AATS performance of the VSI RS t chart with estimated process mean is assessed using the standard deviation of ATS criterion. The minimum number of Phase-I samples to estimate the process mean so that the VSI RS t chart with estimated process mean has the desired in-control AATS performance is provided. [ABSTRACT FROM AUTHOR]

A control chart is very useful to control assignable causes that detect the shifted process parameters (e.g., mean and dispersion). Simultaneous monitoring of the process parameters is a well-known approach utilized for the bilateral processes. This paper explores the joint monitoring to study the impact of both process mean and coefficient of variation (CV) by using log-normal transformation under ranked set sampling (RSS) schemes. Utilizing RSS schemes in joint monitoring of mean and CV with exponentially weighted moving average (EWMA) statistic enhances the capacity of the monitoring plan as compare to EWMA with simple random sampling for a mean shift, CV shift as well as joint shift. RSS schemes using EWMA can lessen the antagonistic impact on recognizing the capacity of a joint monitoring plan. The illustration of the proposed control chart is presented with the aid of a real data set. [ABSTRACT FROM AUTHOR]

In statistical process monitoring (SPM), most of the monitoring schemes are designed assuming that the process parameters for the underlying distribution are known (i.e., Case K). In a variety of contexts, it has been shown that when the parameters used to design the control limits are unknown (i.e., Case U), this greatly affects the monitoring schemes properties. Hence, in this paper, we study the parameter estimation effect of the side-sensitive double sampling (SSDS) X ¯ monitoring scheme for detecting changes in the process mean when distribution design parameters are estimated from an in-control retrospective sample. A thorough investigation is conducted using the unconditional run-length properties (i.e., average, standard deviation and percentiles), average sample size (ASS) and average number of observations to signal (ANOS) through exact integral formulas and simulations. In addition, the average extra quadratic loss (AEQL), average ratio of the average run-length (ARARL) and performance comparison index (PCI) are used to quantify the run-length of the SSDS scheme from an overall performance perspective. Comparisons with other established monitoring schemes when parameters are unknown indicate that the SSDS scheme has a better overall performance. An illustrative example is also given to facilitate the design and implementation of the new scheme. An additional section briefly discussing the synthetic version of the SSDS scheme is also provided. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive T2 control chart for monitoring shifts in the mean vector of a multivariate normally distributed process. This chart which combines the double sampling, variable sample size and variable sampling interval features, called the DSVSSI T2 chart and uses three different sample sizes, two different sampling intervals, two warning limits and two different control limits. A Markov chain approach is employed to compute the statistical performance measures. The proposed chart is compared with the existing standard T2 and other adaptive multivariate T2 charts. In all cases, the DSVSSI T2 chart is quicker at detecting small to moderate shifts in the mean vector. An example is provided to illustrate the construction and implementation of the DSVSSI T2 chart. Finally, some concluding remarks are given. [ABSTRACT FROM AUTHOR]

A multivariate exponentially weighted moving average (MEWMA) and Hotelling's T2control charts are types of multivariate control charts for monitoring the mean vector. In this paper, we propose an efficient construction of bivariate copulas on MEWMA and Hotelling's T2 control charts. Observations are classified with Kendall's tau values as weak, moderate, and strong positive dependence by using a Monte Carlo simulation to measure the average run length as a performance metric. The numerical results obtained from the simulation show that the performances of the MEWMA and Hotelling's T2 control charts were similar for small shifts ( δ ≤ 0.01 ) but the MEWMA control chart showed higher performance for moderate to large shifts. [ABSTRACT FROM AUTHOR]

This paper presents formulas for gradients of the Average Run Length (ARL) and other Run Length characteristics by control scheme parameters, in the context of Markov Chain approach to analysis of Markov-type control schemes. We illustrate use of these formulas in several problems related to control charting, including (a) design of control schemes, (b) inference on Run Length characteristics based on Phase-1 data and (c) performance analysis for highly complex input data distributions. [ABSTRACT FROM AUTHOR]

Developing control charts for monitoring health-care systems has attracted many researchers attention in recent years. Considering the fact that continuous data have more information rather than discrete data, survival time after cardiac surgery is considered as a continuous quality characteristic in this paper. Accelerated failure time regression model is used to adjust the risk of patients' conditions and surgeon groups on the survival times. After that, a risk-adjusted exponentially weighted moving average control chart is proposed to monitor the standardized residuals of accelerated failure time regression. Since the false alarm rate of each patient change dramatically, dynamic probability control limits have been extended to address the issue. The proposed method is evaluated in Phase II in terms of average run length criterion. The results indicate that the proposed method has acceptable performance in identifying the out-of-control state in the process. Furthermore, considering the effect of surgeon groups in the accelerated failure time regression model leads to improvement in the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for monitoring attributed social networks considering random effects, the concept which enables the monitoring procedure to be effective for the networks where the available pairs of communications are randomly accessible from an unknown larger set of individuals. The proposed method presents a statistical approach based on generalized linear mixed model and a likelihood-based control chart for monitoring the social network data. Numerical examples, including simulation studies and a real-world case study, verify the effectiveness of the proposed method due to its' ability in considering random effects. [ABSTRACT FROM AUTHOR]

Student evaluations of faculty members' teaching effectiveness are considered quite important in Higher Education (HE). In this paper, we elaborate on the framework of Nikolaidis and Dimitriadis, based mainly on Statistical Process Control techniques and tools, which enables a deeper analysis and broader exploitation of student evaluation data. More specifically, we thoroughly examine and evaluate through simulation, several popular types of control charts (CCs), identifying the most suitable among them, using as comparison criteria various statistical properties of CCs. The ultimate goal of our research is to provide decision makers in HE institutions with an easy-to-use reliable tool for not only monitoring the teaching process, but also identifying the effective and ineffective faculty members' teaching performance to promote the quality of their Institution. [ABSTRACT FROM AUTHOR]

The economic and economic-statistical performances of auxiliary information based X ¯ , synthetic and EWMA charts in monitoring the mean is investigated. Incorporating the auxiliary characteristic improves the cost and out-of-control run length performances of the charts. As it is often difficult to specify the exact shift size where a quick detection is needed, this paper investigates the economic and economic-statistical performances of auxiliary information based X ¯ , synthetic and EWMA charts, for both cases when the exact shift sizes can and cannot be specified. This is achieved by using the average run length (ARL) and expected average run length (EARL) performance criteria, respectively. The cost performances of these charts are compared and the cost savings are discussed. [ABSTRACT FROM AUTHOR]

Most control charts that assume quality characteristics of interest follow a normal or specific distribution. However, in reality there is often limited or no information regarding the underlying process distribution. Therefore, in recent years, nonparametric techniques for quality control have been developed. In this paper, the nonparametric generally weighted moving average sign chart based on repetitive sampling (hereinafter RS-GWMA sign chart) is proposed to improve performance capability of existing charts in small process shifts. Simulation studies show that the nonparametric RS-GWMA sign chart with large design and adjustment parameters outperforms competing charts considered in this article. The fill volume of soft-drink beverage bottles is as industrial example used to illustrate the application of the proposed nonparametric RS-GWMA sign chart. [ABSTRACT FROM AUTHOR]

The Conway-Maxwell Poisson (COM-Poisson) distribution is a generalization of the Poisson distribution and encompasses the geometric, the Poisson and the Bernoulli distribution as special cases. This distribution can be used to model over- or under- dispersed data in manufacturing processes. For monitoring such data, a flexible memory-type control chart based on the progressive mean (PM) statistic (regarded as CMP-PM chart) is developed in the present paper. Through a simulation study, we investigate the run-length distribution of the proposed chart. The performance comparison study shows that the CMP-PM chart outperforms the Sellers, the GEWMA and the μ-CUSUM charts at almost all levels of shifts for both over- and under-dispersed data. Finally, the application of the proposed chart is given through an illustrative example. [ABSTRACT FROM AUTHOR]

The CUSUM chart is usually designed based on a known shift size when monitoring the process mean. In practice, shift size is rarely known but it can be assumed to vary within an interval. With such a range of shift size, the dual CUSUM (DCUSUM) chart provides more sensitivity than the CUSUM chart. In this paper, auxiliary information based (AIB) DCUSUM and dual Crosier CUSUM (DCCUSUM) charts with and without fast initial response (FIR) features are proposed for efficiently monitoring the infrequent changes in the process mean, named the AIB-DCUSUM and AIB-DCCUSUM charts, respectively. Monte Carlo simulations are used to compute the run length characteristics of these control charts. The AIB-DCUSUM and AIB-DCCUSUM charts with and without FIR features are compared with the DCUSUM, DCCUSUM and AIB-CUSUM charts in terms of the integral relative average run length. It is found that the proposed charts show better performance when detecting mean shifts in different ranges. A simulated dataset is considered to illustrate the implementation of existing and proposed charts. [ABSTRACT FROM AUTHOR]

In some quality control applications, processes consist of multiple components, stations or stages to finish the final products or the services. Some quality characteristics in each stage of these processes (called multistage processes) can be represented by a relationship between a response and one or more explanatory variables which is named as profile. In this paper, a general model is proposed for monitoring multivariate profiles in multistage processes. To this aim, the multivariate form of the U transformation approach is first used to remove the effect of the cascade property between the stages. Then, three control schemes are employed to monitor the parameters of multivariate simple linear profiles in multistage processes in Phase II. The performances of the proposed schemes are evaluated in terms of the average run length criterion using extensive simulation experiments. The results show that the transformation approach efficiently eliminated the effect of the cascade property involved in multistage processes and that all of the schemes perform well under different conditions. In the end, an illustrative example is given to demonstrate the application of the proposed methodology. [ABSTRACT FROM AUTHOR]

Recently, a MIX S2 control chart using both attribute and variable inspections is proposed by Ho and Quinino (2016) to monitor the process variability. This paper considers the variable sampling interval (VSI) MIX S2 control chart in which the sampling interval between two successive sampling points is varied based on the value of the preceding complementary sample variance to improve the performance of the original MIX S2 control chart. Additionally, a unified economical design model for the process is established with the cost function derived. The performance of the VSI chart is compared to that of the original static chart. The numerical results show that comparing to original MIX S2 control chart, the VSI chart yields better performance in terms of less expected cost per unit of time. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of parameter estimation from in-control Phase I samples on the in-control and out-of-control performance of the four Phase II control charts used for monitoring multivariate multiple linear profiles. These methods are evaluated in terms of out-of-control performance using corrected limits. The monitoring approaches investigated are then compared using the statistical properties of the ARL distribution. Furthermore, two optimization models are proposed for finding the optimum number of Phase I samples needed to achieve proper parameter estimations of desired accuracy and solved by using a novel hybrid simulation algorithm named HBSO. [ABSTRACT FROM AUTHOR]

Dealing with high-dimensional data is a major challenge primary in statistics and consequently in statistical process control (SPC). "Shewhart-type" charts are control charts using rational subgrouping. Shewhart-type charts are effective in the detection of large shifts. In high-dimensional settings, where the number of variables is nearly as large as or larger than the number of observations, it will be very hard to use Shewhart-type charts based on rational subgroups. An alternative is to use Shewhart-type charts based on individual observations. Also, for most conventional control charts, the design of the control limits is commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, this may not be reasonable in many real-world problems. This paper addresses these issues and suggests a monitoring methodology motivated by statistical learning theory. The proposed multivariate control chart uses tensor space model to represent a high-dimensional vector. This chart makes use of information extracted from in-control preliminary samples. Simulation studies demonstrate that the proposed control chart has very good performances. [ABSTRACT FROM AUTHOR]

In this paper, Phase II monitoring of multivariate simple linear profiles is considered when the independency assumption of observations within each profile is violated. Then, two control charts for simultaneous monitoring of mean vector and covariance matrix in autocorrelated multivariate simple linear profiles are proposed. Performance of the proposed methods is evaluated by using simulation runs in terms of average run length (ARL) criterion. The results indicate satisfactory performance of the proposed methods. Finally, the application of the proposed methods is shown through a real case data. [ABSTRACT FROM AUTHOR]

This paper studies the residual-based CUSUM test statistic in first-order Poisson integer-valued autoregressive (PINAR(1)) models. The residuals are calculated with squared difference (SD) estimates, and a retrospective phase (Phase I) analysis is conducted. Moreover, the upper one-sided CUSUM-type chart (RCUSUMV chart) is considered for effectively detecting abnormal shifts in a monitoring phase (Phase II) situation. Numerical experiments show that our method can be good alternative to conventional CUSUM charts when an effective detection and accurate change point estimation for small shifts are primary concerns. [ABSTRACT FROM AUTHOR]

Outliers presence may lead to misdetection on out-of-control observations in Phase II, therefore, they should be cleaned in Phase I. This paper proposes PCA Mix based T2 chart with Kernel Density control limit for mixed continuous and categorical data. Simulation studies are conducted to evaluate the performance of proposed chart in detecting outliers from clean and contaminated data. The proposed chart has better performance than the benchmark in monitoring clean data. For contaminated data, proposed chart has optimal performance in situation when categorical data are generated from multinomial distribution with balanced parameters. This is confirmed by simulated and real dataset. Compared to the conventional and other robust charts, the proposed chart demonstrated a great performance by success to detect more outlier correctly for the higher percentage of outlier added. [ABSTRACT FROM AUTHOR]

In this paper, we propose an attribute np control chart for monitoring the mean lifetime of the products under accelerated life test. The control chart is designed based on a new sampling procedure namely modified multiple dependent state sampling which is a modified version of multiple dependent state sampling methodology. The optimal parameters to construct the proposed chart are determined so that the in-control average run length is as close as possible to the specified average run length. The performance of the proposed control chart is investigated using a simulated data and compared with the performance of existing control charts. [ABSTRACT FROM AUTHOR]

This paper develops a new double sampling (DS) monitoring scheme, namely, the side-sensitive DS X ¯ chart, to monitor the process mean. The operational procedure is presented first followed by the exact form of the probability of the in-control process under the normality assumption. Finally, the performance of the new scheme is investigated by minimizing the out-of-control average run-length and extra quadratic loss function. It was observed that the proposed chart presents a better overall performance than the existing DS X ¯ chart. An illustrative example is given to facilitate the design and implementation of the new chart. [ABSTRACT FROM AUTHOR]

This paper develops control charts for monitoring the service times at the different stages of a congested system that provides a multi-stage service to its customers. A simulation-based optimization procedure is used to simulate the whole service process and determine the chart parameters that minimize the average time a customer spends in the system. Statistical constraints are also considered to enhance the statistical properties of the control chart. The proposed socio-statistical method may be of interest for a variety of applications. The method is applied to a fast food restaurant with two waiting stages. [ABSTRACT FROM AUTHOR]