Search

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]

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 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]

In the statistical process control, the most useful tool to monitor the manufacturing processes in the industries is the control chart. Quality practitioners always desire the charting structure that identifies sustainable changes in the monitoring processes. The sensitivity of the control chart is improved when additional correlated auxiliary information about the study variable is introduced. The regression estimate in the form of auxiliary and supporting variables presents an unbiased and efficient statistic of the mean of the process variable. In this study, auxiliary information‐based moving average (AB‐MA) control chart is designed for efficient monitoring of shifts in the process location parameter. The performance of the AB‐MA control chart is evaluated and compared with existing charts using average run length and other run length characteristics. The comparison reveals that the AB‐MA control chart outperforms the competitors in detecting the small and medium changes in the process location parameter. The application of the proposal is also provided to implement it in real situation. [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]

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]

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]

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]

The statistical performance of traditional control charts for monitoring the process shifts is doubtful if the underlying process will not follow a normal distribution. So, in this situation, the use of a nonparametric control charts is considered to be an efficient alternative. In this paper, a nonparametric exponentially weighted moving average (EWMA) control chart is developed based on Wilcoxon signed-rank statistic using ranked set sampling. The average run length and some other associated characteristics were used as the performance evaluation of the proposed chart. A major advantage of the proposed nonparametric EWMA signed-rank chart is the robustness of its in-control run length distribution. Moreover, it has been observed that the proposed version of the EWMA signed-rank chart using ranked set sampling shows better detection ability than some of the competing counterparts including EWMA sign chart, EWMA signed-rank chart, and the usual EWMA 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]

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]