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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]

Statistical process control provides package of statistical tools applied for improving quality of manufacturing, production and services processes. Cumulative sum, exponentially weighted moving average (EWMA) and progressive mean charts belong to family of memory-type charts which effectively spot small and persistent shifts in the process parameter(s). EWMA chart requires normality and a proper choice of smoothing parameter to perform efficiently. Any deviation from these conditions affects its performance in terms of efficacy and robustness. For the said concerns, progressive exponentially weighted moving average (PEWMA) chart is developed to monitor the shifts in the process location. The performance of the proposed PEWMA chart is evaluated in terms of average run length and some other metrics of run length distribution. The assessment of the proposed chart has been made under standard normal, Student's t, gamma, Laplace, logistic, exponential, contaminated normal and lognormal distributions. The performance of the proposed PEWMA chart is also compared with some existing competitors including the classical EWMA, classical CUSUM, HWMA, MEC, MCE and DEWMA charts. The analysis reveals that the proposal offers a design structure which has high sensitivity to small and persistent drifts in the process mean and has advantage of robustness under non-normal scenarios. An application from substrates manufacturing process (in which flow width of the resist is the key quality characteristic) is also provided for practical implementations. [ABSTRACT FROM AUTHOR]

The memory‐type control charts are quite efficient in monitoring small and moderate shifts in process parameters. These charts, such as exponentially weighted moving average (EWMA), face the problem of inertia due to the time dependence of statistics. This issue could be solved to some extent using a break at regular/irregular intervals in plotting statistics. We have proposed this method by introducing a break feature at regular intervals, to handle the inertia problem, and found it quite useful for memory control charts. A comparative analysis of the proposal with some existing charts is also part of the study. [ABSTRACT FROM AUTHOR]

Statistical quality control is an integral part of modern manufacturing, and control charts are widely used to improve and control the performance of a process. However, much of the work related to process monitoring has been done using frequentist approaches, whereas the Bayesian methodology has a practical advantage in process monitoring; that is, it can be used with a small phase-I dataset. In this article, we considered different symmetric and asymmetric loss functions for designing Bayesian control charts and noticed that the performance of the Bayesian charts is biased, i.e., in-control performance of the charts is not equal to the desired value for some loss functions and comparison among different Bayesian charts can be misleading. Therefore, to get the desired in-control performance under different loss functions, we propose a corrected design of the Bayesian control charts. In addition to posterior charts, we also discuss a predictive control chart to monitor the future observations in this study. Besides a simulation study, we also present two real data examples: the first example is to monitor the surface roughness of reamed holes in a particular metal part while the second is about monitoring the measurements of a diameter. [ABSTRACT FROM AUTHOR]

A single control chart is very famous to control assignable causes that shift the process because of variations in parameters (e.g., location and dispersion). Simultaneous monitoring of processes is another popular approach used for the bilateral processes. In this study, we have proposed the mixed control charts for simultaneously monitoring of process location and dispersion parameters. We have used the idea of mixed exponential weighted moving average and cumulative sum charts and designed the charting structures for simultaneous monitoring. The proposals are compared with several existing counterparts. The comparisons reveal numerous advantages of the proposed charts over the other existing scheme. The practical application of the proposed charts is also highlighted using an illustrative example based on a real dataset. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger disturbances in the process parameters, whereas cumulative sum (CUSUM) and exponential weighted moving average (EWMA) are meant for smaller and moderate changes. In this study, we enhanced mixed EWMA–CUSUM control charts with varying fast initial response (FIR) features and also with a runs rule of two out of three successive points that fall above the upper control limit. We investigate their run-length properties. The proposed control charting schemes are compared with the existing counterparts including classical CUSUM, classical EWMA, FIR CUSUM, FIR EWMA, mixed EWMA–CUSUM, 2/3 modified EWMA, and 2/3 CUSUM control charting schemes. A case study is presented for practical considerations using a real data set. [ABSTRACT FROM PUBLISHER]

The control chart is a very popular tool of statistical process control. It is used to determine the existence of special cause variation to remove it so that the process may be brought in statistical control. Shewhart-type control charts are sensitive for large disturbances in the process, whereas cumulative sum (CUSUM)-type and exponentially weighted moving average (EWMA)-type control charts are intended to spot small and moderate disturbances. In this article, we proposed a mixed EWMA-CUSUM control chart for detecting a shift in the process mean and evaluated its average run lengths. Comparisons of the proposed control chart were made with some representative control charts including the classical CUSUM, classical EWMA, fast initial response CUSUM, fast initial response EWMA, adaptive CUSUM with EWMA-based shift estimator, weighted CUSUM and runs rules-based CUSUM and EWMA. The comparisons revealed that mixing the two charts makes the proposed scheme even more sensitive to the small shifts in the process mean than the other schemes designed for detecting small shifts. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Control charts are popular monitoring tools in statistical process control toolkit. These are used to identify assignable causes in the process parameters (location and/or dispersion). These assignable causes result in a shift in the process parameter(s). The shift can be categorized into three sizes (small, moderate, and large). Memory control charts such as the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts are effective for identifying small‐to‐moderate shift(s) in the process. Likewise, mixed memory control charts are useful for efficient process monitoring. In this study, we have proposed two new mixed memory control charts based on auxiliary information named MxMEC and MxMCE control charts to improve the efficiency of these mixed charts. The MxMEC chart is a merger of the auxiliary information based MxEWMA chart and the classical CUSUM chart. Likewise, the MxMCE chart integrates the auxiliary information based MxCUSUM with the classical EWMA chart. The proposed MxMEC and MxMCE charts are evaluated through famous performance measures including average run length, extra quadratic loss, relative average run length, and performance comparison index. The performance of the study proposals is compared with the existing counterparts such as the classical CUSUM and EWMA, MxCUSUM, MxEWMA, MEC, MCE, and runs rules‐based CUSUM charts. The comparisons revealed the superiority of the proposed charts against other competing charts particularly for small‐to‐moderate shifts in the process location. Finally, a real‐life data is used to show the implementation procedure of the proposed charts in practical situations. [ABSTRACT FROM AUTHOR]

Primarily, three types of control charts are employed to observe the disturbances in the process parameters. The large shifts are detected efficiently by Shewhart's control chart, whereas for small and medium instabilities, cumulative sum and exponentially weighted moving average control charts are used. This study proposes an assorted approach to monitor small, medium, and large disturbances in a single control charting procedure. For the evaluation of the proposed assorted control chart, we have used various measures like average run length, standard deviation run length, extra quadratic loss, relative average run length, and performance comparison index. A comparison of the proposed assorted control chart is presented with some typical charts including the Shewhart, cumulative sum, exponentially weighted moving average control charts, and some advanced modification such as combined Shewhart‐EWMA (CSE), combined Shewhart‐CUSUM (CSC), mixed EWMA‐CUSUM (MEC), FIR EWMA, and adaptive EWMA (AEWMA). Finally, a real‐life application example is presented to monitor the pH level of water in an ecotoxicology lab. This study proposes an assorted approach to monitor small, medium and large disturbances in a single control charting procedure. For the evaluation of the proposed assorted control chart, we have used various measures like average run length, standard deviation run length, extra quadratic loss, relative average run length, and performance comparison index. Finally, a real‐life application example is presented to monitor the pH level of water in an ecotoxicology lab. [ABSTRACT FROM AUTHOR]