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In this paper, an attempt is made to develop a new variable control chart under the resubmitted sampling scheme. The resubmission of a sample allows retaking a sample to pass through inspection/analyzing if there is a question on the inspection of the sample at any stage. The proposed control chart includes the X-bar chart as a special case. The performance of the proposed control chart is assessed by determining the in-control and out-of-control average run lengths for the various parametric settings. For illustrative purposes, a real-life example is presented to explain a detailed implementation of the proposed control chart. [ABSTRACT FROM AUTHOR]

In this paper, we propose a mixed control chart to monitor the process quality using attribute data combined with variable data. The proposed control chart proceeds like an np control chart based on the number of nonconforming parts but requires variable data only when the decision is indeterminate. The control coefficients are determined by considering the in-control and the out-of-control average run lengths for various specified parameters. The extensive tables are provided for the industrial use. The advantages of the proposed control chart are discussed over the traditional np control chart. [ABSTRACT FROM AUTHOR]

In this paper, a resubmitted sampling‐based successive sampling over two successive occasions control chart is proposed to monitor the underlying characteristic of interest. Auxiliary information of the first occasion is utilized to monitor the relative change in the study variable over the second occasion successively despite high degree of correlation. The structural and operational design is presented along with the comparative performance evaluation. The average run length is used as a performance evaluation measure and proved the argument in favor of the presented concept in comparison with the other auxiliary data control charts. The implementation is explained through two real examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose extended exponential weighted moving average (EEWMA) control chart (CC) for the surveillance the process mean using repetitive sampling scheme under the assumption that quality characteristic of a product is a quantitative variable following the normal distribution. Two control limits named as inner and outer control limits of the proposed CC are derived using the in-control process mean and variance. The necessary measures are derived to obtain the process in-control and out-of-control average run length (ARL) and tabulated for different process shifts and smoothing constants. Comparisons are made to check the capability of intended CC with the EEWMA CC and repetitive EWMA CC in term of ARL. It has been observed that the presented CC shows better results in term of early detection of divergence in process mean. A simulation study is carried out to illustrate the performance of the proposed CC. An industrial example has been incorporated for its practical purpose. [ABSTRACT FROM AUTHOR]

Control charts are developed to make the specific quality measures for a successful production process and follow normal distribution behaviors. But some real‐life practices do not match such practices and exhibit some positively skewed behavior like lognormal distribution. The present study has considered this situation and proposed a monitoring control chart based on lognormal process variation using a repetitive sampling scheme. This concept proved better for detecting shifts as quickly as possible, and compared with the existing concept, results are elaborated through extensive tables. The average run lengths and standard deviations of the run lengths are being used as a performance evaluation measures and computed by using Monte Carlo simulations performed in R language. A real‐life situation has been discussed in the example section to strengthen the proposed control chart concept in a real‐life situation. [ABSTRACT FROM AUTHOR]

In this article, a new non parametric control chart based on the modified or controlled exponentially weighted moving average (EWMA) statistic is developed to monitor the process deviation from the target value. The proposed control chart is evaluated for different values of design parameters using the average run length as a performance criterion under various sample sizes. The proposed chart is compared with the existing non parametric EWMA sign control chart. It is observed that the proposed chart is better than the existing EWMA sign control chart in terms of run length characteristics. An empirical example is provided for the practical implementation of the proposed chart. [ABSTRACT FROM AUTHOR]

In this paper, successive sampling (SS) mean monitoring control chart is proposed for two successive occasions using multiple dependent state (MDS) sampling and characterized as MDS‐SS control chart. The sample size is selected on the basis of SS for the control statistic in two pairs of the control limits. The average run length is evaluated, and the performance of the proposed concept over the existing SS control chart is presented using a real‐life example. The SS monitoring chart concept is compiled with MDS, and it becomes more sophisticated in detection of warning signs. [ABSTRACT FROM AUTHOR]

Repetitive sampling has been found very popular in improving the control chart techniques for last couple of years. In repetitive sampling based control charts, there are two additional control limits inside the usual upper control limit (UCL) and lower control limit (LCL). If any subgroup crosses these limits but remain inside outer limits, it is deferred and replaced with another selection. Process is said to be out of control if any subgroup falls outside UCL/LCL. In this article, the technique has been modified by introducing a relation between outer and inner control limits in terms of a ratio and need of this modification has also been justified by highlighting a gap in the existing technique. By using Monte Carlo simulation, several results have been generated relevant to different sample sizes and introduced ratios. The results have been described with the help of average run length (ARL) tables that how the efficiency of control chart is effected by using different ratios. The modification in the technique also provides variety of alternatives within the scope of repetitive based control charts. All the discussed options have summarized to one table to see that how the control limits under this technique behave and impact on detecting shifts in the process average. The schemes have been interpreted in the light of above ratio and their comparison has been described under different sample sizes that facilitate the user to select most appropriate scheme for a desired process control. An example has been included by choosing one of the proposed schemes to show the application and performance of the proposed control chart in a manufacturing process. [ABSTRACT FROM AUTHOR]

A control chart based on double exponentially weighted moving average and Benjamini‐Hochberg multiple testing procedure is proposed that controls the false discovery rate (FDR). The proposed control chart is based on probabilities (or P values) to accept or reject the null hypothesis of the underlying process is in control. To make a decision, instead of using only the current probability, previous "m" probabilities are considered. The performance of the control chart is evaluated in terms of average run length (ARL) using Monte Carlo simulations. Procedure for estimation of parameters used in the control chart is also discussed. The proposed control chart is compared with previous control charts and found to be more efficient in controlling the false discovery rate. [ABSTRACT FROM AUTHOR]

The Weibull distribution is one of the most popular distributions for lifetime modeling. However, there has not been much research on control charts for a Weibull distribution. Shewhart control is known to be inefficient to detect a small shift in the process, while exponentially weighted moving average (EWMA) and cumulative sum control chart (CUSUM) charts have the ability to detect small changes in the process. To enhance the performance of a control chart for a Weibull distribution, we introduce a new control chart based on hybrid EWMA and CUSUM statistic, called the HEWMA-CUSUM chart. The performance of the proposed chart is compared with the existing chart in terms of the average run length (ARL). The proposed chart is found to be more sensitive than the existing chart in ARL. A simulation study is provided for illustration purposes. A real data is also applied to the proposed chart for practical use. [ABSTRACT FROM AUTHOR]

In this manuscript, a control chart is designed for two-piece normal distribution using repetitive sampling. The necessary measures to determine the average run lengths for in control and out-of-control process are given. The average run lengths are presented for various specified parameters and shift constants. The efficiency of the proposed chart is compared with the existing control chart using single sampling. The application of the proposed chart is given with the help of an example. [ABSTRACT FROM AUTHOR]

In practice, the products can be manufactured through several stages. In this manuscript, we will propose an attribute control chart plotting the number of defectives for a two-stage process. The in-control average run length is derived and the out-of-control average run lengths are also analyzed according to the process shifts in the first and/or the second stage process. The tables of the average run lengths are given for various specified parameters. An example is given with synthetic data for the illustration of the proposed control chart. [ABSTRACT FROM AUTHOR]

A control chart for monitoring process variation by using multiple dependent state (MDS) sampling is constructed in the present article. The operational formulas for in-control and out-of-control average run lengths (ARLs) are derived. Control constants are established by considering the target in-control ARL at a normal process. The extensive ARL tables are reported for various parameters and shifted values of process parameters. The performance of the proposed control chart has been evaluated with several existing charts in regard of ARLs, which empowered the presented chart and proved far better for timely detection of assignable causes. The application of the proposed concept is illustrated with a real-life industrial example and a simulation-based study to elaborate strength of the proposed chart over the existing concepts. [ABSTRACT FROM AUTHOR]

The control charts are designed to examine and detect the changes in the manufacturing process. We present repetitive exponentially weighted moving average (EWMA) control chart using regression estimator for monitoring process mean based on ranked set sampling (RSS). We study the detection ability of the proposed control chart for monitoring process mean when mean is shifted due to some random causes. The performance and efficiency of proposed repetitive EWMA-RSS control chart is evaluated in terms of ARLs for different process shift. The effectiveness of the repetitive EWMA-RSS control chart is compared with the single EWMA-RSS control chart. The practical application of the proposed chart is also discussed for better understanding. [ABSTRACT FROM AUTHOR]

A new EWMA control chart has been proposed under repetitive sampling when a quantitative characteristic follows the exponential distribution. The properties of the proposed chart, including the average run lengths has been is compared with two existing control charts with the help of simulated data. An application of the proposed chart hs been illustrated using a healthcare data set. [ABSTRACT FROM AUTHOR]

In this paper, we present a new control chart for exponentially distributed quality characteristics using variable transformation and exponentially weighted moving average (EWMA). The in-control and out-of-control average run lengths (ARLs) are derived using the normal approximation. The parameters of the proposed control chart are determined by considering the target in-control ARL. The efficiency of the proposed control chart is discussed in terms of ARLs over the existing control chart without using EWMA. The application of the proposed control chart is discussed with the help of a real example. [ABSTRACT FROM AUTHOR]

In this paper, a new nonparametric control chart based on the exponentially weighted moving average (EWMA) sign statistic is proposed using repetitive sampling. The control chart is proposed to effectively detect the process mean shift away from the target value without the distributional assumption on the quality characteristic. The proposed control chart is based on two pairs of upper and lower control limits having different control coefficients. The in-control and the out-of-control average run lengths of the proposed control chart are evaluated through the Monte Carlo simulation. The proposed control chart is shown to be more efficient than the existing EWMA sign control chart in terms of the average run length. [ABSTRACT FROM AUTHOR]