Journal articles: 'EWMA chart'

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Yang, Su-Fen, and Barry C. Arnold. "A Simple Approach for Monitoring Business Service Time Variation." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/238719.

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Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from processes having nonnormal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. In this paper, we propose a new asymmetric EWMA variance chart (EWMA-AV chart) and an asymmetric EWMA mean chart (EWMA-AM chart) based on two simple statistics to monitor process variance and mean shifts simultaneously. Further, we explore the sampling properties of the new monitoring statistics and calculate the average run lengths when using both the EWMA-AV chart and the EWMA-AM chart. The performance of the EWMA-AV and EWMA-AM charts and that of some existing variance and mean charts are compared. A numerical example involving nonnormal service times from the service system of a bank branch in Taiwan is used to illustrate the applications of the EWMA-AV and EWMA-AM charts and to compare them with the existing variance (or standard deviation) and mean charts. The proposed EWMA-AV chart and EWMA-AM charts show superior detection performance compared to the existing variance and mean charts. The EWMA-AV chart and EWMA-AM chart are thus recommended.

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Shamsuzzaman, Mohammad. "Optimization Design of 2-EWMA Control Chart Based on Random Process Shift." Applied Mechanics and Materials 465-466 (December 2013): 1185–90. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1185.

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The exponentially weighted moving average (EWMA) control charts are widely used for detecting process shifts of small and moderate sizes in Statistical Process Control (SPC).This article presents an algorithm for the optimization design of a multi-EWMA scheme comprising two EWMA control charts (known as 2-EWMA chart) considering random process shifts in mean. The random process shifts in mean is characterized by a Rayleigh distribution. The design algorithm optimizes the charting parameters of the 2-EWMA chart based on loss function. Comparative study shows that the optimal 2-EWMA chart outperforms the original 2-EWMA chart, as well as the original EWMA chart. In general, this article will help to enhance the detection effectiveness of the 2-EWMA chart, and facilitate its applications in SPC.

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Chen, Jen-Hsiang, and Shin-Li Lu. "An Enhanced Auxiliary Information-Based EWMA-t Chart for Monitoring the Process Mean." Applied Sciences 10, no. 7 (March 26, 2020): 2252. http://dx.doi.org/10.3390/app10072252.

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The exponentially weighted moving average t chart using auxiliary information (AIB-EWMA-t chart) is an effective approach for monitoring small process mean shifts when the process standard deviation is unstable or poorly estimated. To further enhance the sensitivity of the AIB-EWMA-t chart, in this study, we propose an AIB generally weighted moving average (GWMA) t chart (AIB-GWMA-t chart) to monitor the process mean. The existing EWMA-t, GWMA-t, and AIB-EWMA-t charts are special cases of the AIB-GWMA-t chart. Numerical simulation studies indicate that the AIB-GWMA-t chart performs uniformly and substantially better than the EWMA-t and GWMA-t charts in terms of average run length. Moreover, the AIB-GWMA-t chart with large design and adjustment parameters also outperforms the AIB-EWMA-t chart when the correlation coefficients are within a certain range. An illustrative example is provided to highlight the efficiency of the proposed AIB-GWMA-t chart in detecting small process mean shifts.

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Ng, Jing Wen, Voon Hee Wong, and Sook Theng Pang. "A synthetic exponentially weighted moving average control chart to monitor process median based on ranked set sampling." ITM Web of Conferences 36 (2021): 01002. http://dx.doi.org/10.1051/itmconf/20213601002.

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Exponentially Weighted Moving Average (EWMA) control charts yield insights into data in a way more comprehensible to the practitioners and researchers because of its capability in discovering small to moderate process mean shifts. EWMA control chart is incorporated with conforming run length (CRL) chart, named synthetic EWMA chart, to enhance the performance of the chart in detecting the out-of-control signal. Synthetic EWMA chart based on ranked set sampling (RSS) for monitoring process mean has been proposed as it advanced the detection of chart over a series of mean shifts. With the situation that normality assumption is scarcely attain in practice, we proposed synthetic EWMA median chart based on RSS. Rather than select average run length (ARL) as sole performance evaluating tool, the median and percentiles of run-length distribution are used to examine the performance of the proposed chart as it provides more information on the entire run-length distribution. Near-optimal parameters of the proposed chart will be acquired by setting the incontrol ARL at a designated value. The run length performances of the proposed chart are then compared with the existing charts such as EWMA median chart based on RSS.

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Yang, Chung Ming, Su Fen Yang, and Jeng Sheng Lin. "A New EWMA Loss Control Chart with Adaptive Control Scheme." Applied Mechanics and Materials 631-632 (September 2014): 12–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.12.

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A single chart, instead of and R charts or and S charts, to simultaneously monitor the process mean and variability would reduce the required time and effort. A number of studies have attempted to find such charts. Moreover, a number of studies demonstrated that the adaptive control charts may detect process shifts faster than the fixed control charts. This paper proposes the EWMA loss chart with variable sample sizes and sampling intervals (VSSI) to effectively monitor the difference of process measurements and target. An example is used to illustrate the application and performance of the proposed control chart in detecting the changes in the difference of the process measurements and target. Numerical analyses demonstrated that the VSSI EWMA loss chart outperforms the fixed sampling interval EWMA average loss chart and the Shewhart joint and S charts. Therefore, the VSSI EWMA loss chart is recommended.

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Resti, Aulia, Tatik Widiharih, and Rukun Santoso. "GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)." Jurnal Gaussian 10, no. 1 (February 28, 2021): 114–24. http://dx.doi.org/10.14710/j.gauss.v10i1.30938.

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Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

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Su-Fen, Yang, Tsai Wen-Chi, Huang Tzee-Ming, Yang Chi-Chin, and Cheng Smiley. "Monitoring process mean with a new EWMA control chart." Production 21, no. 2 (May 27, 2011): 217–22. http://dx.doi.org/10.1590/s0103-65132011005000026.

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In practice, sometimes the process data did not come from a known population distribution. So the commonly used Shewhart variables control charts are not suitable since their performance could not be properly evaluated. In this paper, we propose a new EWMA Control Chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. The sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. Furthermore, an Arcsine EWMA Chart is proposed since the average run lengths of the Arcsine EWMA Chart are more reasonable than those of the new EWMA Chart. The Arcsine EWMA Chart is recommended if we are concerned with the proper values of the average run length.

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CASTAGLIOLA, PHILIPPE, GIOVANNI CELANO, SERGIO FICHERA, and VALERIA NUNNARI. "A VARIABLE SAMPLE SIZE S2-EWMA CONTROL CHART FOR MONITORING THE PROCESS VARIANCE." International Journal of Reliability, Quality and Safety Engineering 15, no. 03 (June 2008): 181–201. http://dx.doi.org/10.1142/s0218539308003039.

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Monitoring the stability of measures dispersion from a process quality parameter is an important aspect of Statistical Process Control which should be carefully planned by practitioners. To perform this task, this paper proposes an adaptive EWMA chart as a practical and efficient tool. The developed EWMA chart is the Variable Sample Size (VSS) version of a static S2-EWMA control chart previously developed by one of the authors to monitor the sample variance. The way to compute the design parameters of this VSS S2-EWMA control chart is discussed and an optimal design strategy based on the Average Time to Signal (ATS) after a shift in process dispersion is presented. The statistical performance of the VSS S2-EWMA has been evaluated by means of a comparison with two other EWMA charts: the static S2-EWMA and the adaptive (VSI) S2-EWMA allowing to vary the sampling intervals. The obtained results show how the possibility of varying the sample size significantly improves the statistical performance over the static S2-EWMA; furthermore, some interesting findings suggest to implement the VSS S2-EWMA with respect to the VSI S2-EWMA when some particular process operating conditions occur.

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DEWIANTARI, NI KADEK YUNI, I. WAYAN SUMARJAYA, and G. K. GANDHIADI. "PETA KENDALI EWMA RESIDUAL PADA DATA BERAUTOKORELASI." E-Jurnal Matematika 8, no. 1 (February 2, 2019): 64. http://dx.doi.org/10.24843/mtk.2019.v08.i01.p236.

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Control charts with autocorrelation can be overcome by creating control chart with residuals from the best forecasting model. EWMA control chart is a alternative to the Shewhart control chart when detecting small shifts. The purpose of this study is to make the best forecasting model to obtain residual, and see the stability of the rupiah exchange rate against US dollar using EWMA control chart with residual. The best model of the case is ARIMA (1,1,1). The results of the EWMA residual control chart with ? = 0.1 there is a pattern that makes the process unstable.

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Engmann, Gideon Mensah, and Dong Han. "Multichart Schemes for Detecting Changes in Disease Incidence." Computational and Mathematical Methods in Medicine 2020 (May 15, 2020): 1–14. http://dx.doi.org/10.1155/2020/7267801.

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Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.

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Neubauer, Aljoscha Steffen. "The EWMA control chart: properties and comparison with other quality-control procedures by computer simulation." Clinical Chemistry 43, no. 4 (April 1, 1997): 594–601. http://dx.doi.org/10.1093/clinchem/43.4.594.

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Abstract A quality-control chart based on exponentially weighted moving averages (EWMA) has, in the past few years, become a popular tool for controlling inaccuracy in industrial quality control. In this paper, I explain the principles of this technique, present some numerical examples, and by computer simulation compare EWMA with other control charts currently used in clinical chemistry. The EWMA chart offers a flexible instrument for visualizing imprecision and inaccuracy and is a good alternative to other charts for detecting inaccuracy, especially where small shifts are of interest. Detection of imprecision with EWMA charts, however, requires special modification.

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Ikpotokin, O., J. O. Braimah, and H. E. Oboh. "Performance evaluation of conventional exponentially weighted moving average (EWMA) and p-value cumulative sum (CUSUM) control chart." Global Journal of Pure and Applied Sciences 27, no. 2 (June 24, 2021): 171–79. http://dx.doi.org/10.4314/gjpas.v27i2.9.

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This paper is aimed at comparing the performances of the conventional Exponentially Weighted Moving Average (EWMA) and p-value Cumulative Sum (CUSUM) control chart. These charts were applied in monitoring the outbreak of pulmonary tuberculosis in Delta State University Teaching Hospital (DELSUTH), Oghara for a period of eighty four (84) calendar months. Line chart and histogram were plotted to test for stationary and normality of the data. Autocorrelation plot was also used to study the randomness of the data. The results of the control charts show that conventional EWMA chart detects shifts faster in monitoring process mean than the p-value CUSUM control chart. Keywords and Phrases: Exponentially Weighted Moving Average (EWMA), p-value, Cumulative Sum (CUSUM), Autocorrelation, Randomness

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Yang, Chung Ming, Su Fen Yang, and Jin Tyan Yeh. "Design of EWMA Control Charts for Controlling Dependent Process Stages with Attribute Data." Applied Mechanics and Materials 411-414 (September 2013): 1085–88. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1085.

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In this study, we propose EWMA control charts to monitor two dependent process stages with attribute data. The detection ability of the EWMA control charts is compared to those of Shewhart attribute control charts and cause selecting control chart by different correlation. Numerical example and simulation study show that the EWMA control charts have better performance compared to Shewhart attribute control charts and cause selecting control charts.

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Engler, J., K. H. Tölle, H. H. Timm, E. Hohls, and J. Krieter. "Control charts applied to pig farming data." Archives Animal Breeding 52, no. 3 (October 10, 2009): 272–83. http://dx.doi.org/10.5194/aab-52-272-2009.

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Abstract. Statistical control charts are effective tools to reveal changes in a production process. The CUSUM (cumulative sum) and the EWMA (exponentially weighted moving average) control chart are used to detect small deviations in a process. Data from two sow herds, herd A and herd B, were collected from 1999 to 2004. Farm A had an average number of 530 breeding sows, Farm B had an average of 370 breeding sows. Both herds were diagnosed with Porcine Reproductive and Respiratory Syndrome (PRRS). The weekly means of the number of piglets weaned (NPW), the pre-weaning mortality (PWM) and return to service rate (RSR) were analysed with different settings of the CUSUM as well as the EWMA control chart to reveal a shift in the production process. For the pre-weaning mortality and the number of piglets weaned, the two charts detected a change in the process 4 weeks (Farm A) and 2 weeks before (Farm B) PRRS was diagnosed. The CUSUM and the EWMA chart revealed a shift in the return to service rate on Farm A 3.5 months before PRRS was detected. On Farm B, the signal occurred 6 weeks before the infection was detected. The CUSUM and the EWMA control charts were effective tools for detecting small deviations in sow herd data. Compared with EWMA, the use of the CUSUM chart is more straightforward and the settings are more easily handled. The CUSUM chart is therefore the preferred option for use in practice.

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Park, Young Soo, Eui Pyo Hong, Kyoung Yong Park, and Woong Hee Shon. "Performance Improvement Study of CV-EWMA Control Chart to Detect Small Shifts of CV." Advanced Materials Research 1051 (October 2014): 1016–22. http://dx.doi.org/10.4028/www.scientific.net/amr.1051.1016.

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In order to control a process that has short production cycle and where the product type and specifications change often with conventional shewhart control charts such as and control charts, a new control chart must be applied every time the parameters change . As this is a very inefficient method in terms of the cost and time, CV control chart using coefficient of variation statistics was developed. As CV control chart reflects only the current sample data on control chart, it can be useful when there is a significant change in process. However, it does not respond sensitively to a process that has subtle change or requires a high control level. CV-EWMA control chart was researched to monitor small shifts in CV. This study proposes a way to improve accuracy and precision of population parameter estimation of conventional CV-EWMA control chart and applied it to a control chart before analyzing its performance. As a result, the accuracy and precision of conventional CV-EWMA control chart has been improved and it was verified that the proposed control chart is a proper control chart to control small shifts of CV.

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Naveed, Muhammad, Muhamma Azam, Nasrullah Khan, and Muhammad Aslam. "Design of a Control Chart Using Extended EWMA Statistic." Technologies 6, no. 4 (November 16, 2018): 108. http://dx.doi.org/10.3390/technologies6040108.

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In the present paper, we propose a control chart based on extended exponentially weighted moving average (EEWMA) statistic to detect a quick shift in the mean. The mean and variance expression of the proposed EEWMA statistic are derived. The proposed EEWMA statistic is unbiased and simulation results show a smaller variance as compared to the traditional EWMA. The performance of the proposed control chart with the existing chart based on the EWMA statistic is evaluated in terms of average run length (ARL). Various tables were constructed for different values of parameters. The comparison of the EEWMA control chart with the traditional EWMA and Shewhart control charts illustrates that the proposed control chart performs better in terms of quick detection of the shift. The working procedure of the proposed control chart was also illustrated by simulated and application data.

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SHEU, SHEY-HUEI, and SHIN-LI LU. "MONITORING AUTOCORRELATED PROCESS MEAN AND VARIANCE USING A GWMA CHART BASED ON RESIDUALS." Asia-Pacific Journal of Operational Research 25, no. 06 (December 2008): 781–92. http://dx.doi.org/10.1142/s0217595908002012.

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This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control charts of residuals used to monitor process variability and to monitor simultaneously the process mean and variance are considered to evaluate how average run lengths (ARLs) differ in each case.

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NOOROSSANA, RASSOUL, AMIR AFSHIN FATAHI, PERSHANG DOKOUHAKI, and MASSOUD BABAKHANI. "ZIB-EWMA CONTROL CHART FOR MONITORING RARE HEALTH EVENTS." Journal of Mechanics in Medicine and Biology 11, no. 04 (September 2011): 881–95. http://dx.doi.org/10.1142/s0219519411004125.

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Monitoring rare health events, as a significant public health subject, has been considered recently by different authors. In this regard, different statistical methods such as g-type control chart, Poisson CUSUM control chart, sets-based methods, and Bernoulli CUSUM chart have been developed. Zero-inflated binomial (ZIB) distribution, due to its structure, can also be considered to develop methods for monitoring rare health-related events. If zero inflation is considered in the sampling data, and the sampling subgroup size is mandatory greater than 1, then the data best fits the ZIB distribution and the aforementioned control charts cannot be applied. ZIB distribution assumes that random shocks, corresponding to rare health events, occur and then number of failures in each subgroup fits a binomial distribution. In this paper, an exponentially weighted moving average (EWMA) control chart is applied for ZIB data to develop a ZIB-EWMA chart. Since ZIB-EWMA statistic values are not independent, Markov chain approach is considered to evaluate the performance of the proposed control chart in terms of average run length (ARL). According to the ARL measure, this ZIB-EWMA chart has a better performance in comparison with the methods available in the literature. In addition, a real case study related to rare infections in a hospital is investigated to show the applicability of the proposed control chart.

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Saruhashi, Takumi, Masato Ohkubo, and Yasushi Nagata. "Study on Likelihood-Ratio-Based Multivariate EWMA Control Chart Using Lasso." Quality Innovation Prosperity 25, no. 1 (March 31, 2021): 3–15. http://dx.doi.org/10.12776/qip.v25i1.1552.

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Purpose: When applying exponentially weighted moving average (EWMA) multivariate control charts to multivariate statistical process control, in many cases, only some elements of the controlled parameters change. In such situations, control charts applying Lasso are useful. This study proposes a novel multivariate control chart that assumes that only a few elements of the controlled parameters change. Methodology/Approach: We applied Lasso to the conventional likelihood ratio-based EWMA chart; specifically, we considered a multivariate control chart based on a log-likelihood ratio with sparse estimators of the mean vector and variance-covariance matrix. Findings: The results show that 1) it is possible to identify which elements have changed by confirming each sparse estimated parameter, and 2) the proposed procedure outperforms the conventional likelihood ratio-based EWMA chart regardless of the number of parameter elements that change. Research Limitation/Implication: We perform sparse estimation under the assumption that the regularization parameters are known. However, the regularization parameters are often unknown in real life; therefore, it is necessary to discuss how to determine them. Originality/Value of paper: The study provides a natural extension of the conventional likelihood ratio-based EWMA chart to improve interpretability and detection accuracy. Our procedure is expected to solve challenges created by changes in a few elements of the population mean vector and population variance-covariance matrix.

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Tran, Kim Phuc, Philippe Castagliola, Thi Hien Nguyen, and Anne Cuzol. "Design of a Variable Sampling Interval EWMA Median Control Chart." International Journal of Reliability, Quality and Safety Engineering 26, no. 05 (June 30, 2019): 1950021. http://dx.doi.org/10.1142/s0218539319500219.

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In the literature, median type control charts have been widely investigated as easy and efficient means to monitor the process mean when observations are from a normal distribution. In this work, a Variable Sampling Interval (VSI) Exponentially Weighted Moving Average (EWMA) median control chart is proposed and studied. The Markov chains are used to calculate the average run length to signal (ARL). A performance comparison with the original EWMA median control chart is made. The numerical results show that the proposed chart is considerably more effective as it is faster in detecting process shifts. Finally, the implementation of the proposed chart is illustrated with an example in food production process.

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Baker, Arthur W., Salah Haridy, Joseph Salem, Iulian Ilieş, Awatef O. Ergai, Aven Samareh, Nicholas Andrianas, James C. Benneyan, Daniel J. Sexton, and Deverick J. Anderson. "Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study." BMJ Quality & Safety 27, no. 8 (November 24, 2017): 600–610. http://dx.doi.org/10.1136/bmjqs-2017-006474.

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BackgroundTraditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data.MethodsWe performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations.ResultsConventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data.ConclusionsOur findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks.

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Yang, Ming Jie, and Xue Min Zi. "The Comparison among Three Control Charts for Monitoring the Auto Correlated Processes." Applied Mechanics and Materials 490-491 (January 2014): 1579–83. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1579.

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We compare the ARL of three charts for monitoring the mean shifts of the first-order auto regressive model to choose a proper control chart. Simulation results show that the REWMA chart has a large superior to the EWMA and T2 the chart when -1<Ø<0, but when Ø>0, the chart is better than the other two charts.

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FATAHI, AMIR AFSHIN, RASSOUL NOOROSSANA, PERSHANG DOKOUHAKI, and BABAK FARHANG MOGHADDAM. "ZERO INFLATED POISSON EWMA CONTROL CHART FOR MONITORING RARE HEALTH-RELATED EVENTS." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1250065. http://dx.doi.org/10.1142/s0219519412500650.

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Recently, rare health events issue has motivated many researches in the field of control charting. Various methods such as g-type control chart, g-type CUSUM control chart, sets method, CUSCORE method, SHDA method and the Bernoulli CUSUM have been developed in this regard, in which each of them has a specific approach to the problem. As a relatively new approach, zero inflation in Poisson distribution, named ZIP distribution can be applied. In this paper, an exponentially weighted moving average (EWMA) control chart is developed for the ZIP random variable to monitor rare health-related events with a predefined performance measure value. Since the ZIP-EWMA plotted data are dependent, Markov chain approach is applied to calculate average run lengths (ARLs) as the control chart performance criteria. Based on the ARL measure, the ZIP-EWMA chart performs better in comparison with the methods available in the literature. As the main contribution of this paper is the development a control chart which performs better than the previously proposed charts. Also, a motivating real case study related to monitoring needle-stick rare occurrences in a hospital is investigated to show the applicability of the developed chart.

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Hong, Eui Pyo, Hae Woon Kang, and Chang Wook Kang. "DEWMA Control Chart for the Coefficient of Variation." Advanced Materials Research 201-203 (February 2011): 1682–88. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.1682.

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When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. The CV-EWMA (exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. In this paper, we propose the CV-DEWMA control chart, combining the DEWMA (double exponentially weighted moving average) technique. We show that CV-DEWMA control chart perform better than CV-EWMA control chart in detecting small shifts when sample size n is larger than 5.

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Li, Li. "The GLR Chart for Poisson Process with Individual Observations." Advanced Materials Research 542-543 (June 2012): 42–46. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.42.

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A GLR (generalized likelihood ratio) chart for Poisson distributed process with individual observations is proposed and the design procedure of the GLR chart is discussed. The performance of the GLR charts is compared to the exponentially weighted moving average (EWMA) chart and the GWMA chart. The numerical experiments show that the GLR chart has comparable performance as the other two charts. However, the GLR chart is much easier to design and implement since there are more design parameters in these two charts.

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Aslam, Muhammad, Srinivasa Rao Gadde, Mansour Sattam Aldosari, and Chi-Hyuck Jun. "A hybrid EWMA chart using coefficient of variation." International Journal of Quality & Reliability Management 36, no. 4 (April 1, 2019): 587–600. http://dx.doi.org/10.1108/ijqrm-12-2017-0285.

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Purpose The purpose of this paper is to develop a new control chart using two EWMA statistics called the hybrid exponentially weighted moving average (HEWMA) chart to improve the sensitivity of EWMA chart proposed by Zhang et al. (2014). When mean and variance of process are not constants, the use of control chart using coefficient of variation (CV) is a successful approach. Design/methodology/approach The control chart using EWMA statistics has ability to detect moderate and small shifts in the process. The authors present the designing of the proposed HEWMA statistics control chart called the HEWMACV chart based on two hybrid EWMA (HEWMA) statistics. The proposed control chart utilizes the current information and previous information to make decision about the state of control chart. Findings In this paper, the authors will present the designing of HEWMA statistics control chart called the HEWMACV chart. The efficiency of the proposed control chart is shown using the simulated data and real data from the industry. The application of proposed chart on the real data shows that the proposed chart has ability to detect shift in the process and it is superior than existing chart in terms of average run length (ARL). Research limitations/implications The design and implementation of the proposed control chart on a real data shows that it can be applied in several industries, such as chemical industry, biological assays, etc. Practical implications The practical application of HEWMA chart using coefficient variation is gaining extensive adequacy. The design and implementation of the HEWMA chart offers a new approach in the detection of small process mean shift. Originality/value In practice, when mean and variance of process are not constants, the use of control chart using CV is a successful approach. In this paper, the authors designed a new control chart using two EWMA statistics called the HEWMA chart to improve the sensitivity of EWMA chart. The comparison shows that the proposed chart is superior than the existing chart in terms of ARL.

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Khan, Muhammad Zahir, Muhammad Farid Khan, Muhammad Aslam, Seyed Taghi Akhavan Niaki, and Abdur Razzaque Mughal. "A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean." Information 9, no. 12 (December 7, 2018): 312. http://dx.doi.org/10.3390/info9120312.

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Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data.

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Hong, Eui Pyo, Hae Woon Kang, Chang Wook Kang, and Jae Won Baik. "CV Control Chart Using GWMA Technique." Advanced Materials Research 337 (September 2011): 247–54. http://dx.doi.org/10.4028/www.scientific.net/amr.337.247.

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When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shifts in the magnitude of CV. This study suggest the CV-GWMA(generally weighted moving average) control chart, combining the GWMA technique, which shows better performance than the EWMA(exponentially weighted moving average) or DEWMA(double exponentially weighted moving average) technique in detecting small shifts of the process. Through a performance evaluation, the proposed control chart showed more excellent performance than the existing CV-EWMA control chart or the CV-DEWMA control chart in detecting small shifts in CV.

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Umar, Adamu A., Michael BC Khoo, Sajal Saha, and Abdul Haq. "A combined variable sampling interval and double sampling control chart with auxiliary information for the process mean." Transactions of the Institute of Measurement and Control 42, no. 6 (November 18, 2019): 1151–65. http://dx.doi.org/10.1177/0142331219885525.

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In recent years, the suitable use of auxiliary information technique in control charts has shown an improved run length performance compared to control charts that do not have this feature. This article proposes a combined variable sampling interval (VSI) and double sampling (DS) chart using the auxiliary information (AI) technique (called VSIDS-AI chart, hereafter). The plotting-statistic of the VSIDS-AI chart requires information from both the study and auxiliary variables to efficiently detect process mean shifts. The charting statistics, optimal design and performance assessment of the VSIDS-AI chart are discussed. The steady-state average time to signal (ssATS) and steady-state expected average time to signal (ssEATS) are considered as the performance measures. The ssATS and ssEATS results of the VSIDS-AI chart are compared with those of the DS AI, variable sample size and sampling interval AI, exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The results of comparison show that the VSIDS-AI chart outperforms the charts under comparison for all shift sizes, except the EWMA-AI and RS-AI charts for small shift sizes. An illustrative example is provided to demonstrate the implementation of the VSIDS-AI chart.

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AFGANI, L. M. JAMALUDDIN Al. "Accuracy of Zero Inflated Generalized Poisson Exponentially Moving Average Control Chart." Jurnal Matematika, Statistika dan Komputasi 18, no. 1 (September 2, 2021): 121–29. http://dx.doi.org/10.20956/j.v18i1.14035.

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The Zero-Inflated Generalized Poisson (ZIGP) distribution is a case-based distribution where the discrete data has a large number of zeros and an overdispersion occurs, i.e. the variance is greater than the mean value. The purpose of this study is to determine the Exponential Weight Moving Average (EWMA) control chart with the assumption that the data has a Zero-Inflated Generalized Poisson (ZIP) distribution. The results show that the ARL value of the ARL ZIGP EWMA control chart has better accuracy when compared to when using the ZIP EWMA control chart on ZIGP distributed data. This is indicated by the smaller ARL value compared to the ZIP EWMA control chart, namely when φ = 1.4, and φ = 0.6. So that the ARL ZIGP EWMA control chart has a fairly good accuracy in detecting out of control conditions for ZIGP distributed data. In addition, the modified ARL shows the same values before and after the modification for the underdispersion data and shows a larger or negative value for the overdispersion data. This can eliminate or reduce errors in analyzing the accuracy of the control chart.

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Zhang, Lingyun, and Gemai Chen. "An Extended EWMA Mean Chart." Quality Technology & Quantitative Management 2, no. 1 (January 2005): 39–52. http://dx.doi.org/10.1080/16843703.2005.11673088.

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Maravelakis, Petros, John Panaretos, and Stelios Psarakis. "EWMA Chart and Measurement Error." Journal of Applied Statistics 31, no. 4 (May 2004): 445–55. http://dx.doi.org/10.1080/02664760410001681738.

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Haq, Abdul, and Michael B. C. Khoo. "An adaptive multivariate EWMA chart." Computers & Industrial Engineering 127 (January 2019): 549–57. http://dx.doi.org/10.1016/j.cie.2018.10.040.

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Alves, Custodio Da Cunha, Andréa Cristina Konrath, Elisa Henning, Olga Maria Formigoni Carvalho Walter, Edson Pacheco Paladini, Teresa A. Oliveira, and Amílcar Oliveira. "The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing process." Brazilian Journal of Operations & Production Management 16, no. 1 (January 21, 2019): 1–13. http://dx.doi.org/10.14488/bjopm.2019.v16.n1.a1.

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Goal: The objective is to conclude, based on a comparative study, if there is a significant difference in sensitivity between the application of MCE and the individual application of the CUSUM or EWMA chart, i.e., greater sensitivity particularly for cases of lesser magnitude of change. Design/Methodology/Approach: These are an applied research and statistical techniques such as statistical control charts are used for monitoring variability. Results: The results show that the MCE chart signals a process out of statistical control, while individual EWMA and CUSUM charts does not detect any situation out of statistical control for the data analyzed. Limitations: This article is dedicated to measurable variables and individual analysis of quality characteristics, without investing in attribute variables. The MCE chart was applied to items that are essential to the productive process development being analysed. Practical Implications: The practical implications of this study can contribute to: the correct choice of more sensitive control charts to detect mainly small changes in the location (mean) of processes; provide clear and accurate information about the fundamental procedures for the implementation of statistical quality control; and encourage the use of this quality improvement tool. Originality/Value: The MCE control chart is a great differential for the improvement of the quality process of the studied company because it goes beyond what CUSUM and EWMA control charts can identify in terms of variability.

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Kang, Hae Woon, Chang Wook Kang, Jae Won Baik, and Sung Ho Nam. "Demerit-GWMA Control Chart for Demerit Statistics." Advanced Materials Research 156-157 (October 2010): 413–21. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.413.

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A classical Demerit control chart is used to monitor the process through which various types of defects in complex products, such as automobiles, computers, mobile phones, etc. are found in general. As a technique for rapidly detecting small shifts of the process mean in the control chart, the EWMA(exponentially weighted moving average) technique is very effective. This study suggested the Demerit-GWMA control chart, combining the GWMA(generally weighted moving average) technique, which shows better performance than EWMA technique in detecting small shifts of process mean, into the classical Demerit control chart, and evaluated its performance. Through the evaluation of its performance, it was found that the Demerit-GWMA control chart is more sensitive than both the classical Demerit control chart and the Demerit-EWMA control chart in detecting small shifts of process mean.

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Rashid, Kawa M. Jamal, and Suzan S. Haydar. "Construction of control charts by using Fuzzy Multinomial -FM and EWMA Chart “Comparative study"." Journal of Zankoy Sulaimani - Part A 16, no. 3 (July 3, 2014): 21–26. http://dx.doi.org/10.17656/jzs.10300.

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You, Huay Woon, Michael Khoo Boon Chong, Chong Zhi Lin, and Teoh Wei Lin. "The Expected Average Run Length of the EWMA Median Chart with Estimated Process Parameters." Austrian Journal of Statistics 49, no. 3 (February 20, 2020): 19–24. http://dx.doi.org/10.17713/ajs.v49i3.1020.

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The performance of a control chart is commonly investigated based on the assumption of known process parameters. Nevertheless, in most manufacturing and service applications, the process parameters are usually unknown to practitioners. Hence, they are estimated from an in-control Phase-I samples. As such, the performance of the control chart with estimated process parameters will behave differently from the corresponding chart with known process parameters. To study this issue, the exponentially weighted moving average (EWMA) median chart is examined in this article. The EWMA median chart is traditionally investigated based on the average run length (ARL). The limitation of the ARL is that it requires practitioners to specify the shift size in advance. This phenomenon is not ideal for practitioners who do not have background knowledge of the process. In view of this, the EWMA median chart with known and estimated process parameters is studied based on the ARL and expected average run length (EARL). The results indicate that as long as the particular shift size is within the range of shifts, the performance of the chart is almost the same, for the EWMA median chart with known and estimated process parameters.

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Supharakonsakun, Yadpirun, Yupaporn Areepong, and Saowanit Sukparungsee. "The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data." PeerJ 8 (December 15, 2020): e10467. http://dx.doi.org/10.7717/peerj.10467.

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PM2.5 (particulate matter less than or equal to 2.5 micron) is found in the air and comprises dust, dirt, soot, smoke, and liquid droplets. PM2.5 and carbon monoxide emissions can have a negative impact on humans and animals throughout the world. In this paper, we present the performance of a modified exponentially weighted moving average (modified EWMA) control chart to detect small changes when the observations are autocorrelated with exponential white noise through the average run length evaluated (ARLs) by explicit formulas. The accuracy of the solution was verified with a numerical integral equation method. The efficacy of the modified EWMA control chart to monitor PM2.5 and carbon monoxide air pollution data and compare its performance with the standard EWMA control chart. The results suggest that the modified EWMA control chart is far superior to the standard one.

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Chen, Jen-Hsiang. "A Double Generally Weighted Moving Average Chart for Monitoring the COM-Poisson Processes." Symmetry 12, no. 6 (June 16, 2020): 1014. http://dx.doi.org/10.3390/sym12061014.

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Generalized exponentially weighted moving average (EWMA) and double EWMA (DEWMA) charts based on the Conway–Maxwell–Poisson (CMP or COM-Poisson) distribution, also known as the GEWMA and CMP-DEWMA charts, are effectively used for monitoring the counts of non-conformities in a process. To further enhance their performance, this study utilizes design and adjustment parameters to develop generally weighted moving average (GWMA) and double GWMA charts, also known as the CMP-GWMA and CMP-DGWMA charts, to monitor COM-Poisson attributes. Numerical simulations indicate that the CMP-DGWMA chart outperforms its prototype CMP-DEWMA and CMP-GWMA charts in detecting small location and dispersion shifts, as well as both shifts together, in terms of average run lengths. Finally, an example is provided to demonstrate the efficiency of the proposed CMP-DGWMA chart and its counterparts.

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Huay Woon, You. "A Comparative Analysis of Control Charts for Monitoring Process Mean." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 2616–22. http://dx.doi.org/10.17762/turcomat.v12i3.1263.

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Control charts serve as an effective tool for controlling and monitoring process quality in industries of production and service. The Shewhart chart is the first control chart that was used to detect large mean shifts in a process. Since then, to increase the Shewhart chart’s sensitivity, synthetic type control charts, such as synthetic control chart, side sensitive group runs (SSGR) control chart, have been proposed. SSGR chart ismore efficient compared to the Shewhart chart and synthetic chart,primarily due to the side sensitive feature in SSGR chart. Meanwhile, exponentially weighted moving average (EWMA) chart isoften used to detect small process changes. In practice, the evaluation of a control chart’s performance is vital. Nevertheless, the cost of implementing a control chart is an important factor that influences the choice of a control chart. The cost of repairs, sampling, nonconforming products from a failure in detecting out-of-control status, and investigating false alarms, can be significantly high. Therefore, the aim of this paper is to compare the implementation cost of synthetic, SSGR and EWMA charts, so that quality practitioners can identify the most cost-effective chart to implement. Here, the cost function was adopted to compute the implementation cost of the control chart. According to the findings, quality practitioners are recommended to adopt the SSGR chart,since it is more economical compared to the synthetic chart. However, the cost to implement anEWMA chart is higher than the synthetic and SSGR charts. In light of this, this study allows for quality practitioners to have a better idea on the selection of the control chart to implement, with respect to its cost.

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Wang, Yan, Xuelong Hu, Xiaojian Zhou, Yulong Qiao, and Shu Wu. "New One-Sided EWMA t Charts without and with Variable Sampling Intervals for Monitoring the Process Mean." Mathematical Problems in Engineering 2020 (November 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/7567215.

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In statistical process control (SPC), t charts play a vital role in the monitoring of the process mean, especially when the process variance is unknown. In this paper, two separate upper-sided and lower-sided exponentially weighted moving average (EWMA) t charts are first proposed and the Monte Carlo simulation method is used to obtain their run length (RL) properties. Compared with the traditional one-sided EWMA t charts and several run rules t charts, the proposed charts are proven to have better performance than these competing charts. In addition, by adding the variable sampling interval (VSI) feature to the proposed charts, the new VSI one-sided EWMA t charts are shown to detect different shift sizes in the process more efficient than the chart without VSI feature. Finally, an example of a milk filling process illustrates the use of the charts.

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Wang, Hai Yu. "Comparing Robustness of EWMA Dispersion Control Chart for Non-Normal Process." Advanced Materials Research 912-914 (April 2014): 1189–92. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1189.

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This article discusses robustness to non-normality of EWMA charts for dispersion. Comparison analysis of run length of four kinds of EWMA charts to monitoring process dispersion is provided to evaluate control charts performance and robustness. At last robust EWMA dispersion charts for non-normal processes are proposed by this way.

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Kang, Hae Woon, Jae Won Baik, Young Jae Choi, and Sung Ho Nam. "Demerit-GWMA Control Chart with Fast Initial Response." Applied Mechanics and Materials 157-158 (February 2012): 1655–60. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1655.

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Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective systems to monitoring the different types of defects. So, classical demerit control chart used to monitor counts of several different types of defects simultaneously in complex products. Recently, H.W. Kang et al.[7] introduced Demerit-GWMA(generally weighted moving average) and Demerit-EWMA control charts that can detect small shifts of the process mean more sensitively than the classical demerit control charts. In this paper, we present an effective method for process control using the Demerit-GWMA statistics with fast initial response. Moreover, we evaluate exact performance of the Demerit-GWMA control chart with fast initial response(FIR), Demerit-GWMA and Demerit-EWMA according to changing sample size or parameters.

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Lopes, Allan Remor, Marcio Antonio Vilas Boas, Felix Augusto Pazuch, Diane Aparecida Ostroski, and Marta Juliana Schmatz. "Control charts for monitoring drip irrigation with different hydraulic heads." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 15, no. 4 (July 8, 2020): 1. http://dx.doi.org/10.4136/ambi-agua.2554.

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This study monitored a drip irrigation system with different hydraulic heads, using control charts. The study included 25 tests, and was conducted at the Experimental Nucleus of Agricultural Engineering of the State University of Western Paraná, located in the municipality of Cascavel, Paraná. The drip irrigation system was operated by gravity, and had four hydraulic heads (10, 11, 12 and 15 kPa). The uniformity of the system was determined based on uniformity distribution. Uniformity monitoring was performed using Shewhart and exponentially weighted moving-average (EWMA) control charts. An increase in the hydraulic head increased uniformity. The use of 12 and 15 kPa hydraulic heads yielded good performance, whereas 10 and 11 kPa yielded regular performance. The use of control charts proved to be efficient; the Shewhart control chart was more robust, whereas the EWMA control chart, which indicated trends and deviations not shown by Shewhart control charts, was more sensitive.

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CASTAGLIOLA, PHILIPPE. "An $(\tilde{X}/R)$-EWMA CONTROL CHART FOR MONITORING THE PROCESS SAMPLE MEDIAN." International Journal of Reliability, Quality and Safety Engineering 08, no. 02 (June 2001): 123–35. http://dx.doi.org/10.1142/s0218539301000414.

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The method proposed in this paper is a new EWMA type control chart, dedicated to the monitoring of the process sample median [Formula: see text]. Because this control chart uses the range of the process, we call it a [Formula: see text]-EWMA control chart. In this paper, we show how to compute the control limits of this chart, give an illustrative example, describe how to compute the ARL and how to obtain optimal parameters minimizing the out-of-control ARL.

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Goswami, Ajit. "A Study on ARL Performance of Ewma Control Chart." Asian Journal of Current Engineering and Maths 3, no. 4 (July 28, 2014): 39–41. http://dx.doi.org/10.15520/ajcem.2014.vol3.iss4.1.pp39-41.

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Zou, Changliang, and Fugee Tsung. "A Multivariate Sign EWMA Control Chart." Technometrics 53, no. 1 (February 2011): 84–97. http://dx.doi.org/10.1198/tech.2010.09095.

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Gan, F. F. "Ewma control chart under linear drift." Journal of Statistical Computation and Simulation 38, no. 1-4 (May 1991): 181–200. http://dx.doi.org/10.1080/00949659108811328.

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Prabhu, Sharad S., and George C. Runger. "Designing a Multivariate EWMA Control Chart." Journal of Quality Technology 29, no. 1 (January 1997): 8–15. http://dx.doi.org/10.1080/00224065.1997.11979720.

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Albin, Susan L., Lan Kang, and Gerald Shea. "AnXand EWMA Chart for Individual Observations." Journal of Quality Technology 29, no. 1 (January 1997): 41–48. http://dx.doi.org/10.1080/00224065.1997.11979723.

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Journal articles on the topic 'EWMA chart'

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