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In this research, it was proposed to create three new robust multivariate charts corresponding to the |S|‐ chart, which are robust to outliers, using three methods, an algorithm, namely the Rousseuw and Leroy algorithm, Maronna and Zamar, and the family of 'concentration algorithms' by Olive and Hawkins. Then the comparison between the proposed and classical method of the researcher Shewhart depends on the total variance (trace variance matrix), the general variance (determinant of the variance matrix), and the difference between the upper and lower control limits to obtain the most efficient charts against outliers through simulation and real data and using a program in the MATLAB language designed for this purpose. The study concluded that the proposed charts dealt with the problem of the influence of outliers and were more efficient than the classical method, in addition, the proposed robust chart (Orthogonalized Gnanadesikan‐Kettenring) was more efficient than the rest of the proposed charts. [ABSTRACT FROM AUTHOR]

A statistical process control (SPC) model is introduced that incorporates sample data on the number of defectives and allows the probability that an assignable cause of variation in each time interval of a finite production process to be nonconstant. A Limited Memory Influence Diagram (LIMID) model is implemented to facilitate the decision in each time interval on whether or not to investigate the potential presence of the assignable cause of variation and restore the process to working condition. The method determines control limits that minimize the costs of inspection, repair, sampling, and operating under the assignable cause of variation. Sample size and sampling intervals can also be adjusted to further reduce the costs of maintaining quality control. This method expands on the capabilities of models that assume that the conditional probability of an assignable cause occurring remains constant throughout the production horizon. [ABSTRACT FROM AUTHOR]

In recent years, researchers and practitioners have been exploring new methods for quality control, including image processing. The effective use of high‐volume image data can significantly improve the monitoring of production and service systems in terms of speed, accuracy, and cost. Adopting an image‐based approach is better than relying on operator‐based solutions, and it offers new perspectives for process monitoring. Image processing can involve extracting features to identify, classify, detect, and cluster. Although there are several transformations to extract features from images, the Fourier method cannot consider the concurrency of frequency and time data, and the wavelet method only considers two specific directions. In multidimensional transforms, the optimal method can provide more information using fewer coefficients. The contourlet transform has advantages such as multiresolution, localization, critical sampling, directionality, and anisotropy. This research investigates the advantages of applying the contourlet transform to images and using data in a generalized likelihood ratio control chart. The results show that this method is more accurate than others because it can examine various directions in images. The proposed methodology algorithm is also presented in this study. [ABSTRACT FROM AUTHOR]

In today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain. [ABSTRACT FROM AUTHOR]

The index Cpk was created to quantify the ability of the process to produce products within the upper and lower specified limits (USL/LSL); but in the last two decades, the sample estimator Ĉpk${\hat{C}}_{pk}$ of the capability index Cpk has also been used to decide whether a lot should be accepted or not; if theĈpk$\ {\hat{C}}_{pk}$ value of a sample of size n, randomly taken from the lot, is lower/larger than a threshold, then the lot is rejected/accepted. The sampling plan based on theĈpk$\ {\hat{C}}_{pk}$ value is known as the Cpk sampling plan. In recent publications, the Cpk sampling plan has been combined with the repetitive sampling scheme. In short, with the repetitive sampling scheme the Cpk sampling plan might be applied m times, consequently, the lot has m chances to be accepted; if the Cpk sampling plan is applied m times and the lot is not accepted, then the lot is finally rejected. The average number of inspected items per lot (ANI) the repetitive Cpk sampling plan (where m > 1) requires to decide whether the lot should be accepted or not is larger than the sample size n of the standard Cpk sampling plan (where m = 1), because of that the repetitive sampling strategy is not recommended. In this article, we propose the sequential Cpk sampling plan where the Ĉpk$\ {\hat{C}}_{pk}$ is compared with two thresholds (c1; c2) with c1 < c2; if theĈpk$\ {\hat{C}}_{pk}$ is lower (larger) than c1 (c2), then the lot is rejected (accepted); otherwise, the decision is left to the next sample. The ANI of the sequential Cpk sampling plan is a function of c1 and c2; depending on the choice of these two design parameters, the ANI of the sequential Cpk sampling plan is substantially lower than the sample size n of the standard Cpk sampling plan. [ABSTRACT FROM AUTHOR]

With recent technological advancements, production systems have become more susceptible to cyber‐physical attacks. Such attacks can be intelligently designed to both alter product dimensions and avoid detection by current Quality Control (QC) tools. The objective of this work is to assess the performance of random sampling strategies for control charting in detecting cyber‐physical attacks. More specifically, the methodology adopts traditional random sampling approaches used for inspection and applies them to univariate control charts. Different random sampling strategies are discussed and their performances under varying attack scenarios are evaluated. In addition, new control chart performance metrics were developed for a more in‐depth analysis, to guide practitioners in assessing how well these control charts increase their robustness to cyber‐physical attacks. [ABSTRACT FROM AUTHOR]

The stochastic block model (SBM) is a random graph model that focuses on partitioning the nodes into blocks or communities. A degree‐corrected stochastic block model (DCSBM) considers degree heterogeneity within nodes. Investigation of the type of edge label can be useful for studying networks. We have proposed a labeled degree‐corrected stochastic block model (LDCSBM), added the probability of the occurrence of each edge label, and monitored the behavior of this network. The LDCSBM is a dynamic network that varies over time; thus, we applied the monitoring process to both the US Senate voting network and simulated networks by defining structural changes. We used the Shewhart control chart for detecting changes and studied the effect of Phase I parameter estimation on Phase II performance. The efficiency of the model for surveillance has been evaluated using the average run length for estimated parameters. [ABSTRACT FROM AUTHOR]

As far as literature of quality control is concerned, this is the first article that advocates the run sum ratio scheme with measurement errors, called the RS‐RZ ME chart. The linear covariate error model is employed in designing the RS‐RZ ME chart in detecting increases and decreases in the ratio of two variables from the normal distribution. The average run length and expected average run length values of the RS‐RZ ME chart are obtained using the Markov chain model. A comparison of the RS‐RZ ME scheme with two measurement errors based charts in the literature, namely, the Shewhart ratio and standard run sum ratio charts is conducted. The results indicate the superiority of the RS‐RZ ME chart over the aforesaid existing charts for most of the shift sizes and shift intervals considered. The findings reveal that as the values of the parameters controlling the accuracy error of the measurement system, (θX,θY)$({{\theta _X},{\theta _Y}})$ increase, the RS‐RZ ME scheme's efficiency increases. In the same vein, as the values of the parameters controlling the precision of the measurement system, (ηX,ηY)$({{\eta _X},{\eta _Y}})$ decrease, the RS‐RZ ME scheme' efficiency increases. Furthermore, as the value of the correlation coefficient between variables X and Y increases, the RS‐RZ ME chart's efficiency increases. The application of the RS‐RZ ME scheme is illustrated using data from a food industry. [ABSTRACT FROM AUTHOR]

Profile data have been widely used in quality control practice. However, in some applications, incomplete profiles, or profiles with continuous missing values, are frequently encountered. Imputation of continuous missing values in profiles for quality control is a challenging problem. In this work, we propose a latent feature model and a two‐step learning algorithm to reconstruct missing values. In the model, an unsupervised deep‐learning technique, variational autoencoder (VAE), is utilized to learn the latent features from profile data. Then a supervised variational coding (SVC) scheme is proposed to map the incomplete profile data to the learned latent features, and imputation is then made based on the latent features. The performance of the proposed method is evaluated by simulated and real datasets, and the results prove the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A measurement system fit for use is an essential resource to perform quality control activities: its assessment must be carried out periodically to quantify its bias(location) and precision(width) error to qualify it for the purpose it is used. In particular, the measurement system capability must be determined to evaluate how much of the observed variability originates from the gauge's precision error. Gauge Repeatability and Reproducibility (R&R$R{\&}R$) studies are aimed at getting a reliable estimate of the precision error σM$\sigma _M$ of the measurement system. The outcome of a Gauge R&R$R{\&}R$ study are point estimates and confidence intervals of the precision error components and related measurement capability metrics. Here, a general bootstrap‐based procedure is proposed to get confidence interval estimations of the measurement error and its components from a Gauge (R&R$R{\&}R$) study for continuous observations carried out with either the Average and Range (ARGG) control chart or the experimental design method. An application of the proposed bootstrap‐method is presented for a dataset of observations. [ABSTRACT FROM AUTHOR]

Burn‐in is an effective method to screen out early failures of electronic devices. Typically, this is achieved by operating the devices under accelerated stress conditions. This paper focuses on a burn‐in concept where a random sample of devices is drawn out of the running production, put to burn‐in, and investigated for early failures. This procedure is called burn‐in study. In parallel, as long as the burn‐in study is ongoing, all other produced devices are subjected to burn‐in screening. In this article, new flexible sampling plans for burn‐in studies are introduced. These are based on the progress of these studies and defined quality targets. Furthermore, these sampling plans enable fast burn‐in time reductions and various time reduction strategies. From a statistical point of view, this requires to combine the proportion of early failures in a population with their lifetime distribution function. The new model is illustrated by case studies and simulations. It contributes to burn‐in cost reductions, while controlling quality levels at the same time. [ABSTRACT FROM AUTHOR]

Some international pharmaceutical companies have succeeded in producing vaccines against COVID‐19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K‐independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics such as the vaccine production cost and the speed of the required quantity delivery. We present a new detection model that helps in providing an appropriate decision to choose a suitable company. Moreover, the concept of balking and the retention of reneged countries is taken into consideration under the quality control process of each waiting list. Under steady state, we face an interesting and difficult discrete stochastic optimization problem. Its solution gives an optimal distribution of the searching effort, which is bounded by a known probability distribution. A simulation study has been derived to get the minimum value of the paid cost random values. The highest service rate, the total expected profit of each queuing system, and the optimum performance measures, which depend on this cost, have been obtained to show the effectiveness of this model. [ABSTRACT FROM AUTHOR]

This article generalizes the historical S2 chart (of equal sample sizes) to subgroups of differing sample sizes. As a sequel to a previous article, Section 2 compares all estimators of process standard deviation for more than M> 1 subgroups of equal sample sizes from a normal universe. Section 3 provides the general probability control limits (CONLs) for the S2 chart of unequal sample sizes. Section 4 uses the chi‐square distribution to obtain the exact α‐level CONLs for the S chart. Section 5 proves that the average run length (ARL) of S and S2 charts are identical and tabulates the ARL for the most common sample sizes n = 2, 3, ..., and 12. Further, as expected, the ARL of S charts always perform better than those of the corresponding R charts. [ABSTRACT FROM AUTHOR]

Exponentially distributed data are commonly encountered in high‐quality processes. Control charts dedicated to the univariate exponential distribution have been extensively studied by many researchers. In this paper, we investigate a multivariate cumulative sum (MCUSUM) control chart for monitoring Gumbel's bivariate exponential (GBE) data. Some tables are provided to determine the optimal design parameters of the proposed MCUSUM GBE chart. Furthermore, both zero‐state and steady‐state properties of the proposed MCUSUM GBE chart for the raw and the transformed GBE data are compared with the multivariate exponentially weighted moving average (MEWMA) chart and the paired individual cumulative sum (CUSUM) chart. The results show that the proposed MCUSUM GBE chart outperforms the other two types of control charts for most shift domains. In addition, an extension to Gumbel's multivariate exponential (GME) distribution is also investigated. Finally, an illustrative example is provided in order to explain how the proposed MCUSUM GBE chart can be implemented in practice. [ABSTRACT FROM AUTHOR]

The inclusion of correlated auxiliary variables into the monitoring scheme of quality characteristic of interest has gained notable attention in recent statistical process control (SPC) literature. Several authors have investigated the use of a correlated auxiliary variable for efficient monitoring of variability in Phase II of SPC. This phase is generally used to detect any shifts in the expected behavior of the process parameters which are often estimated from the historical in‐control process in Phase I. However, no study has investigated the performance of auxiliary‐based variability charts in Phase I of SPC. Here, we propose auxiliary‐based dispersion control charts in Phase I of SPC. The auxiliary information is considered in the forms of regression, ratio, exponential, and power‐ratio forms. The performance of the variability charts is evaluated and compared using probability to signal as a performance measure. An illustrative example is also provided to show the application of the charts. This study will provide practitioners with appropriate recommendations on the choice of dispersion charts for Phase I analysis. [ABSTRACT FROM AUTHOR]

In multivariate statistical process control, it is recommendable to run two individual charts: one for the process mean vector and another one for the covariance matrix. The resulting joint scheme provides a way to satisfy Shewhart's dictum that proper process control implies monitoring both process location and spread. The multivariate quality characteristic is deemed to be out of control whenever a signal is triggered by either individual chart of the joint scheme. Consequently, a shift in the mean vector can be misinterpreted as a shift in the covariance matrix and vice versa. Compelling results are provided to give the quality control practitioner an idea of how joint schemes for the mean vector and covariance matrix are prone to trigger misleading signals that will likely lead to a incorrect diagnostic of which parameter has changed. [ABSTRACT FROM AUTHOR]

A number of recent research studies have applied queueing theory as an approximate modeling tool to mathematically describe industrial systems, which include manufacturing, distribution, and service, for instance. Among the main observable characteristics in queues, the number of users in the system can be controlled to keep waiting times as minimal as possible. The design of efficient control charts is an attempt to monitor and control such systems. Control charts are proposed to monitor infinite queues with Markovian arrivals, exponential service times, and s identical parallel servers. The proposed charts monitor traffic intensities, which are the ratio between the arrival rate and the service rate, estimated through the number of users in the queueing system at random epochs. The effectiveness and efficiency of the proposed approaches in terms of the average run lengths are established by a comprehensive set of Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Profile monitoring is a technique to test the stability of the relationship between a response variable and explanatory variables over time. The most relevant linear profile monitoring methods have been constructed using the normality assumption. However, the normality assumption could be violated in many quality control applications. In this study, we consider a situation in which the random errors in a linear profile model follow a skew‐normal distribution. The skew‐normal distribution is a generalized version of the normal distribution. A new Shewhart‐type chart and exponentially weighted moving average (EWMA) chart, named the ShewhartR and EWMAR charts, respectively, are constructed based on residuals to monitor the parameters of linear profile model. The simulation results show that the multivariate EWMA chart is sensitive to the normality assumption and that the proposed ShewhartR and EWMAR charts have good ability to detect big and small‐to‐moderate process shifts, respectively. An example using photo mask techniques in semiconductor manufacturing is provided to illustrate the applications of the ShewhartR and EWMAR charts. [ABSTRACT FROM AUTHOR]

This paper studies how Cognitive Reliability Error Analysis Method (CREAM) can add value to the existing Quality Management System at the International Thermonuclear Experimental Reactor. We tested the CREAM framework on a sample of 71 nonconformance reports between 2016 and 2018 from the International Thermonuclear Experimental Reactor Document Management Database. All the nonconformances described in these reports relate to the incidents connected to the welding process of the vacuum vessel components. For failure categories like wrong identification, lack of attention, memory loss, or inadequate planning, CREAM provides additional information to redesign the work space. Moreover, CREAM identified quality control and management control as main underlying weaknesses that cause nonconformities. Future analysis should focus more on organizational error causes and resulting indirect links. Also, the use of a methodology like CREAM needs to be provided as special training by experts throughout all areas of activities. [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]

In many applications, it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. We propose and investigate the use of the degree corrected stochastic block model (DCSBM) to model and monitor dynamic networks that undergo a significant structural change. We apply statistical process monitoring techniques to the estimated parameters of the DCSBM to identify significant structural changes in the network. We apply our surveillance strategy to a dynamic US Senate covoting network. We detect significant changes in the political network that reflect both times of cohesion and times of polarization among Republican and Democratic party members. Our analysis demonstrates that the DCSBM monitoring procedure effectively detects local and global structural changes in complex networks, providing useful insights into the modeled system. The DCSBM approach is an example of a general framework that combines parametric random graph models and statistical process monitoring techniques for network surveillance. [ABSTRACT FROM AUTHOR]

In quality control applications, the control chart is a powerful tool but its performance is adversely affected by the contamination from either the inspector or the measuring device leading to measurement errors. In this paper, we investigate the performance of the AEWMA median chart with measurement errors, and a methodology is proposed to obtain the optimal parameters by considering a linearly covariate error model. Several figures and tables show that, with the existence of measurement errors, the efficiency of the AEWMA median chart can be strongly affected, but this negative effect can be compensated by taking multiple measurements at each sample point. Comparisons with the Shewhart and the classical EWMA schemes confirm the superiority of the AEWMA scheme for detecting a wide range of shifts in the case of precise and imprecise data. An example is provided to illustrate the use of the AEWMA median chart with measurement errors. [ABSTRACT FROM AUTHOR]

In the literature, coefficient of variation control charts have been introduced under the assumption of no measurement errors. However, measurement errors always exist in practice, and they do affect the performance of control charts in the detection of an out‐of‐control situation. In this paper, we therefore study the performance of a coefficient of variation Shewhart‐type control chart (Shewhart‐CV chart) and also one‐sided coefficient of variation exponentially weighted moving average–type control charts (EWMA‐γ2 charts) using a model with linear covariates. Moreover, we propose and study the performance of a two‐sided EWMA‐γ2 chart using a model with linear covariates. Several figures and tables are provided and analyzed to evaluate the statistical performance of these control charts for different sources of measurement errors. The obtained results show that the precision and accuracy errors significantly affect the performance of both the Shewhart‐CV and EWMA‐γ2 control charts. An example illustrating the use of this study is finally presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new control chart that integrates a powerful high‐dimensional covariance matrix test with the exponentially weighted moving average procedure for monitoring high‐dimensional variability with individual observations. Design and implementation of the proposed chart are provided, including search algorithm and a table for the control limits, diagnostic aids after the signal, effect of misspecifying the in‐control distribution, and a bootstrap procedure. Monte Carlo simulation results show that the new chart, with its powerful inherited properties, provides satisfactory performance in various cases, especially for covariance shifts that involve diagonal components. The application of the proposed method is illustrated with a real data example from a white wine production process. [ABSTRACT FROM AUTHOR]

The sign SN distribution‐free control charts are efficient and simple online tools to perform quality monitoring when the normality of observations cannot be assumed a priori. The possibility of designing an SN control chart whichever is the distribution of observations allows it to be implemented with success in processes with finite production horizon (FPH). Today, FPH processes are widespread in industry. Recently, Shewhart SN control charts have been investigated in literature for this kind of quality control problem. In this paper, we investigate the implementation of an exponentially weighted moving average (EWMA) SN control chart for FPH processes. The property to have varying control limits at each inspection allows the EWMA SN control chart to be very sensitive to shifts in the location. A nonhomogeneous Markov chain model is used to evaluate the statistical properties of the EWMA SN control chart with varying control limits. An illustrative example shows the implementation of the EWMA SN control chart with observations collected from a quality monitoring procedure in a company bottling soft drinks. [ABSTRACT FROM AUTHOR]

Processes with a finite number of scheduled inspections are very common in industry. Frequent setup changeovers in mass production aimed at switching from one product code to another and the growing need to manufacture small lots of customized products are challenging issues for quality engineers who want to perform statistical process monitoring (SPM). In this paper, we propose a Shewhart control chart plotting a statistic defined as the ratio F of the between‐sample variability over the within‐sample variability. The F control chart is able to monitor the stability of the location for a normally distributed quality parameter. Being the F statistics autocorrelated, the statistical performance has been evaluated by means of simulation. The numerical analysis shows that the F control chart outperforms the available t control chart for monitoring finite production horizon processes. [ABSTRACT FROM AUTHOR]

The Taguchi approach for robust design has been a common practice in industrial experimentation for many years. However, these designs possess serious disadvantages such as the inability to estimate control × control interactions. In this article, we propose the application of the R3 algorithm as an augmentation tool for Taguchi experiments. The augmented Taguchi designs were compared with its competitors, mixed resolution designs, and D‐optimal augmentation, using performance indicators. The results showed that Taguchi designs augmented with the R3 algorithm are capable of estimating control × control interactions, possess similar values for performance indicators when compared with other techniques, and in most cases require less runs. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a new model for Non‐Conformity Management, aimed at overcoming the limitations occurring when dealing with Non‐Conformities through traditional methods like Root Cause Analysis. The need to suggest a new approach to Non‐Conformities Management is connected to those situations where a large variety of Non‐Conformities is recorded, with a significant number of low‐impacting ones; addressing them directly through methods like Root Cause Analysis would often be not economically viable. In this context, the paper discusses Holistic Non‐Conformities Reduction (HNCR) approach as a suitable option to address this kind of business situations. In particular, the contribution of this paper is related to the development of HNCR model and the evaluation of how to properly structure the model and the flow that shall be followed. By clustering Non‐Conformities into flexible categories to be easily modified, expanded and rearranged through time, the HNCR model allows to identify new critical Non‐Conformity families, otherwise hardly detectable. A proof of concept relative to HNCR deployment is presented: 2 different scenarios are introduced, the first being the Non‐Conformities management within the extended supply chain of a large pharmaceutical distribution centre, while the second deals with Non‐Conformities in the context of a company's new programs development. [ABSTRACT FROM AUTHOR]

Abstract: Recently, the monitoring of compositional data by means of control charts has been investigated in the statistical process control literature. In this article, we develop a Phase II multivariate exponentially weighted moving average control chart, for the continuous surveillance of compositional data based on a transformation into coordinate representation. We use a Markov chain approximation to determine the performance of the proposed multivariate control chart. The optimal multivariate exponentially weighted moving average smoothing constants, control limits, and out‐of‐control average run lengths have been computed for different combinations of the in‐control average run lengths and the number of variables. Several tables are presented and enumerated to show the statistical performance of the proposed control chart. An example illustrates the use of this chart on an industrial problem from a plant in Europe. [ABSTRACT FROM AUTHOR]

Abstract: In this article, we study Shewhart and exponentially weighted moving average control charts for monitoring the mean or, equivalently, the percentiles of a Weibull process when additional sources of variation, also known as variance components, are present. We adopt a frailty model to describe the monitored process. We derive analytical properties for this model and use them to develop control charts. We consider charts for the sample mean and exponentially weighted moving averages. We compare their average run length performances to their traditional counterparts when they do not account for variance components. [ABSTRACT FROM AUTHOR]

Abstract: The coefficient of variation (CV) of a population is defined as the ratio of the population standard deviation to the population mean, which can be regarded as a measure of stability or uncertainty and can also indicate the relative dispersion of data to the population mean. This paper proposes a new exponentially weighted moving average chart for monitoring CV, which is constructed by truncating those negative normalized observations to 0 in the traditional exponentially weighted moving average CV statistics. The implementation and optimization procedures of the proposed chart are presented. The new chart is compared with some existing CV charts by means of average run length, and the comparison results show that the new chart outperforms other charts in most cases. Two examples illustrate the use of this chart on real data gathered from a metal sintering process and from a die casting hot chamber process. [ABSTRACT FROM AUTHOR]

Abstract: The Sequential Probability Ratio Test (SPRT) is widely used in the field of reliability and quality control. This paper is a continuation and a significant extension of the authors' earlier paper; it is dedicated to various risk ratios (α/β) and will lead to the increased use of the Sequential Probability Ratio Test for practical and research needs. The sample number (SN) until the test stops is a random value, and its distribution tails can be extremely long relative to the average SN (ASN). This is not suitable for practical use; therefore, truncation is required, usually by a pair of lines whose intersection, denoted as the Truncation Apex (TA), determines the maximum SN (maxSN). The optimality of the test is determined by the minimality of the SN (by means of maxSN and ASN) for a given Operating Characteristic. Presented are formulas and an algorithm for the TA and other parameters of the optimal test stopping boundaries for various α/β. This methodology also shortens the test planning process. Displacement of the TA from the optimal location results in a significant increase in ASN. The study was implemented in the Israeli standard SI‐61123. Revision of IEC 61123 and IEC 61124 (for exponential distributed data), by this study, has been accepted to the work plan of TC‐56 of IEC. The proposed methodology can be the basis for the improvement of additional standards, for example, in ISO 8422:2006. [ABSTRACT FROM AUTHOR]

Abstract: Change point estimation is a useful concept in time series models that could be applied in several fields such as financing, quality control. It helps to decrease costs of decision making and production by monitoring stock market and production lines, respectively. In this paper, the maximum likelihood technique is developed to estimate change point at which the stationary AR(1) model changes to a nonstationary process. Filtering and smoothing of dynamic linear model are used to estimate unknown parameters after change point. We also assume that correlation exists between samples' statistics. Simulation results show the effectiveness of the proposed estimators to estimate the change point of stationary. In addition based on Shewhart control chart, filtering has a better accuracy in comparison to smoothing. A real example is provided to illustrate the application. [ABSTRACT FROM AUTHOR]

In Phase I analysis, data are used retrospectively to check process stability and to establish limits that will be later used in Phase II for the prospective monitoring. It is clear that the results of Phase I analysis are of crucial importance for the right interpretation of control chart during Phase II. Many of recent papers devoted to Phase I analysis discuss the accuracy of limits found from Phase I data and the impact of these limits' accuracy on the probability of Type I or II errors. Although this is an interesting issue, we think that its importance is overestimated because there is another problem that escapes the attention of many researchers. This is the problem of the order of points in Phase I or the problem of data randomness. We review the role of data succession on right application of simple Shewhart control chart and hope to draw the attention of the statistical community to this problem because it may affect not only Phase I issues but also many other sides of statistical process control. Besides, we suggest a new test for data randomness and give a preliminary estimate of how it works. We also state our thoughts about the possible causes of neglecting the data order and suppose ideas about future researches. [ABSTRACT FROM AUTHOR]

When a factorial design has some missing value but there are contrasts (at least as many as there are missing values) corresponding to interactions that can be considered negligible, equating the expressions of those contrasts to 0 allows the missing values to be deduced. This procedure allows estimating values that correspond to runs that have gone wrong or could not be performed as planned, as well as to reduce the experimental plan while saving some runs whose results can be estimated a posteriori. The problem is that the estimated values have a variance that is higher than that of the experimental runs, and the variance depends on which are the missing values and the contrasts used to make the estimate. This article performs a thorough analysis of the variances obtained by estimating up to 2 missing values in 8-run designs and up to 5 in 16-run designs, with tables included which provide the missing values and contrasts that should be used to obtain estimates that have minimal variability. [ABSTRACT FROM AUTHOR]

Quality of final products depends on several decision variables and design factors from several stages of operations. Multiresponse optimization is a well-grounded method for offline quality design that can consider several inputs and outputs. This study introduces a new methodology for quality optimization in multistage systems with multiple response variables. Several stochastic parameters, including response surfaces and covariates, are to be considered in this methodology. For this purpose, scenario-based multistage stochastic programming models are developed in three cases addressing (1) decision on control factors, (2) decision on control factors as well as targets of intermediate stages, and (3) scenario-based decision on control factors. An example is presented at the end to illustrate the mechanism of the methodology and applicability of the three cases. [ABSTRACT FROM AUTHOR]

This article focuses on monitoring nonparametric profile with time-varying sample sizes and random predictors. Traditional profile monitoring schemes, whose control limits are often determined before the monitoring initiates, are constructed based on perfect knowledge of profile sample sizes and predictors. In practice, however, our foreknowledge about future random sample sizes and predictors is seldom available. An inappropriate assumption or estimation of the sample sizes model and/or predictors distribution function may lead to unexpected performance of traditional control charts. To overcome this problem, we propose a kernel-based nonparametric profile monitoring scheme which integrates the multivariate exponentially weighted moving average procedure with the probability control limits. The success of the proposed chart lies in the use of dynamic control limits which are determined online, essentially aiming at guaranteeing the conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point to meet a pre-specified false alarm rate. The simulation studies show that the proposed control scheme has good in-control and out-of-control performances under various scenarios of time-varying sample sizes and random predictors. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis has been extensively used as a powerful method in a wide range of industries, but many scholars questioned the risk priority number method and proposed some new methods to improve. However, these methods still only evaluated from risks viewpoint while ignoring the effectiveness of corrective actions from utility viewpoint. In practical, it may not reach significant corrective effectiveness because of the corrective efficiency if corrective action is ignored. In addition, corrective actions may be treated as a complex system; they may not be simple and have an independent relationship with each other; the interdependence relationship; and the sequence or structure of hierarchy; even feedback relationship may exist. This study develops a fuzzy interpretive structural model in order to evaluate the structure of hierarchy and interdependence of corrective actions, and integrating the analytic network process with decision-making and trial evaluation laboratory to take account into the causal relationship and influence strength of corrective actions to obtain the more accuracy weights. At last, a case study implemented to demonstrate that the proposed approach effectively circumvents the drawbacks of traditional risk priority number method and adapts flexibly to real-world situations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper is motivated by Food Safety Modernization Act, signed into law by President Obama on Jan. 4, 2011, after several outbreaks of tainted foods have cost human health and contracted food-industry prosperity over the past decade. It develops a situationally sample-size-adjusted procedure, called a tightened-normal-tightened (TNT) sampling strategy, on the basis of yield-index ( S pk) verification of the production process, whose measurements, garnered from a key quality characteristic with bilateral specification limits, follow a normal distribution. In this paper, we firstly describe the sampling procedure of the S pk - TNT( n T, n N, k) inspection scheme whose sets of parameters include sample sizes for the tightened inspection ( nT) and normal inspection ( n N), and the critical value ( k). Those parameters are determined from the joint solution of two nonlinear inequalities complying with the tolerable vendor's risk, buyer's risk, the desired acceptable quality level (AQL) and limiting quality level (LQL). Then, the proposed S pk - TNT( n T, n N, k) schemes for several inspection standards, widely utilized in practice, are delineated, and their functioning behaviors and propensities are further investigated. Moreover, we compare the S pk - TNT( n T, n N, k) strategy with two unadjusted inspection schemes, S pk-tightened, S pk - T( n T, k), and S pk-normal, S pk - N( n N, k), to illustrate its inspection flexibility and efficiency in real applications. Finally, implementing with a vendor-buyer contract agreement using the S pk - TNT( n T, n N, k) sampling plan, a company inspecting the submitted universal-serial-bus (USB) connectors demonstrates the applicability of our proposed methodologies. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The variable sample size (VSS) [ABSTRACT FROM AUTHOR]

No abstract is available for this article. [ABSTRACT FROM AUTHOR]

Minimum resolution IV (MinResIV) and Plackett and Burman (PB) designs are popular screening designs because of their run size efficiency and good projection properties. The purpose of this investigation is to find out which projection properties that can be maintained when these designs are blocked and also the efficiency by which the effects of interest can be estimated. MinResIV designs with 10-20 runs and PB designs with 12 and 20 are investigated. For the PB designs, design factor columns were used as blocking columns, while for the MinResIV designs, the main rule consisted of finding good arrangements for allocating mirror-image pair runs to the same blocks, a method inspired from Jacroux. As a criterion for a good blocking arrangements, we used maximum D s - efficiency, which we found to be more generally applicable and provide better projection properties than previous suggested methods. It came out that for most of these designs, it was possible to find blocking schemes satisfying that for any three factors, either all main effects and their interactions or all main effects and their two-factor interactions could be estimated with a reasonable high efficiency when blocked. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Process capability analysis is an important aspect of quality control. Various process capability indices are proposed when the distribution is normal. For non-normal cases, percentile and yield-based indices have been introduced. These two methods use partial features of a process distribution, such as key percentiles and the proportion of non-conforming (PNC) to estimate the process capability. However, these local features may not reflect the uniformity of a process appropriately when the distribution is non-normal. In this paper, continuous ranked probability score (CRPS) is introduced to process capability analysis and a CRPS-based approach is proposed. This method can assess the dispersion of process variation across the overall distribution and is applicable to any continuous distribution. An example and simulations show that CRPS-based indices are more stable and accurate indicators of process capability than the existing indices in reflecting the degree of process fluctuation. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Quality control charts based on exponentially weighted moving average (EWMA) has been widely used for monitoring continuous process data. However, many quality characteristics of interest are in the form of counts for nonconformities and are often monitored by a Poisson model. In this article, we introduce a new design structure for the Poisson EWMA charts for monitoring Poisson processes. The proposed scheme is based on a well-structured sampling technique, ranked set sampling instead of the traditional simple random sampling. Using Monte Carlo simulations, we compute the run length properties of the new Poisson EWMA chart and compare their relative performance with the existing schemes for monitoring increases and decreases at the Poisson rate. It is found that the new scheme significantly improves the classical procedures for detecting changes in the Poisson processes. Finally, we illustrate the practical application of the proposed scheme through numerical example. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In some manufacturing processes, complex profiles are collected to characterize quality status. However, some of these profiles may have unequal lengths, which makes the attempt of directly comparing them difficult. In addition, when a shift occurs in a profile, it usually affects a segment of continuously connected observations. That is, local shifts instead of global shifts are frequently seen. As shift signals are easily mixed with allowable mean trends, statistical monitoring of such unaligned profiles becomes a challenging task. In this paper, we propose a framework for monitoring profiles with unequal lengths. The profiles are first aligned using a modified robust dynamic time warping algorithm, which is insensitive to local mean shifts. Penalization-based methods are then used to estimate profile means. Finally, mean estimates are utilized in a likelihood ratio test statistic for effective monitoring. Both simulation studies and a real example are used to demonstrate the effectiveness of the proposed monitoring procedure. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Solar energy is a fast growing energy source and has allowed the development of efficient, affordable, and easy-to-install photovoltaic systems over the years. Solar energy stakeholders are, however, concerned with sudden deterioration of photovoltaic systems' performance. Thus, effective change-point detection in solar panel performance analysis is essential for better harnessing solar energy and making photovoltaic systems more efficient. In particular, this study focuses on retrospectively identifying the time points of abrupt changes. Because the power generations from the solar panels are affected by a wide variety of factors, it is very difficult, if not impossible, to find a parametric model to detect abrupt changes in the power generation. We present a nonparametric detection method based on thresholded least absolute shrinkage and selection operator. The proposed method has low computational complexity and is able to accurately detect performance changes while being robust against false detection under noisy signals. The performance of the proposed method in detection of abrupt changes is evaluated and compared with state-of-the-art methods through extensive simulations and a case study using data collected from four solar energy facilities. We demonstrate that the proposed method is superior to benchmark methods. The proposed method will help solar energy stakeholders in several aspects including operations planning, maintenance scheduling, warranty underwriting, and cost-benefit analysis. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Profile monitoring is useful whenever the quality is described by a relationship between a response and one or more of explanatory variables. It is conventional to refer to this relationship as Quality Profile. Despite the fact that there are numerous examples of the polynomial quality profiles in industrial practice, only few Phase II techniques are available in literature for monitoring these profiles. However, to promote this research context, we extend the applications of the double exponentially weighted moving average (dEWMA) statistic to include phase II of the Mth polynomial quality profiles. Some of existing methods have been modified in order to capture the nature of the dEWMA statistic under the polynomial profile monitoring. Their detection accuracy measured by the average run length performance is evaluated. To expand our comparison, the average run length performance of the multivariate cumulative sum and T2 are also subjected to evaluation as well. The comparative simulations showed the superior performance of the dEWMA-based charting techniques in detecting changes in the parameters of the polynomial quality profiles. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A profile is a relationship between a response variable and one or more independent variables that can describe the quality of a process or product. On the other side, for an in-control process, capability indices are the criteria for process quality improvement that allows meeting customer expectations. Recently, evaluating the process capability of profiles has been investigated by some researchers. In all of these efforts, the response variable in the profile follows a normal distribution. However, sometimes, this assumption is violated, and the response variable may follow a binary or binomial distribution. In this paper, we propose two methods to measure the process capability when the quality of a process is characterized by a logistic regression profile. The performance of the proposed indices is evaluated through simulation studies. Finally, the application of the proposed methods is illustrated through a real case. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In most quality control applications, the errors generated from measurement system can adversely affect the ability of control charts in detecting out-of-control conditions. In this paper, the effect of measurement error with linearly increasing-type variance on the performance of maximum exponentially weighted moving average and mean-squared deviation (MAX-EWMAMS) control chart is studied. For this purpose, different out-of-control scenarios including mean shifts, variance shifts, and simultaneous shifts in both are considered, and the detecting performance of the proposed approach is investigated through simulation study. The results of simulation study in terms of three criteria including average run length, standard deviation of run lengths, and the empirical distribution of run lengths prove that the measurement error with linearly increasing-type variance can adversely affect the performance of MAX-EWMAMS control. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Process capability indices provide a measure of the output of an in-control process that conforms to a set of specification limits. These measures, which assume that process output is approximately normally distributed, are intended for measuring process capability for manufacturing systems. When the performance of a system results in a product that fails to fall within a given specification range, however, the product is typically scrapped or reworked, and the actual distribution that the customer perceives after inspection is truncated. In this paper, the concept of a truncated measure for three types of quality characteristics is introduced as the key to linking customer perception to process capability. Subsequently, a set of customer-perceived process capability indices is presented as an extension of traditional manufacturer-based counterparts. Finally, data transformation-based process capability indices are also discussed. A comparative study and numerical example reveal considerable differences among the traditional and proposed process capability indices. It is believed that the proposed process capability index for various quality characteristics may more aptly lead to process improvement by facilitating a better understanding of the integrated effects found in engineering design problems. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]