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Control charts are widely used in the manufacturing and service sectors to track, regulate, and enhance process output. A manufacturing industry desires to utilize a control chart that has an effective structure and is sensitive to detect infrequent variations in the process. Generally, control charts are developed with the presumption that the understudy quality variable is normally distributed. In actual application, many processes have skewed distributions. The purpose of this study is to use the moving average (MA) charts to track the dispersion of a log‐normal distribution. The design of the proposals is developed and the performance is assessed by run‐length properties. The cumulative distributions of run‐length under in‐control and out‐of‐control are provided to have a broad view of the performance. The simulation findings show that when the value of the log‐normal dispersion parameter is large, the proposed chart is more sensitive to the changes in the dispersion. Additionally, an industrial application is given to illustrate the suggested charts in this research. [ABSTRACT FROM AUTHOR]

To identify small‐to‐moderate shifts (also known as outliers and anomalies) in the process parameters (location and/or dispersion), the classical cumulative sum (CUSUM) and exponential weighted moving average (EWMA) control charts are famous. The traditional CUSUM control chart is less capable to detect out‐of‐control signals of ±$ \pm $1.0 standard deviation shifts. The advanced versions of the traditional CUSUM and EWMA control charts are ACUSUM (adaptive CUSUM) and AEWMA (adaptive EWMA) control charts, respectively. The aforementioned issue of the traditional CUSUM control chart can be addressed through the ACSUUM control chart. It also identifies various sizes of shifts in the process parameters. In order to efficiently identify and remove outliers and anomalies (i.e., shifts) from datasets, the score functions are typically used to establish ACUSUM control charts. The goal of the study is to propose a new ACUSUM control chart based on the Hampel (score) function and it is symbolized as ACUSUMHampel${\mathrm{ACUSU}}{{\mathrm{M}}_{{\mathrm{Hampel}}}}$ control chart. Different sizes of shift in the process location parameter can be distinguished using the ACUSUMHampel${\mathrm{ACUSU}}{{\mathrm{M}}_{{\mathrm{Hampel}}}}$ control chart. The primary novelty is the substitution of a proposed time‐varying parameter for the typical CUSUM control chart's prioritization of the process characteristic by appropriate weights. The proposed ACUSUMHampel${\mathrm{ACUSU}}{{\mathrm{M}}_{{\mathrm{Hampel}}}}$ control chart's run length feature is obtained through the Monte Carlo simulation technique for zero‐state and steady‐states. For evaluating a single shift, performance measures like average run length (ARL), standard deviation of run length (RL), and different percentiles of run length are preferred. However, for evaluating a range of shifts, extra quadratic loss, relative average run length, and performance comparison index are used. Comparison based on performance measures shows the superiority of the proposed ACUSUMHampel${\mathrm{ACUSU}}{{\mathrm{M}}_{{\mathrm{Hampel}}}}$ control chart against the classical CUSUM and EWMA, ACUSUM, AEWMA, mixed EWMA‐CUSUM, and mixed CUSUM‐EWMA control charts. Furthermore, the proposed ACUSUMHampel${\mathrm{ACUSU}}{{\mathrm{M}}_{{\mathrm{Hampel}}}}$ control chart is also implemented with real‐life data which is taken from cost of medical insurance to demonstrate its competency in statistical process control field for quality engineers, practitioners, experts, and researchers. [ABSTRACT FROM AUTHOR]

Quality control (QC) is a systematic approach to ensuring that products and services meet customer requirements. It is an essential part of manufacturing and industry, as it helps to improve product quality, customer satisfaction, and profitability. Quality practitioners generally apply control charts to monitor the industrial process, among many other statistical process control tools, and to detect changes. New developments in control charting schemes for high-quality monitoring are the need of the hour. In this paper, we have enhanced the performance of the mixed homogeneously weighted moving average (HWMA)-cumulative sum (CUSUM) control chart by using the auxiliary information-based (AIB) regression estimator and named it MHCAIB. The proposed MHCAIB chart provided an unbiased and more efficient estimator of the process location. The various measures of the run length are used to judge the performance of the proposed MHCAIB and to compare it with existing AIB charts like CUSUMAIB, EWMAAIB, MECAIB (mixed AIB EWMA-CUSUM), and HWMAAIB. The Run length (RL) based performance comparisons indicate that the MHCAIB chart performs relatively better in monitoring small to moderate shifts over its competitor's charts. It is shown that the chart's performance improves with the increase in correlation between the study variable and the auxiliary variable. An illustrative application of the proposed MHCAIB chart is also provided to show its implementation in practical situations. [ABSTRACT FROM AUTHOR]

A control chart has become a choice of quality practitioners for monitoring the output of industrial and production processes. It is a common practice to develop control charts under normality assumption or known distribution of the quality characteristic(s). These control charts are known as parametric charts. These charts may provide misleading results when the normality assumption of the process distribution is doubtful or unknown. In such situations, the nonparametric (NP) charts serve as an effective alternative for efficient monitoring of the process parameter(s). In this study, we have proposed a new NP double progressive mean chart based on sign statistic for detecting small shifts in the process location. The run-length (RL) properties of the proposed chart have been computed and compared with the existing competitor charts. We have used the most popular performance measure namely average RL (ARL) as an evaluation criterion. The comparisons revealed that the proposed chart performs better than its existing competitor's charts. We have also considered a real-life data set related to a piston ring manufacturing process for the practical implementation of the proposed and existing charts. [ABSTRACT FROM AUTHOR]

In every process of the manufacturing and service industry, there exist variations. Quality practitioners desire to avoid these variations to maintain and improve the quality level of products and services. The control charts are commonly applied under the assumption of normality, however, when the underlying assumption is not valid, nonparametric (NP) control charts become alternatives for quality engineers and practitioners. In this article, an NP progressive mean control chart based on Wilcoxon signed-rank statistic (NPPM-SR) has been proposed for prompt detection of shifts in the process target. The NPPM-SR chart proves in-control robust and much effective performance in spotting deviations in the process location for heavy-tailed and skewed distributions. The run-length distribution performance of the proposed NPPM-SR chart for some selected continuous symmetrical distributions; normal, t, Laplace, logistic and contaminated normal distributions are evaluated under zero- and steady states using average run length (ARL) and some other run-length characteristics. The proposed NPPM-SR chart is compared with existing NP counterparts such as; NP exponentially weighted moving average sign (NPEWMA-SN) chart, NP EWMA based on Wilcoxon signed-rank statistic (NPEWMA-SR) chart and NP cumulative sum based on Wilcoxon signed-rank statistic (NPCUSUM-SR) chart. For the comparative analysis, the parametric competitors such as; traditional EWMA (EWMA- X ¯ ) chart and Double EWMA- X ¯ (DEWMA- X ¯ ) control charts are also included in this study. The proposed NPPM-SR charting scheme under zero-state has been found efficient as compared to steady-state and its existing counterparts. The piston rings data and simulated data have been taken for the illustration of the proposal. [ABSTRACT FROM AUTHOR]

In statistical process control (SPC), the control chart is a quite popular technique to monitor the process efficacy. From a statistical point of view, the control chart is considered superior if it has an effective structure withholding property of the resistance against infrequent situations in a practical environment. The current study is designed for the same purpose for observing the dispersion parameter of log‐normal distribution by using the structure of an exponentially weighted moving average (EWMA) chart. For the extensive study, EWMA range, EWMA standard deviation, EWMA Qn,${Q_n},$ EWMA Sn,${S_n},$ EWMA mean absolute deviation, and EWMA transform standard deviation control charts are proposed. Properties of the run‐length profile of the proposed designed structures based on different existing estimators as well as a newly transformed dispersion estimator for log‐normal standard deviation are evaluated. The results indicate that the newly developed scheme outperforms the competitors when the dispersion parameter of the log‐normal distribution attains a large value. A real‐life application is also provided to validate how the developed design can be used in practice. [ABSTRACT FROM AUTHOR]

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

The assumption of normally distributed data is emerged with several statistical inferences as well as in statistical process control. But various real practices on data in different fields like Biological sciences, health, production processes, and manufacturing industries exhibit non‐normal behavior. The current study is concerned with developing robust adaptive exponentially weighted moving average (AEWMA) control charts to monitor the location of non‐normal environments. In the current study, four estimators are considered and listed as Mean (Y¯), Mid‐range (MR), Median (Y∼)and Trimean (TM) for observing process target. Robust proposals of the said schemes are scrutinized towards symmetric non‐normal (t and Laplace) and skewed (Log‐normal and Gamma) environments. The average of run‐length and standard deviation of run‐length are taken as performance evaluation measures. Additionally, some percentile points of distribution run length are also reported for a better understanding of run‐length distribution. Corrected design constants of the proposed charts are also provided for mentioned distributions. Implementation of the proposed schemes is illustrated by providing examples related to real practice. [ABSTRACT FROM AUTHOR]

The nonparametric (NP) control charts are famous for detecting a shift in the process parameters (location and/or dispersion) when the underlying process characteristic does not follow the distributional assumptions. Similarly, when the cost of estimations is very high and the ranking of observational is relatively simple, the ranked set sampling (RSS) technique is preferred over the simple random sampling (SRS) technique. On the other hand, the NP triple exponentially weighted moving average (EWMA) control chart based on SRS is superior to the NP EWMA and NP double EWMA (NP DEWMA) based on the SRS technique to detect a shift in the process location. This study designed an advanced form of NP TEWMA Wilcoxon signed-rank based on RSS, denoted as TEWMA − SR RSS control chart to identify a shift in the process location parameter. The Monte Carlo simulation method is used to assess the performance of the proposed TEWMA − SR RSS control chart along with SRS-based NP TEWMA (TEWMA-SR), SRS-based NP TEWMA sign (TEWMA-SN), SRS-based TEWMA − X ¯ , and RSS-based NP DEWMA-SR DEWMA − SR RSS control charts. The study shows that the proposed TEWMA − SR RSS control chart is more efficient in identifying shifts (especially in small shifts) in the process location than the existing control charts. Finally, a real-life application is also provided for the practical implementation of the proposed TEWMA − SR RSS control chart. [ABSTRACT FROM AUTHOR]

The homogeneously weighted moving average (HWMA) control chart is famous to identify small deviations in the process mean. The plotting statistic of the HWMA chart assigns equal weight among the previous samples as compared to the plotting statistic of the exponentially weighted moving average chart. We propose a new HWMA chart that uses the plotting statistic of the cumulative sum chart. The run length performance of the proposed chart is measured in terms of the average, the standard deviation, some percentile points, and compared with some existing counterparts' charts. The comparison shows that the proposed chart performs superior to their existing counterparts. An application based on a real‐life dataset is also presented. [ABSTRACT FROM AUTHOR]

In practical situations, the underlying process distribution sometimes deviates from normality and their distribution is partially or completely unknown. In that instance, rather than staying with/depending on the conventional parametric control charts, we consider non‐parametric control charts due to their exceptional performance. In this paper, a new non‐parametric double homogeneously weighted moving average sign control chart is proposed with the least assumptions. This chart is based on a sign test statistic for catching the smaller deviations in the process location. Run‐length (RL) properties of the proposed chart are studied with the help of Monte Carlo simulations. Both in‐control and out‐of‐control RL properties show that the proposed chart is a better contender as compared to some existing charts from the literature. A real‐life application for practical consideration of the proposed chart is also provided. [ABSTRACT FROM AUTHOR]

Toxicant testing is a major component of the environmental solution testing program. This test system aims to evaluate the sensitivity of the test species, evaluate comparability between laboratory test results, and classify possible variability sources such as healthy organisms, organism batches, laboratory water, and food quality changes. For the continuous assessment of sensitivity and accuracy of laboratory toxicity tests, control charts are often used. The cumulative sum (CUSUM) charting structure is a popular monitoring tool that is often used due to its outstanding ability in recognizing small‐to‐moderate unusual changes in process parameters. The CUSUM control charts have now been widely accepted/applied for monitoring the process dispersion under the assumption that the process does not have outliers. In practice, most of the processes have outliers. Therefore, the use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. Hence, in this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in the variable LC50 by using quantitative structure–activity relationship (QSAR) aquatic toxicity data set. The proposed IQR‐CUSUM charts are compared to its counterparts by using a variety of main output run‐length functions, such as average run length (ARL), standard deviation of run length (SDRL), and extra quadratic loss (EQL), derived from the distribution of run‐length. Further, results revealed that the proposed CUSUM charts have better detection ability as compared to existing counterpart charts, both in theoretical and practical considerations. Control charts are often used for process monitoring. Cumulative sum (CUSUM) charts are popular monitoring tool. The use of the interquartile range (IQR) as a plotting statistic is an attractive choice for dispersion monitoring. In this study, the CUSUM control chart with auxiliary information‐based IQR estimators is proposed and employed to monitor dispersion or variation in LC50 by using quantitative structure‐activity relationship (QSAR) aquatic toxicity data set. The results revealed that the proposed charts outer performed as compared to existing counterpart charts. [ABSTRACT FROM AUTHOR]

Memory‐type control charts play a significant role to identify slight changes in the parameters of the production process. In this article, we have proposed a new cumulative sum chart that utilizes the statistic of the homogeneously weighted moving average chart. The performance of the proposed chart is studied using Monte Carlo simulations. The proposed chart is compared with some existing charts under different run length profiles. The run length profile comparisons reveal that the proposed chart performs superior as compared to the existing control charts. A real‐life application using a manufacturing process dataset is also part of this study. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) plays a vital role in the maintenances and improvements of quality outputs in manufacturing, industrial and service production processes. Control chart is an important SPC tool, used to detect noises and to improve process performance. When the underlying process distribution lacks the assumption of normality, nonparametric (NP) control charts become essential and particularly are useful because their in-control (IC) run length properties remain the same for every continuous distribution. This article develops the NP progressive mean sign (NPPM-SN) chart for monitoring the process target through 100% inspection by taking individual measurements from the process. The performance of the proposed NPPM-SN chart is examined under zero-state and steady-state scenarios. The IC and out-of-control run length properties of the proposed control chart are evaluated using Monte Carlo simulation. The proposed NPPM-SN chart is compared with traditional exponentially weighted moving average (EWMA), nonparametric EWMA sign (NPEWMA-SN) and traditional progressive mean (PM) control charts based on individual measurements using average run length and some other characteristics of the run-length distribution. The proposed NPPM-SN chart is found more robust (for all distributions) and efficient (for skewed distributions) as compared to its competitors. Along with a real-life example related to high voltage power supply, a simulated data example is also presented for the implementation of the proposed NPPM-SN chart. [ABSTRACT FROM AUTHOR]

Recently, a new double progressive mean (DPM) control chart has been proposed in the literature of statistical process control. In the said proposal, an important term is missing in the variance expression of the DPM statistic that affects the detection ability of the proposed chart. In this study, we have derived and provided the correct version of the said variance along with its corresponding control limits. The run length profiles of the DPM chart are investigated, and the results are updated for the new version of the limits. Moreover, a sensitivity analysis between DPM and progressive mean charts based on the different choices of the design parameter is also included in this study. It is revealed that the revised version offers even more efficient outcomes than the previous ones. In addition, a real dataset application is also presented for practical considerations of the refined version. [ABSTRACT FROM AUTHOR]

The exponentially weighted moving average (EWMA) control chart is a memory chart that is widely used in process monitoring to spot small and persistent disturbances in the process parameter(s). This chart requires normality of the quality characteristic(s) of interest and a smaller choice of smoothing parameter. Any deviations from these conditions affect its performance in terms of efficiency and robustness. For the said two concerns, this study develops a new mixed EWMA chart under progressive setup (mixed EWMA–progressive mean [MEP] chart). The proposed MEP chart combines the advantages of robustness (under nonnormal scenarios) and high sensitivity to small and persistent shifts in the process mean. The performance of the proposed MEP control chart is evaluated in terms of average run length and some other characteristics of run length distribution. The assessment of the proposed chart is made under standard normal, student's t, gamma, Laplace, logistic, exponential, contaminated normal and lognormal distributions. The performance of the proposed MEP chart is also compared with some existing competitors including the classical EWMA, the classical cumulative sum (CUSUM), the homogenously weighted moving average, the mixed EWMA–CUSUM, the mixed CUSUM–EWMA and the double EWMA charts. The analysis reveals that the proposal of this study offers a superior design structure relative to its competing counterparts. An application from substrates manufacturing process (in which flow width of the resist is the key quality characteristic) is also provided in the study. [ABSTRACT FROM AUTHOR]

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

Investigation and removal of unnatural variation in the processes of manufacturing, production and services require application of statistical process control. Control charts are the most famous and commonly used statistical process control tools to trace changes in the manufacturing and nonmanufacturing processes parameter(s). The nonparametric control charts become necessary when the distribution of underlying process is unknown or questionable. The nonparametric charts are robust alternative along with holding property of quick shift detection ability in process parameter(s). In this article, we have proposed nonparametric double exponentially weighted moving average chart based on Wilcoxon signed rank test under simple and ranked set sampling schemes for efficient monitoring of the process location. The proposed control charts are compared with classical exponentially weighted moving average, double exponentially weighted moving average, nonparametric exponentially weighted moving average sign, nonparametric exponentially weighted moving average signed rank, nonparametric cumulative sum signed rank charts using average run length and some other characteristics of run length distribution as performance measures. Comparison reveals that the proposed control charts performs better to detect all kinds of shifts in the process location than existing counterparts. A real-life application related to manufacturing process (the variable of interest is the diameter of piston ring) is also provided for the practical implementation of the proposed chart. [ABSTRACT FROM AUTHOR]

In the service and manufacturing industry, memory‐type control charts are extensively applied for monitoring the production process. These types of charts have the ability to efficiently detect disturbances, especially of smaller amount, in the process mean and/or dispersion. Recently, a new homogeneously weighted moving average (HWMA) chart has been proposed for efficient monitoring of smaller shifts. In this study, we have proposed a new double HWMA (DHWMA) chart to monitor the changes in the process mean. The run length profile of the proposed DHWMA chart is evaluated and compared with some existing control charts. The outcomes reveal that the DHWMA chart shows better performance over its competitor charts. The effect of non‐normality (in terms of robustness) and the estimation of the unknown parameters on the performance of the DHWMA chart are also investigated as a part of this study. Finally, a real‐life industrial application is offered to demonstrate the proposal for practical considerations. [ABSTRACT FROM AUTHOR]

Variation is an important phenomenon of the output of every manufacturing and production process. To deal with the natural and special cause variations in the process, quality practitioners mostly apply control charts. There have been regular advancements over time in the design structures of these charts such as runs rules, fast initial response, sampling mechanisms among many others. In this article, auxiliary‐information‐based progressive mean (AIB‐PM) control chart has been proposed, in which study variable is found correlated with another auxiliary variable. The development of the proposed AIB‐PM structure utilises both the study and auxiliary variables. It is based on the regression estimator to introduce an unbiased and efficient estimate of the location parameter of the study variable. The performance assessment is carried out using average run length as a metric under zero‐state and steady‐state modes. The proposed AIB‐PM chart is compared with some existing competitors and found that it performs uniformly superior than the existing competitors at small and persistent shifts in the process mean. An illustrative example using a real data set is presented to show the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

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

Control charts play a significant role to monitor the performance of a process. Nonparametric control charts are helpful when the probability model of the process output is not known. In such cases, the sampling mechanism becomes very important for picking a suitable sample for process monitoring. This study proposes a nonparametric arcsine exponentially weighted moving average sign chart by using an efficient scheme, namely, sequential sampling scheme. The proposal intends to enhance the detection ability of the arcsine exponentially weighted moving average sign chart, particularly for the detection of small shifts. The performance of the proposal is assessed, and compared with its counterparts, by using some popular run length properties including average, median and standard deviation run lengths. The proposed chart shows efficient shift detection ability as compared to the other charts, considered in this study. A real-life application based on the smartphone accelerometer data-set, for the implementation of the proposed scheme, is also presented. [ABSTRACT FROM AUTHOR]

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

To maintain and improve the quality of the processes, control charts play an important role for reduction of variation. To detect large shifts in the process parameters, Shewhart control charts are commonly applied but for small shifts, exponentially weighted moving averages (EWMA), cumulative sum (CUSUM), double exponentially weighted moving average (DEWMA), double CUSUM, moving average (MA), double moving average (DMA), and progressive mean (PM) control charts, are used. This study proposes double progressive mean (DPM) and optimal DPM control charts to enhance the performance of the PM chart. As the proposed DPM control charts use information sequentially, hence their performance is compared with natural competitors EWMA, CUSUM, DEWMA, double CUSUM, MA, DMA, and PM control charts. Run length and its different properties are evaluated to compare the performance of the proposed charts and counterparts. Results reveal that proposed optimal DPM outperforms the other charts. An example related to voltage on fixed capacitance level is also provided to illustrate the proposed charts. [ABSTRACT FROM AUTHOR]

Control charts are widely used to monitor the process parameters. Proper design structure and implementation of a control chart requires its in-control robustness, otherwise, its performance cannot be fairly observed. It is important to know whether a chart is sensitive to disturbances to the model (e.g. normality under which it is developed) or not. This study, explores the robustness of Mixed EWMA-CUSUM (MEC) control chart for location parameter under different non-normal and contaminated environments and compares it with its counterparts. The robustness of the MEC scheme and counterparts is evaluated by using the run length distributions, and for better assessment not only is in-control average run length (ARL) used, but also standard deviation of run length (SDRL) and different percentiles – that is, 5th, 50th and 95th– are considered. A careful insight is necessary in selection and application of control charts in non-normal and contaminated environments. It is observed that the in-control robustness performance of the MEC scheme is quite good in the case of normal, non-normal and contaminated normal distributions as compared with its competitor’s schemes. [ABSTRACT FROM AUTHOR]

In this paper, ranked set sampling is used for developing a non-parametric location chart which is developed on the basis of Wilcoxon signed rank statistic. The average run length and some other characteristics of run length are used as the measures to assess the performance of the proposed scheme. Some selective distributions including Laplace (or double exponential), logistic, normal, contaminated normal and student’s t-distributions are considered to examine the performance of the proposed Wilcoxon signed rank control chart. It has been observed that the proposed scheme shows superior shift detection ability than some of the competing counterpart schemes covered in this study. Moreover, the proposed control chart is also implemented and illustrated with a real data set. [ABSTRACT FROM AUTHOR]

Nonparametric control charts can be useful as an alternative in practice to the data expert when there is a lack of knowledge about the underlying distribution. In this study, a nonparametric cumulative sum (CUSUM) sign control chart for monitoring and detecting possible deviation from the process mean using ranked set sampling is proposed. Ranked set sampling is an effective method when the observations are inexpensive, and measurements are perhaps destructive. The average run length is used as performance measure for the proposed nonparametric CUSUM sign chart. Simulation study shows that the proposed version of the CUSUM sign chart using ranked set sampling generally outperforms than that version of the nonparametric CUSUM sign chart and the parametric CUSUM control chart using simple random sampling scheme. An illustrative example is also provided for practical consideration. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To detect sustainable changes in the manufacturing processes, memory-type charting schemes are frequently functioning. The recently designed, homogenously weighted moving average (HWMA) technique is effective for identifying substantial changes in the processes. To make the HWMA chart more effective for persistent shifts in the industrial processes, a double HWMA (DHWMA) chart has been proposed recently. This study intends to develop a triple HWMA (THWMA) chart for efficient monitoring of the process mean under zero- and steady-state scenarios. The non-normal effects of monitoring characteristics under in-control situations for heavy-tailed highly skewed and contaminated normal environments are computed under both states. The relative efficiency of the proposed structure is compared with HWMA, DHWMA, exponentially weighted moving average (EWMA), double EWMA, and the more effective triple EWMA control charting schemes. The relative analysis reveals that the proposed THWMA design performs more efficiently than the existing counterparts. An illustrative application related to substrate manufacturing is also incorporated to demonstrate the proposal. [ABSTRACT FROM AUTHOR]

Recently, a homogeneously weighted moving average (HWMA) chart has been suggested for the efficient detection of small shifts in the process mean. In this study, we have proposed a new one-sided HWMA chart to effectively detect small changes in the process dispersion. The run-length (RL) profiles like the average RL, the standard deviation RL, and the median RL are used as the performance measures. The RL profile comparisons indicate that the proposed chart has a better performance than its existing counterpart's charts for detecting small shifts in the process dispersion. An application related to the Dhahran wind farm data is also part of this study. [ABSTRACT FROM AUTHOR]

Control charts are commonly used tools that deal with monitoring of process parameters in an efficient manner. Multivariate control charts are more practical and are of greater importance for timely detection of assignable causes in multiple quality characteristics. This study deals with multivariate memory control charts to address smaller shifts in process mean vector. By adopting a new homogeneous weighting scheme, we have designed an efficient structure for multivariate process monitoring. We have also investigated the effect of an estimated variance covariance matrix on the proposed chart by considering different numbers and sizes of subgroups. We have evaluated the performance of the newly proposed multivariate chart under different numbers of quality characteristics and varying sample sizes. The performance measures used in this study include average run length, standard deviation run length, extra quadratic loss, and relative average run length. The performance analysis revealed that the proposed control chart outperforms the usual scheme under both known and estimated parameters. An application of the study proposal is also presented using a data set related to Olympic archery, for the monitoring of the location of arrows over the concentric rings on the archery board. [ABSTRACT FROM AUTHOR]

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