Browsing by Author "Jun, Chi-Hyuck"

Now showing 1 - 12 of 12
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    Bootstrap confidence intervals of the modified process capability Index for weibull distribution
    (Springer, 2017) Kashif, Muhammad; Aslam, Muhammad; Rao, Srinivasa G.; AL-Marshadi, Ali Hussein; Jun, Chi-Hyuck
    The objective of the paper is to modify the existing process capability index (PCI) for a Weibull distribution and to construct bootstrap confidence intervals (BCIs) for the newly proposed index. Three BCIs that consist of standard, percentile and bias-corrected percentile bootstrap (BCPB) confidence intervals are constructed for the newly proposed index and the existing Pearn and Chen index. The efficiency of the newly proposed index CGPK is compared with Pearn and Chen index using their coverage probabilities and average widths. The coverage probabilities and average width of three BCIs were calculated using Monte Carlo simulation studies. The newly proposed index shows better performance than Pearn and Chen index. The results indicate that BCPB confidence interval was more efficient in both cases and outperform other two confidence intervals in all situations. The comparison of average width of BCPB apparently shows that the proposed index performed better in all cases. A real-life example is also provided for a practical application.
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    Comparing the efficacy of coefficient of variation control charts using generalized multiple dependent state sampling with various run-rule control charts
    (Springer Science and Business Media LLC, 2024) Rao, G. Srinivasa; Aslam, Muhammad; Alamri, Faten S.; Jun, Chi-Hyuck
    This paper aimed to develop a coefficient of variation (CV) control chart utilizing the generalized multiple dependent state (GMDS) sampling approach for CV monitoring. We conducted a comprehensive examination of this designed control chart in comparison to existing control charts based on multiple dependent state sampling (MDS) and the Shewhart-type CV control chart, with a focus on average run lengths. The results were then compared to run-rule control charts available in the existing literature. Additionally, we elucidated the implementation of the proposed control chart through concrete examples and a simulation study. The findings clearly demonstrated that the GMDS sampling control chart shows significantly superior accuracy in detecting process shifts when compared to the MDS sampling control chart. As a result, the control chart approach presented in this paper holds significant potential for applications in textile and medical industries, particularly when researchers seek to identify minor to moderate shifts in the CV, contributing to enhanced quality control and process monitoring in these domains.
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    Comparing the efficacy of coefficient of variation control charts using generalized multiple dependent state sampling with various run-rule control charts
    (Springer Science and Business Media LLC, 2024) Rao, G. Srinivasa; Aslam, Muhammad; Alamri, Faten S.; Jun, Chi-Hyuck
    This paper aimed to develop a coefficient of variation (CV) control chart utilizing the generalized multiple dependent state (GMDS) sampling approach for CV monitoring. We conducted a comprehensive examination of this designed control chart in comparison to existing control charts based on multiple dependent state sampling (MDS) and the Shewhart-type CV control chart, with a focus on average run lengths. The results were then compared to run-rule control charts available in the existing literature. Additionally, we elucidated the implementation of the proposed control chart through concrete examples and a simulation study. The findings clearly demonstrated that the GMDS sampling control chart shows significantly superior accuracy in detecting process shifts when compared to the MDS sampling control chart. As a result, the control chart approach presented in this paper holds significant potential for applications in textile and medical industries, particularly when researchers seek to identify minor to moderate shifts in the CV, contributing to enhanced quality control and process monitoring in these domains.
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    A control chart for multivariate poisson distribution using repetitive sampling
    (Taylor and Francis Group, 2016) Aslam, Muhammad; Rao, Srinivasa G.; Ahmad, Liaquat; Jun, Chi-Hyuck
    Control charts using repetitive group sampling have attracted a great deal of attention during the last few years. In the present article, we attempt to develop a control chart for the multivariate Poisson distribution using the repetitive group sampling scheme. In the proposed control chart, the monitoring statistic from the multivariate Poisson distribution has been used for the quick detection of the deteriorated process to avoid losses. The control coefficients have been estimated using the specified in-control average run lengths. The procedure of the proposed control chart has been explained by using the real-world example and a simulated data set. It has been observed that the proposed control chart is an efficient development for the quick detection of the nonrandom change in the manufacturing process.
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    Control charts for monitoring process capability index using median absolute deviation for some popular distributions
    (Multidisciplinary Digital Publishing Institute, 2019) Aslam, Muhammad; Rao, G. Srinivasa; AL-Marshadi, Ali Hussein; Ahmad, Liaquat; Jun, Chi-Hyuck
    A control chart monitoring the process capability index (PCI) using median absolute deviation (MAD) is proposed to analyze the industrial process performance. Extensive simulation studies were carried out to evaluate the performance of MAD-based PCI control charts under the low, moderate, and high asymmetric conditions when the process characteristic follows Weibull, log-normal, and gamma distributions. The performance of the proposed control charts was evaluated based on the average run lengths. The practical implementation of the proposed charts was also illustrated with industrial data.
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    The efficacy of process capability indices using median absolute deviation and their bootstrap confidence intervals
    (Springer, 2017) Kashif, Muhammad; Aslam, Muhammad; Jun, Chi-Hyuck; Al-Marshadi, Ali Hussein; Rao, Srinivasa G.
    The process capability indices (PCIs) Cp and C pk are commonly used in industry to measure the process performance.The implementation of these indices required that process should follow a normal distribution. However, in many cases the underlying processes are non-normal which influence the performance of these indices. In this paper, median absolute deviation (MAD)is used as a robust measure of variability in two PCIs, Cp and Cpk . Extensive simulation experiments were performed to evaluate the performance of MAD-based PCIs under low, moderate and high asymmetric condition of Weibull, Log-Normal and Gamma distributions. The point estimation of MAD-based estimator of Cp and Cpk is encouraging and showed a good result in case of Log- Normal and Gamma distributions, whereas these estimators perform very well in case of Weibull distribution. The comparison of quantile method and MAD method showed that the performance of MAD-based PCIs is better for Weibull and Log-Normal processes under low and moderate asymmetric conditions, whereas its performance for Gamma distribution remained unsatisfactory. Four bootstrap confidence intervals (BCIs) such as standard (SB), percentile (PB), bias-corrected percentile (BCPB) and percentile-t (PTB) were constructed using quantile and MAD methods under all asymmetric conditions of three distributions under study. The bias-corrected percentile bootstrap confidence interval (BCPB) is recommended for a quantile (PC)-based PCIs, whereas CIs were recommended for MAD-based PCIs under all asymmetric conditions of Weibull, Log-Normal and Gamma distributions. A real-life example is also given to describe and validate the application of proposed methodology.
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    Mixed EWMA–CUSUM chart for COM-Poisson distribution
    (Taylor & Francis, 2020) Rao, Gadde Srinivasa; Aslam, Muhammad; Rasheed, Umer; Jun, Chi-Hyuck
    In this article, we develop a mixed (SPC) control chart for monitoring moderate and/or small shift in the process following the Conway Maxwell Poisson (COM-Poisson) distribution. To determine the ability and performance of the proposed mixed EWMA-CUSUM control chart, we evaluate the average run lengths and compare with different control charts. We will compare the efficiency of the proposed control chart over the existing control charts in terms of average run length. The results show an improvement in ARL when compared with the existing charts. A real example and a simulation study is also added to use the chart in the industry.
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    Monitoring mortality caused by COVID-19 using gamma-distributed variables based on generalized multiple dependent state sampling
    (Hindawi, 2021) Aslam, Muhammad; Rao, G. Srinivasa; Saleem, Muhammad; Ahmad, Rehan; Sherwani, Khan; Jun, Chi-Hyuck
    More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.
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    A new control chart using GINI CPK
    (Taylor & Francis, 2020) Aslam, Muhammad; Rao, G. S; Ahmad, Liaquat; Jun, Chi-Hyuck
    Process capability indices (PCIs) have been extensively applied to study product potential and performance. The quality control engineers use these process capability indices greatly to magnify the actual performance of the process from the predetermined specifications of the product. A control chart for the process monitoring is developed using the Gini’s mean difference as a measure of variability under the Weibull distribution. We consider the control chart process capability index, Cpk has been used for the efficient monitoring of the manufacturing process. It has been observed that the proposed chart is comparatively better than the existing chart. An example of the practical application of the proposed chart has also been presented.
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    A nonparametric HEWMA-p control chart for variance in monitoring processes
    (Symmetry, 2019) Aslam, Muhammad; Rao, Gadde Srinivasa; AL-Marshadi, Ali Hussein; Jun, Chi-Hyuck
    Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.
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    A variable control chart based on process capability index under generalized multiple dependent state sampling
    (Institute of Electrical and Electronics Engineers, 2019) Rao, G. Srinivasa; Raza, Muhammad Ali; Aslam, Muhammad; AL-Marshadi, Ali Hussein; Jun, Chi-Hyuck
    This paper proposed a process capability index-based control chart under the new extended form of multiple-dependent state sampling (MDS) named generalized MDS (GMDS). The scheme is based on inner and outer control limits and utilizes the previous state of the samples. The performance comparisons of the proposed chart with the existing charts are made by using out-of-control ARL. The simulation study showed the superiority of the proposed chart over the existing PCI-based control charts under Shewhart and MDS schemes. An empirical illustration is also given to demonstrate the application of the proposed chart.
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    A variable sampling plan using generalized multiple dependent state based on a one-sided process capability index
    (2019) Rao, Srinivasa G.; Aslam, Muhammad; Jun, Chi-Hyuck
    In this manuscript, a sampling plan based on one-sided process capability index is designed using the generalized multiple dependent state (GMDS) sampling. The operating characteristic function is developed using the exact distribution of the estimated PCI. The plan parameters of the proposed sampling plans are determined through the non-linear optimization problem at specified producer’s risk and consumer’s risk. An example is reported with the help of real data from the industry. The efficiency of the proposed sampling plan is compared with the existing multiple dependent state (MDS) sampling in terms of sample size.