Journal articles: 'X-bar control chart'

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Er Chiu, Jing. "A Fuzzy System for VSI X-Bar Control Chart." International Journal of Engineering and Technology 4, no. 4 (2012): 427–29. http://dx.doi.org/10.7763/ijet.2012.v4.402.

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Aslam, Muhammad, Ali Hussein AL-Marshadi, and Nasrullah Khan. "A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average." Mathematics 7, no. 10 (October 12, 2019): 957. http://dx.doi.org/10.3390/math7100957.

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The existing Shewhart X-bar control charts using the exponentially weighted moving average statistic are designed under the assumption that all observations are precise, determined, and known. In practice, it may be possible that the sample or the population observations are imprecise or fuzzy. In this paper, we present the designing of the X-bar control chart under the symmetry property of normal distribution using the neutrosophic exponentially weighted moving average statistics. We will first introduce the neutrosophic exponentially weighted moving average statistic, and then use it to design the X-bar control chart for monitoring the data under an uncertainty environment. We will determine the neutrosophic average run length using the neutrosophic Monte Carlo simulation. The efficiency of the proposed plan will be compared with existing control charts.

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Chan, Chan‐Ieong, Alan Ching Biu Tse, and Frederick H. K. Yim. "Comparing and combining individual x‐charts and x‐bar charts." International Journal of Quality & Reliability Management 20, no. 7 (October 1, 2003): 827–35. http://dx.doi.org/10.1108/02656710310491230.

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Control charts have played an important role in monitoring the performance of operation processes, ever since their invention. Traditionally, according to Juran's idea and others, x‐bar charts are more sensitive than individual x‐charts. However, such a conclusion is valid only under certain conditions. Individual x‐charts can outperform x‐bar charts in some situations, especially in cases of minor and extreme changes of the center value. Since each chart has its own advantages and disadvantages, the idea of combining the results of these two charts is studied. The finding seems to be useful for practitioners in quality control.

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Rahn, G. E., S. G. Kapoor, and R. E. DeVor. "Single-Subgroup Performance Measures and Diagnostic Procedures for X-Bar Control Charts." Journal of Engineering for Industry 116, no. 2 (May 1, 1994): 216–24. http://dx.doi.org/10.1115/1.2901933.

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Although Shewhart control charts have had a tremendous impact on quality improvement, the inability to precisely measure chart performance has limited their role, and subsequently overall effectiveness in the control of manufacturing processes. Measures of performance in terms of operational characteristics (OC) are defined on two distinct levels: (a) single-subgroup level, which examines the probability of a rule violation at any given subgroup (b) multiple-subgroup level, which considers the probability of one or more rule violations throughout process monitoring. Single-subgroup performance measures for X-bar charts that employ four rules are formulated. These measures are exact expressions of operational characteristics, except for the numerical approximation to the integral of the normal distribution. Applications of these models to simulated data demonstrate their accuracy in predicting chart performance. In addition, a diagnostic methodology is described which utilizes the derived performance measures to predict the mean of a shifted distribution. The proposed diagnostic procedure is illustrated in validation and application examples.

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Safaei, Abdul Sattar, Reza Baradaran Kazemzadeh, and Heng-Soon Gan. "Robust economic-statistical design of X-bar control chart." International Journal of Production Research 53, no. 14 (March 2, 2015): 4446–58. http://dx.doi.org/10.1080/00207543.2015.1018449.

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Prajapati, D. R., and Sukhraj Singh. "Determination of level of correlation for products of pharmaceutical industry by using modified X-bar chart." International Journal of Quality & Reliability Management 33, no. 6 (June 6, 2016): 724–46. http://dx.doi.org/10.1108/ijqrm-05-2014-0053.

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Purpose – It is found that the process outputs from most of the industries are correlated and the performance of X-bar chart deteriorates when the level of correlation increases. The purpose of this paper is to compute the level of correlation among the observations of the weights of tablets of a pharmaceutical industry by using modified X-bar chart. Design/methodology/approach – The design of the modified X-bar chart is based upon the sum of χ2s, using warning limits and the performance of the chart is measured in terms of average run lengths (ARLs). The ARLs at various sets of parameters of the modified X-bar chart are computed; using MATLAB software at the given mean and standard deviation. Findings – The performance of the modified X-bar chart is computed for sample sizes of four. ARLs of optimal schemes of X-bar chart for sample size of four are computed. Various optimal schemes of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.00, 0.25, 0.50, 0.75 and 1.00 are presented in this paper. Samples of weights of the tablets are taken from a pharmaceutical industry and computed the level of correlation among the observations of the weights of the tablets. It is found that the observations are closely resembled with the simulated observations for the level of correlation of 0.75 in this case study. The performance of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.50 and 0.75 is also compared with the conventional (Shewhart) X-bar chart and it is concluded that the modified X-bar chart performs better than Shewhart X-bar chart. Research limitations/implications – All the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for modified X-bar chart can also be used for other industries; where the manufacturing time of products is small. This scheme may also be used for any sample sizes suitable for the industries Practical implications – The optimal scheme of modified X-bar chart for sample size (n) of four is used according to the computed level of correlation in the observations. The simple design of modified X-bar chart makes it more useful at the shop floor level for many industries where correlation exists. The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation, the suggested control chart parameters can be used. Social implications – The design of modified X-bar chart uses very less numbers of parameters so it can be used at the shop floor level with ease. The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society as suggested by Taguchi (1985). Originality/value – Although; it is the extension of previous work but it can be applied to various manufacturing and service industries; where the data are correlated and normally distributed.

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Bakir, Saad T. "A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution." International Journal of Quality, Statistics, and Reliability 2012 (September 11, 2012): 1–10. http://dx.doi.org/10.1155/2012/147520.

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This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.

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Lee, Sang-Ho, and Chi-Hyuck Jun. "A New Control Scheme Always Better Than X-Bar Chart." Communications in Statistics - Theory and Methods 39, no. 19 (September 24, 2010): 3492–503. http://dx.doi.org/10.1080/03610920903243744.

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CASTAGLIOLA, PHILIPPE. "$\bar{X}$ CONTROL CHART FOR SKEWED POPULATIONS USING A SCALED WEIGHTED VARIANCE METHOD." International Journal of Reliability, Quality and Safety Engineering 07, no. 03 (September 2000): 237–52. http://dx.doi.org/10.1142/s0218539300000201.

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This paper proposes a new [Formula: see text] control chart devoted to the monitoring of skewed populations. This control chart, called the Scaled Weighted Variance[Formula: see text] control chart (SWV [Formula: see text] for short), is an improvement of the Weighted Variance [Formula: see text] control chart proposed by Choobineh and Ballard in 1987. In this paper we derive the control limits of the SWV [Formula: see text] control chart and give an illustrative example. Comparisons in terms of type I error and ARL are performed in the case of a lognormal, gamma and Weibull population.

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Oprime, Pedro Carlos, Naijela Janaina da Costa, Carlos Ivan Mozambani, and Celso Luiz Gonçalves. "X-bar control chart design with asymmetric control limits and triple sampling." International Journal of Advanced Manufacturing Technology 104, no. 9-12 (September 13, 2018): 3313–26. http://dx.doi.org/10.1007/s00170-018-2640-3.

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Oprime, Pedro Carlos, Fabiane Leticia Lizarelli, Marcio Lopes Pimenta, and Jorge Alberto Achcar. "Acceptance X-bar chart considering the sample distribution of capability indices, C ˆ p and C ˆ p k." International Journal of Quality & Reliability Management 36, no. 6 (June 3, 2019): 875–94. http://dx.doi.org/10.1108/ijqrm-11-2017-0239.

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Purpose The traditional Shewhart control chart, the X-bar and R/S chart, cannot give support to decide when it is not economically feasible to stop the process in order to remove special causes. Therefore, the purpose of this paper is to propose a new control chart design – a modified acceptance control chart, which provides a supportive method for decision making in economic terms, especially when the process has high capability indices. Design/methodology/approach The authors made a modeling expectation average run length (ARL), which incorporates the probability density function of the sampling distribution of Cpk, to compare and analyze the efficiency of the proposed design. Findings This study suggested a new procedure to calculate the control limits (CL) of the X-bar chart, which allows economic decisions about the process to be made. By introducing a permissible average variation and defining three regions for statistical CL in the traditional X-bar control chart, a new design is proposed. Originality/value A framework is presented to help practitioners in the use of the proposed control chart. Two new parameters (Cp and Cpk) in addition to m and n were introduced in the expected ARL equation. The Cpk is a random variable and its probability function is known. Therefore, by using a preliminary sample of a process under control, the authors can test whether the process is capable or not.

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Pearn, W. L., Chien-Wei Wu, and H. C. Chuang. "Procedures for testing manufacturing precision Cpbased on ( $\bar{x}$ ,R) or ( $\bar{x}$ ,S) control chart samples." International Journal of Advanced Manufacturing Technology 25, no. 5-6 (September 23, 2004): 598–607. http://dx.doi.org/10.1007/s00170-003-1870-0.

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Oprime, Pedro Carlos, and Glauco Henrique de Sousa Mendes. "The X-bar control chart with restriction of the capability indices." International Journal of Quality & Reliability Management 34, no. 1 (January 3, 2017): 38–52. http://dx.doi.org/10.1108/ijqrm-08-2014-0103.

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Purpose The purpose of this paper is to find the configuration of the number (m) and size (n) of the sample in Phase I that would make it possible to detect the out-of-control (OOC) state of the process with the smallest number of samples and ensure a capability index (Cpk) that would meet the customer’s requirements. Design/methodology/approach The suggested approach addresses this problem using simulation techniques and design of experiments (DOE). The simulation techniques made it possible to reproduce the normal operating conditions of the process. The DOE was used to construct a predictive model for control chart performance and thus to determine combinations of m and n in Phase I that would meet the capability objectives of the process. A numerical example and a simulation study were conducted to illustrate the proposed method. Findings Using simulation techniques and DOE, the authors can find the number (m) and size (n) of the sample in Phase I that would make it possible to detect the OOC state of the process with the smallest number of samples and ensure a Cpk that would meet the customer’s requirements. Originality/value In the real situations of many companies, choosing the numbers and sizes of samples (m and n) in Phases I and II is a crucial decision in relation to implementing a control chart. The paper shows that the simulation method and use of linear regression are effective alternatives because they are better known and more easily applied in industrial settings. Therefore, the need for alternatives to the X control chart comes into play.

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Aslam, Muhammad. "Design of X-Bar Control Chart for Resampling Under Uncertainty Environment." IEEE Access 7 (2019): 60661–71. http://dx.doi.org/10.1109/access.2019.2915007.

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Yang, Xiaomei, and Jianchao Zeng. "Economic design of x-bar control chart under hybrid maintenance policy." Journal of Quality in Maintenance Engineering 24, no. 3 (August 13, 2018): 331–44. http://dx.doi.org/10.1108/jqme-10-2016-0054.

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Purpose According to the relevance of product quality and machine degradation state, a hybrid maintenance policy is designed. The paper aims to discuss this issue. Design/methodology/approach Product quality control and machine maintenance are considered simultaneously in this policy. Based on this policy, the economic model of x-bar control chart is proposed using statistical process control and renewal reward theory. Findings This model is solved by genetic algorithm and the experimental results validated its feasibility. Originality/value In this model, the four corresponding relationship, which is between product quality monitoring result and machine degradation state, is analyzed.

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Patel, Saroj Kumar, and Abhijeet Ganguly. "Fuzzy multi-objective economic-statistical design of X-bar control chart." International Journal of Productivity and Quality Management 27, no. 4 (2019): 435. http://dx.doi.org/10.1504/ijpqm.2019.10023395.

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Ganguly, Abhijeet, and Saroj Kumar Patel. "Fuzzy multi-objective economic-statistical design of X-bar control chart." International Journal of Productivity and Quality Management 27, no. 4 (2019): 435. http://dx.doi.org/10.1504/ijpqm.2019.101925.

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ZHANG, YING, and PHILIPPE CASTAGLIOLA. "RUN RULES $\bar{X}$ CHARTS WHEN PROCESS PARAMETERS ARE UNKNOWN." International Journal of Reliability, Quality and Safety Engineering 17, no. 04 (August 2010): 381–99. http://dx.doi.org/10.1142/s0218539310003858.

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Supplementary Run Rules are applied to increase the power of the standard Shewhart [Formula: see text] chart for detecting small shift. The performance of this chart is usually evaluated under the assumption of known parameters. However, in practice, the process parameters are rarely known and, usually estimated from an in-control Phase I data set. In this paper, we evaluate (using a Markov Chain model) the performances of the Run Rules [Formula: see text] chart when the process parameters are estimated, and compare them when the process parameters are assumed known. We draw the conclusion that these performances are quite different when the number of samples used during the phase I is small and we suggest new chart parameters in order to overcome this problem.

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Khatiwada, Ram Prasad. "Bayesian Methods for Obtaining X-bar Chart: An Application on Food Quality Control." Journal of Institute of Science and Technology 19, no. 2 (November 9, 2015): 100–104. http://dx.doi.org/10.3126/jist.v19i2.13861.

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This article is about the Bayesian methods of constructing control chart for process mean. The control chart for mean based on the use of normal prior distribution seems to be robust for normal observations. The concussion is derived from an experiment of a set of real dada of the weights of a food product filled in pouches. The results were also supported by the simulated data.Journal of Institute of Science and Technology, 2014, 19(2): 100-104

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SIM, S. B., C. W. KANG, and M. XIE. "ON VARIABLE SAMPLE SIZE $\bar X$ CHART FOR PROCESSES WITH DOUBLE ASSIGNABLE CAUSES." International Journal of Reliability, Quality and Safety Engineering 11, no. 01 (March 2004): 47–58. http://dx.doi.org/10.1142/s0218539304001336.

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In statistical process control applications, variable sample size (VSS) [Formula: see text] chart is often used to detect the assignable cause quickly. However, it is usually assumed that only one assignable cause results in the out-of-control in the process. There are papers dealing with double assignable causes for traditional fixed sample size Shewhart chart. In this paper, we consider double assignable causes to occur with compound in the process and adopt the Markov chain approach to investigate the statistical properties of VSS [Formula: see text] chart. A procedure that can compute the optimal sample sizes is proposed.

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Mohammadi, Ali, Seyed Hadi Mirghaderi, and Zahra Mohit Ghiri. "Many-objective x-bar control chart using hybrid NSGA-III and DEA." International Journal of Quality Engineering and Technology 8, no. 2 (2020): 1. http://dx.doi.org/10.1504/ijqet.2020.10034260.

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Shiau, Yau-Ren, Meng-Hung Lin, I.-Ping Chung, and Yen-Ling Pan. "Monitoring capability study and genetic economic design of X-bar control chart." International Journal of Advanced Manufacturing Technology 30, no. 3-4 (December 1, 2005): 273–78. http://dx.doi.org/10.1007/s00170-005-0062-5.

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Ghiri, Zahra Mohit, Ali Mohammadi, and Seyed Hadi Mirghaderi. "Many-objective x-bar control chart using hybrid NSGA-III and DEA." International Journal of Quality Engineering and Technology 8, no. 2 (2021): 201. http://dx.doi.org/10.1504/ijqet.2021.113722.

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LUPO, TONI. "ECONOMIC-STATISTICAL DESIGN APPROACH FOR A VSSI X-BAR CHART CONSIDERING TAGUCHI LOSS FUNCTION AND RANDOM PROCESS SHIFTS." International Journal of Reliability, Quality and Safety Engineering 21, no. 02 (April 2014): 1450006. http://dx.doi.org/10.1142/s0218539314500065.

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Economic design approaches of control charts are commonly based on the assumption that various cost parameters values and the occurrence risk of assignable causes have to be a priori known with precision. However, in real operative contexts, such parameters can be really difficult to accurately estimate, especially considering costs arising from out-of-control conditions of the process. As consequence, pure economic design approaches can involve chart schemes with low statistical performance. To overcome such limitation, it is herein proposed a multi-objective economic-statistical design approach for an adaptive X-bar chart. In particular, such approach aims at the minimization of both the total quality related costs and the out-of-control average run length, in such a way assuring an optimal trade-off between economic and statistical performance of the related control procedure. Moreover, for a robust design approach, the mean shift is considered as a random variable. A mixed integer nonlinear constrained mathematical model is formulated to solve the treated problem, whereas the Pareto optimal frontier is described by the ε-constraint method. In order to show the employment of the proposed approach, an illustrative example is developed and the related considerations are given. Finally, some sensitivity analysis is also performed to investigate the effects of operative and costs parameters on chart parameters.

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Quinino, R. C., L. L. Ho, F. R. B. Cruz, and L. F. Bessegato. "A control chart to monitor the process mean based on inspecting attributes using control limits of the traditional X-bar chart." Journal of Statistical Computation and Simulation 90, no. 9 (March 27, 2020): 1639–60. http://dx.doi.org/10.1080/00949655.2020.1741588.

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Veljkovic, Kristina, Halima Elfaghihe, and Vesna Jevremovic. "Economic statistical design of X bar control chart for non-normal symmetric distribution of quality characteristic." Filomat 29, no. 10 (2015): 2325–38. http://dx.doi.org/10.2298/fil1510325v.

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In economic statistical design of a control chart, the economic-loss function is minimized subject to a constrained minimum value of power, maximum value of probability of false alarms and average time to signal an expected shift. This paper is concerned with the optimum economic statistical design of the X bar chart when quality characteristic has non-normal symmetric distribution. We considered three types of distributions: Student distribution, standard Laplace distribution and logistic distribution. For each of these distributions, we calculated theoretical distribution of standardized sample mean (or its best approximation) and approximated it with normal, Pearson VII and Johnson SU distributions. For considered example, constrained minimization of expected loss function was done using genetic algorithm in statistical software R. We compared results of economic statistical design of X-bar chart for theoretical distribution of standardized sample mean with the results for normal, Pearson and Johnson distributions. We found that, for all chosen distributions of quality characteristic, Pearson VII distribution and Johnson SU distribution give results very close to results based on theoretical distribution of standardized sample mean, while normal distribution gives much worse fit.

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Amiri, Farzad, Kazem Noghondarian, and Abdul Sattar Safaei. "Evaluating the performance of variable scheme X-bar control chart: a Taguchi loss approach." International Journal of Production Research 52, no. 18 (April 10, 2014): 5385–95. http://dx.doi.org/10.1080/00207543.2014.906762.

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Aslam, Muhammad, Rashad A. R. Bantan, and Nasrullah Khan. "Design of X-Bar Control Chart Using Multiple Dependent State Sampling Under Indeterminacy Environment." IEEE Access 7 (2019): 152233–42. http://dx.doi.org/10.1109/access.2019.2947598.

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Takemoto, Yasuhiko, and Ikuo Arizono. "A study of multivariate $(\bar{X},S)$ control chart based on Kullback–Leibler information." International Journal of Advanced Manufacturing Technology 25, no. 11-12 (March 25, 2004): 1205–10. http://dx.doi.org/10.1007/s00170-003-1947-9.

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Safaei, Abdul Sattar, Reza Baradaran Kazemzadeh, and Seyed Taghi Akhavan Niaki. "Multi-objective economic statistical design of X-bar control chart considering Taguchi loss function." International Journal of Advanced Manufacturing Technology 59, no. 9-12 (December 23, 2011): 1091–101. http://dx.doi.org/10.1007/s00170-011-3550-9.

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Ganguly, Abhijeet, and Saroj Kumar Patel. "A teaching–learning based optimization approach for economic design of X-bar control chart." Applied Soft Computing 24 (November 2014): 643–53. http://dx.doi.org/10.1016/j.asoc.2014.08.022.

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Ravichandran, J. "Self-starting X-bar control chart based on Six Sigma quality and sometimes pooling procedure." Journal of Statistical Computation and Simulation 89, no. 2 (November 27, 2018): 362–77. http://dx.doi.org/10.1080/00949655.2018.1551394.

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Rao, K. Srinivasa, N. A. Neelufur, and K. Venkata Subbaiah. "Optimal operating policies of X-bar control chart for non-normal variables with generalised Pareto in-control times." International Journal of Productivity and Quality Management 6, no. 1 (2010): 91. http://dx.doi.org/10.1504/ijpqm.2010.033886.

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S.Ramesh and B.A.Vasu. "Application Of Cusum Chart In Control Of Paper Quality." GIS Business 14, no. 6 (January 31, 2020): 1062–69. http://dx.doi.org/10.26643/gis.v14i6.16856.

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This paper is an attempt to assess if the manufacturing process of paper machine is in statistical control thereby improving the quality of paper being produced in a paper industry at the time of process itself. Quality is the foremost criteria for achieving the business target. Therefore, emphasis was made on controlling the quality of paper at the time of manufacturing process itself, rather than checking the finished lots at a later time. This control on quality will help the industry deduct the small shift in the process parameters and modify the operating characteristics at the time of production itself rather than receiving complaints from customers at a later stage. This paper describes controlling quality at the time of manufacture itself and helps the industry to concentrate on quality at low cost. The researcher has collected primary data at a leading paper industry during October, 2019. Though X-bar and Range charges were primarily used, CUSUM charts were used to sense the minor shifts in manufacturing process, to explore the possibility of adjusting process parameters during manufacture of paper.

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Geraldo Leite, Leonardo, Ítalo de Abreu Gonçalves, Carlos Henrique De Oliveira, Tarcísio Gonçalves De Brito, Sandra Miranda Neves, and Emerson José De Paiva. "ANALYSIS OF SURFACE ROUGHNESS MILLED OF STEEL AISI 1045 USING X-BAR AND R CONTROL CHART." International Journal of Engineering Technologies and Management Research 5, no. 6 (February 27, 2020): 15–23. http://dx.doi.org/10.29121/ijetmr.v5.i6.2018.242.

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The steel milling AISI 1045 has been gaining prominence in industry in recent years as it allows machined parts to be obtained with low-cost inserts. However, to ensure the final product quality, it is important that the milling for machining procedure be well planned in order to the cutters have their wear minimized in the process, as well as a considerable productivity rate with a zero occurrence of reworked parts or scraps. Thus, this paper presents a study about the quality of the machined surface on the end milling process of AISI 1045 steel using titanium nitride (TiN) coated carbide inserts, optimized for a combined design, using Design of Experiments (DOE). Statistical Process Control (SPC) is applied to analyze the process variations using X-bar and R control charts. The objective of this study is to identify the optimal combination of the input setup such as cutting speed (Vc), feed per tooth (fZ), work penetration (ae) and machining depth (ap) that is capable of minimize the process variation. The response measured is the roughness parameter Ra, observed under the influence of cutting fluid, tool wear, concentration and flow of the cutting fluid as noise. The obtained result was the stability of the Ra roughness for the AISI 1045 steel in end milling process, which is not influenced by noise variables due to Robust Parameter Design used in this study

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Caballero Morales, Santiago Omar. "Economic Statistical Design of Integrated X-bar-S Control Chart with Preventive Maintenance and General Failure Distribution." PLoS ONE 8, no. 3 (March 18, 2013): e59039. http://dx.doi.org/10.1371/journal.pone.0059039.

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Yu, F. J., and Y. S. Chen. "An Economic design for a variable-sampling-interval ${\bar{x}}$ control chart for a continuous-flow process." International Journal of Advanced Manufacturing Technology 25, no. 3-4 (August 11, 2004): 370–76. http://dx.doi.org/10.1007/s00170-003-1852-2.

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Bakir, Saad T., and Bob McNeal. "Monitoring The Level Of Students GPA's Over Time." American Journal of Business Education (AJBE) 3, no. 6 (June 1, 2010): 43–50. http://dx.doi.org/10.19030/ajbe.v3i6.441.

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A nonparametric (or distribution-free) statistical quality control chart is used to monitor the cumulative grade point averages (GPAs) of students over time. The chart is designed to detect any statistically significant positive or negative shifts in student GPAs from a desired target level. This nonparametric control chart is based on the signed-ranks of the GPAs of the sampled students. The exact false alarm rate and the in-control average run length of the proposed chart can be computed exactly and are independent of the underlying probability distribution of GPAs. The traditional Shewhart X-bar control chart for monitoring the mean of a process is based on the assumption that data follows a normal distribution. However, student GPAs may differ significantly from the normal distribution. As a result, using a traditional control chart to monitor the GPAs of students may lead to incorrectly specifying the control limits and the average run length and/or the false alarm rate of the chart. A test study was conducted at the College of Business Administration at Alabama State University. The study monitored the median cumulative GPAs of management majors during the period Spring 2005 through Spring 2009. The study revealed that the GPAs of students were stable at a median level of 2.6 over the period of the study.

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Abtew, Mulat Alubel, Subhalakshmi Kropi, Yan Hong, and Linzi Pu. "Implementation of Statistical Process Control (SPC) in the Sewing Section of Garment Industry for Quality Improvement." Autex Research Journal 18, no. 2 (June 1, 2018): 160–72. http://dx.doi.org/10.1515/aut-2017-0034.

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Abstract The main aim of this research was to implement appropriate Statistical Process Control (SPC) techniques for quality characteristics on sewing floor of garment Industry. Among different SPC quality improvements tools, control charts have been selected. After analyzing and selecting different critical parameters based on company and customer requirements, the X-bar and R charts for variable and c-charts for attribute quality characteristics have been identified and implemented in the trouser sewing lines for quality improvement. The check points for selected control chart implementation have also been designed. Remedial action plans for the occurred special cause variations and process stability were developed. The project incorporated theoretical and on-job training schemes for different quality team members, to understand the SPC concept and its implementation procedure. After implementation, significant improvements in the sewing section were achieved. The four months analysis before and after implementation of the SPC tools showed that the rejection percentage was reduced from 9.141% to 6.4%. Successful implementation of the result of this project can significantly improve process performance of other similar manufacturing units with appropriate modification.

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Singh, Gurpartap, Rupinder Singh, and S. S. Bal. "Investigations for partial denture casting by fused deposition modelling-assisted chemical vapour smoothing." Assembly Automation 40, no. 5 (June 29, 2020): 745–54. http://dx.doi.org/10.1108/aa-03-2020-0048.

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Purpose The purpose of this study is to investigate dimensional accuracy (Δd), surface roughness (Ra) and micro hardness (HV) of partial dentures (PD) prepared with synergic combination of fused deposition modelling (FDM) assisted chemical vapour smoothing (CVS) patterns and conventional dental casting (DC) from multi-factor optimization view point. Design/methodology/approach The master pattern for PD was prepared with acrylonitrile butadiene styrene (ABS) thermoplastic on FDM set-up (one of the low cost additive manufacturing process) followed by CVS process. The final PD as functional prototypes was casted with nickel–chromium-based (Ni-Cr) alloy by varying Ni% (Z). The other input parameters were powder to water ratio P/W (X) and pH value (Y) of water used. Findings The results of this study suggest that for controlling the Δd and Ra of the PD, most important factor is X, followed by Z. For hardness of PD, the most important factor is Z. But from overall optimization viewpoint, the best settings are X-100/12, Y-10 and Z-61% (in Ni-Cr alloy). Further, based upon X-bar chart (for HV), the FDM-assisted DC process used for preparation of PD is statistically controlled. Originality/value This study highlights that PD prepared with X-100/12, Y-10 and Z-61% gives overall better results from multi-factor optimization view point. Finally, X-bar chart has been plotted to understand the statistical nature of the synergic combination of FDM, CVS and DC.

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Manyele, Samwel Victor. "Analysis of the Effect of Subgroup Size on the X-Bar Control Chart Using Forensic Science Laboratory Sample Influx Data." Engineering 09, no. 05 (2017): 434–56. http://dx.doi.org/10.4236/eng.2017.95026.

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Gauri, Susanta Kumar. "Improved feature-based test statistic for assessing suitability of the preliminary samples for constructing control limits of $$ \bar{X} $$ chart." International Journal of Advanced Manufacturing Technology 58, no. 9-12 (June 22, 2011): 1171–87. http://dx.doi.org/10.1007/s00170-011-3440-1.

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Naderi, Mohammad Hossein, Asghar Seif, and Mohammad Bameni Moghadam. "Constrained Optimal Design of $bar{X}$ Control Chart for Correlated Data under Weibull Shock Model with Multiple Assignable Causes and Taguchi Loss Function." Journal of Statistical Research of Iran 15, no. 1 (September 1, 2018): 1–44. http://dx.doi.org/10.29252/jsri.15.1.1.

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Yeh, Li Lun, and Fei Long Chen. "An Extension of Banerjee and Rahim's Model for an Economic X-Bar Control Chart for Non-Normally Distributed Data Under Gamma Failure Models." Communications in Statistics - Simulation and Computation 39, no. 5 (April 30, 2010): 994–1015. http://dx.doi.org/10.1080/03610911003734815.

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Dwijanto Witjaksono, Andre. "EVALUASI AKTIV ITAS KA/ZEN DALAM PENERAPAN STATISTICAL PROCESS CONTROL PADA PROSES PENGELASAN SERTA PEMAKAIAN JAM ORANG DI DEPARTEMEN GENERAL ENGINEERING PT PAL INDONESIA." EKUITAS (Jurnal Ekonomi dan Keuangan) 8, no. 1 (January 13, 2017): 94. http://dx.doi.org/10.24034/j25485024.y2004.v8.i1.2028.

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This research was a case study that evaluated of Katzen activity, in welding process and utilization of man hour. The observation was located in PT PAL Indonesia, Ujung, Surabaya.This research contains of four observations, these are: evaluating the length of stator fram e product, welding process between rib and web plate, welding process between web plate and web plate, and utilization of man hour. Thefirst observation was using X Bar & R control chart, while the second observation using u control chart. The third observation using design of experiment, with independent variable to be analyze was welder, welding object, and interaction between welder and welding object, with dependent variable was spatter defect. Thefourth observation was doing to evaluated the succedeed of implementation kaizen in utilization of man hour, with t-test.The yield of calculation has been obtained with using Minitab 11.12 and SPSS 9.0 software, and the significant rate ex:: 0,05, was showed that: First, the process was in statistically control, but the process capability should be rised because the process capability less than 1 (C = 0.53, Cpk = CPL = 0.37). Second, although the welding process was in statistically control, the process capability also need to be increased, because the spatter defect that produced is 53 %. Third, analysis of varians shows that from the design of experiment, all of three independent variables, was influences significantly on welding of defect. The biggest one was welder factor. Fourth, the utilization of man hour for repair work (defect work ratio) on the first sampling, have higher significantly than the second sampling. This means that on the second sampling (latest sampling), repair work rate could be decreased.

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Antony, Jiju, E. V. Gijo, Vikas Kumar, and Abhijeet Ghadge. "A multiple case study analysis of Six Sigma practices in Indian manufacturing companies." International Journal of Quality & Reliability Management 33, no. 8 (September 5, 2016): 1138–49. http://dx.doi.org/10.1108/ijqrm-10-2014-0157.

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Purpose The purpose of this paper is to explore the fundamental barriers/challenges, benefits, commonly used tools and techniques, organisational infrastructure and impact on organisational performance in three Indian manufacturing companies. Design/methodology/approach A multi-case study analysis using the exploratory case study research was adopted by the authors to obtain a deeper insight into the Six Sigma implementation within three distinctive manufacturing organisations in India. Interviews were conducted with relevant staff (Six Sigma Deployment Champions, Six Sigma Master Black Belts and Six Sigma Black Belts) in all three companies. Findings Some of the barriers in implementing and sustaining Six Sigma identified from the case studies include: lack of accuracy of data generated from the processes, lack of understanding of the benefits of Six Sigma in the early stages of its adoption, high-attrition rate of Six Sigma Black Belts and so on. The benefits of Six Sigma included improvement of process yield, reduction of rework and rejection, reduction of raw material inventory, improved on-time delivery, on-time availability of material for production and so on. Supplier-input-process-output-customer, cause and effect diagram, process mapping, hypothesis tests (two sample test, F-test, etc.), control charts (X-bar-R chart, individual chart, etc.), simple graphical tools such as histograms, box plots and dot plots were the most commonly used tools of Six Sigma across the companies that participated for this research. All three companies have reported that Six Sigma had a positive impact on organisational performance and moreover the study also revealed that Six Sigma had positive impact on customer satisfaction, return-on-investment, productivity and product quality. Research limitations/implications The study was carried out in three Indian companies and therefore the findings cannot be generalised. The authors are extending the study to three more companies and the findings will be reported in the forthcoming months. Practical implications The findings of the study provide a good foundation to understand the fundamental barriers, benefits, commonly used tools and whether Six Sigma is having any impact on business performance in the Indian context. Very few empirical studies have been carried out on Six Sigma implementation in the Indian manufacturing companies and this research sets an agenda for a number of studies to follow on in the forthcoming years. Originality/value In authors’ opinion, this is possibly one of the first multi-case empirical studies on Six Sigma implementation in the Indian manufacturing companies. The results of the study can be used to benchmark with similar studies in other countries to understand the good and bad management practices of Six Sigma implementation.

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Jones, Caroline, David Riley, Amy Morris, Jeremy Michael Sen, Alana Ferrari, Joshua Humphrey, Jenna VanHoose, et al. "Reducing length of stay for patients with acute myeloid leukemia receiving inpatient high-dose cytarabine consolidation chemotherapy." Journal of Clinical Oncology 39, no. 28_suppl (October 1, 2021): 257. http://dx.doi.org/10.1200/jco.2020.39.28_suppl.257.

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257 Background: For patients receiving high dose cytarabine (HiDAC) at University of Virginia (UVA) Health between 10/2019 and 10/2020, median length of stay (LOS) from time of clinic appointment to hospital discharge was 119.35 hours. Standard treatment time should be 112 hours from premedication to end of chemotherapy. There are no national standards for duration of inpatient stay for planned chemotherapy, but only 50% of these patients were discharged after noon (over 3 hours post-chemotherapy completion). LOS that extends beyond the standard treatment time results in increased cost, overutilization of hospital resources, delayed admissions for future patients, and patient dissatisfaction. Methods: A multidisciplinary team comprised of licensed providers, pharmacists, and nurses was formed. The team focused on percentage of patients discharged by noon as a surrogate marker for LOS due to inconsistency of admission times; the aim was to increase patients discharged by noon to 65%. Reviewing the baseline data revealed an unstable process with a 3-sigma X-bar statistical process control chart. The team developed current and ideal process state maps, a Pareto chart, and a priority matrix to determine an action plan. The most common identified causes for delay in discharge included: lack of standardized discharge checklist, discharge order placed after 10 am, medications dispensed from the outpatient pharmacy after 11 am, licensed providers not prioritizing discharge patients, and medication reconciliation not completed prior to day of discharge. Results: From 10/2020 to 5/2021, the first PDSA cycle focused on standardizing the discharge process to correct the instability in the process. A discharge checklist was created based on the ideal process map, which allowed the providers to have a consistent process at discharge. 3-sigma Xbar chart demonstrated a now stable process and an increase of patients discharged by noon to 58%. During the second PDSA cycle starting in 6/2021, providers completed medication reconciliation the day before discharge, prioritized HiDAC discharges first during rounds, and ensured discharge orders were placed by completion of the last chemotherapy bag. Data collection is ongoing, and will be analyzed by August 2021. Future tests of change are planned to focus on the pharmacy medication delivery service. Hospital LOS has also decreased after the first PDSA cycle. Conclusions: Using quality improvement methodology, a multidisciplinary team developed an action plan for patients receiving HiDAC which to date has increased the percentage of patients discharging by noon and decreased length of stay. This outcome may lead to reduce hospitalization costs and increase bed availability for other oncology patients. Further PDSA cycles are scheduled and continuous evaluation of the process is ongoing.

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Hsu, Hsi-Mei, and Yan-Kwang Chen. "Grey relation pattern recogniser for X-bar control charts." International Journal of Manufacturing Technology and Management 1, no. 2/3 (2000): 271. http://dx.doi.org/10.1504/ijmtm.2000.001341.

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MARCELLUS, RICHARD L. "Operating characteristics of X-bar charts with asymmetric control limits." IIE Transactions 33, no. 12 (December 2001): 1059–69. http://dx.doi.org/10.1080/07408170108936896.

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Muñoz-Rosas, J. F., E. Álvarez-Verdejo, M. N. Pérez-Aróstegui, and L. Gutiérrez-Gutiérrez. "Empirical Comparisons of X-bar Charts when Control Limits are Estimated." Quality and Reliability Engineering International 32, no. 2 (January 8, 2015): 453–64. http://dx.doi.org/10.1002/qre.1763.

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Journal articles on the topic 'X-bar control chart'

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