Journal articles: 'Shewhart's control charts'

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BALKIN, SANDY D., and DENNIS K. J. LIN. "PERFORMANCE OF SENSITIZING RULES ON SHEWHART CONTROL CHARTS WITH AUTOCORRELATED DATA." International Journal of Reliability, Quality and Safety Engineering 08, no. 02 (June 2001): 159–71. http://dx.doi.org/10.1142/s0218539301000438.

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Sensitizing Rules are commonly applied to Shewhart Charts to increase their effectiveness in detecting shifts in the mean that may otherwise go unnoticed by the usual "out-of-control" signals. The purpose of this paper is to demonstrate how well these rules actually perform when the data exhibit autocorrelation compared to non-correlated data. Since most control chart data are collected as time series, it is of interest to examine the performance of Shewhart's [Formula: see text] Chart using data generated from typical time series models. In this paper, measurements arising from autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) processes are examined using Shewhart Control Charts in conjunction with several sensitizing rules. The results indicate that the rules work well when there are strong autocorrelative relationships, but are not as effective in recognizing small to moderate levels of correlation. We conclude with the recommendation to practitioners that they use a more definitive measure of autocorrelation such as the Sample Autocorrelation Function correlogram to detect dependency.

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Adams, Benjamin M. "Advanced Topics in Statistical Process Control: The Power of Shewhart's Charts." Technometrics 38, no. 3 (August 1996): 286. http://dx.doi.org/10.1080/00401706.1996.10484510.

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Lovelace, Cynthia R. "Advanced Topics in Statistical Process Control: The Power of Shewhart's Charts." Journal of Quality Technology 28, no. 1 (January 1996): 127. http://dx.doi.org/10.1080/00224065.1996.11979644.

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Czabak-Górska, Izabela Dagmara. "THE CLASSIFICATION AND CHARACTERISTICS OF CONTROL CHARTS." CBU International Conference Proceedings 5 (September 22, 2017): 86–93. http://dx.doi.org/10.12955/cbup.v5.907.

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Control Charts are the basic tool for quality control. They were developed in the 1920s when the dominant type of production was mass production. In order to properly use classic Control Charts, the data from the manufacturing process should meet the following assumptions: an empirical distribution of measurement data should be normally distributed or close to a normal distribution, measurement data should be independent, the manufacturing process should be capable of quality depending on the type of Control Chart, a sample that is large enough (sometimes made of several elements) must be taken. Currently, a shift can be observed from mass production towards short production runs, which causes the proper use of the traditional approach to be impossible. In recent years, control charts are once again in the spotlight, and consequently many scientists, i.e. Reynolds, Zimmer, Costa, Calvin and Chan have undertaken the task to adapt the classic idea of keeping Control Charts to modern conditions of production. The development of science in this area allows for the avoidance of making major mistakes in the conduct of Control Charts and for making the wrong decisions based on erroneous analysis. However, the appearance of new literature pieces implies the need to classify Control Charts, therefore, this article describes the idea of conduct, the most important assumptions and distribution of classical Shewhart's Control Charts, as well as a suggestion for the distribution of advanced Control Charts that meet the needs of the currently used production types. The work also contains a concise description of the chosen control charts as well as the threats resulting from their inappropriate selection. This elaboration is an extension to the article of Czabak-Górska (2017).

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Chesher, D., and L. Burnett. "Using Shewhart p control charts of external quality-assurance program data to monitor analytical performance of a clinical chemistry laboratory." Clinical Chemistry 42, no. 9 (September 1, 1996): 1478–82. http://dx.doi.org/10.1093/clinchem/42.9.1478.

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Abstract We have investigated the application of Shewhart's p control charts in our external quality-assurance program to monitor the long-term performance of our laboratory's analytical quality. The p control charts have been able to detect long-term changes in our laboratory's analytical performance that would have been difficult to detect by more-conventional techniques. We have explored methods for interpreting these charts as well as some of their limitations, which include minimum subgroup size and dependence on constant specification limits. These charts may be not only a simple method for the long-term monitoring of analytical performance of a laboratory, but also of use to the organizers of external quality-assurance programs.

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Avakh Darestani, Soroush, Azam Moradi Tadi, Somayeh Taheri, and Maryam Raeiszadeh. "Development of fuzzy U control chart for monitoring defects." International Journal of Quality & Reliability Management 31, no. 7 (July 29, 2014): 811–21. http://dx.doi.org/10.1108/ijqrm-03-2013-0048.

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Purpose – Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart. Design/methodology/approach – If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified. Findings – The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns. Originality/value – This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

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Orssatto, Fábio, Marcio A. Vilas Boas, Ricardo Nagamine, and Miguel A. Uribe-Opazo. "Shewhart's control charts and process capability ratio applied to a sewage treatment station." Engenharia Agrícola 34, no. 4 (August 2014): 770–79. http://dx.doi.org/10.1590/s0100-69162014000400016.

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The current study used statistical methods of quality control to evaluate the performance of a sewage treatment station. The concerned station is located in Cascavel city, Paraná State. The evaluated parameters were hydrogenionic potential, settleable solids, total suspended solids, chemical oxygen demand and biochemical oxygen demand in five days. Statistical analysis was performed through Shewhart control charts and process capability ratio. According to Shewhart charts, only the BOD(5.20) variable was under statistical control. Through capability ratios, we observed that except for pH the sewage treatment station is not capable to produce effluents under characteristics that fulfill specifications or standard launching required by environmental legislation.

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Bachtiak-Radka, Emilia, Sara Dudzińska, and Daniel Grochała. "Application of Shewhart's control card to supervise the quality of manufacturing process in the automotive industry." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 9 (September 30, 2018): 108–11. http://dx.doi.org/10.24136/atest.2018.295.

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The manuscript presents the use of Shewhart control charts to supervision the quality of production processes in the automotive industry of the gearbox automatic. At the request of the company, control charts were prepared for individual characteristic geometrical features. This paper presents the procedure for preparing the control card for monitoring the shape characteristics in an automated production socket.

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DELGADO, M., P. OLAVARRIETA, and P. VERGARA. "FUZZY SET BASED PROTOCOLS FOR PROCESS QUALITY CONTROL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 01 (February 2006): 61–76. http://dx.doi.org/10.1142/s0218488506003832.

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Industrial process quality control has as yet been carried out using Shewhart's classic charts and control charts with probabilistic limits, using sampling statistics for average and deviation [Formula: see text] and [Formula: see text], respectively, or Cp and Cpk, derived from them, in order to determine whether the process is precise or imprecise. Although these statistics has been formulated using crisp mathematics, their use returns statements about "quality control" which are full of vagueness (for example, the aforementioned idea of precise or imprecise processes). For this reason, it seems both natural and interesting to introduce tools from Fuzzy Sets Theory for the formulation of quality control models. Fuzzy Sets shall be used to study process quality capability and to generate a bilateral simultaneous control for the central tendency and a unilateral one for variability. We shall define linguistic rules in order to perform this control and membership functions for the sample control mean and deviation, [Formula: see text] and ŝ.

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Sparks, Ross S., and John B. F. Field. "Using Deming's Funnel Experiment to Demonstrate Effects of Violating Assumptions Underlying Shewhart's Control Charts." American Statistician 54, no. 4 (November 2000): 291. http://dx.doi.org/10.2307/2685781.

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Sparks, Ross S., and John B. F. Field. "Using Deming's Funnel Experiment to Demonstrate Effects of Violating Assumptions Underlying Shewhart's Control Charts." American Statistician 54, no. 4 (November 2000): 291–302. http://dx.doi.org/10.1080/00031305.2000.10474562.

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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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Smajdorová, Tereza, and Darja Noskievičová. "Methodology for the Application of Nonparametric Control Charts into Practice." Emerging Science Journal 4, no. 4 (August 1, 2020): 272–82. http://dx.doi.org/10.28991/esj-2020-01230.

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Classical parametric statistical methods are based on several basic assumptions about data (normality, independence, constant mean and variance). Unfortunately, these assumptions are not always fulfilled in practice, whether due to problems arising during manufacturing or because these properties are not typical for some processes. Either way, when we apply parametric methods to such data, whether Shewhart’s or other types of parametric control charts, it is not guaranteed that they will provide the right results. For these cases, reliable nonparametric statistical methods were developed, which are not affected by breaking assumptions about the data. Nonparametric methods try to provide suitable procedures to replace commonly used parametric statistical methods. The aim of this paper is to introduce the reader to an alternative way of evaluating the statistical stability of the process, in cases where the basic assumptions about the data are not met. First, possible deviations from the data assumptions that must be met in order to use classical Shewhart control charts were defined. Subsequently, simulations were performed to determine which nonparametric control chart was better suited for which type of data assumption violation. First, simulations were performed for the in-control process. Then simulations for an out-of-control process were performed. This is for situations with an isolated and persistent deviation. Based on the performed simulations, flow charts were created. These flow charts give the reader an overview of the possibilities of using nonparametric control charts in various situations. Based on the performed simulations and subsequent verification of the methodology on real data, it was found that nonparametric control charts are a suitable alternative to the standard Shewhart control charts in cases where the basic assumptions about the data are not met.

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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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FRIGO, JIAM PIRES, MARCIO ANTONIO VILAS BOAS, JIANICE PIRES FRIGO, and ELISANDRO PIRES FRIGO. "COMPARAÇÃO ENTRE GRÁFICOS DE CONTROLE DE SHEWHART, CUSUM E MMEP NO PROCESSO DE IRRIGAÇÃO POR ASPERSÃO CONVENCIONAL." IRRIGA 1, no. 01 (June 18, 2018): 56. http://dx.doi.org/10.15809/irriga.2016v1n01p56-70.

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COMPARAÇÃO ENTRE GRÁFICOS DE CONTROLE DE SHEWHART, CUSUM E MMEP NO PROCESSO DE IRRIGAÇÃO POR ASPERSÃO CONVENCIONAL JIAM PIRES FRIGO1; MARCIO ANTONIO VILAS BOAS2; JIANICE PIRES FRIGO2 E ELISANDRO PIRES FRIGO3 1 Instituto Latino-Americano de Tecnologia, Infraestrutura e Território - ILATIT (UNILA), Av. Tancredo Neves, 3838 - Porto Belo, CEP 85867-970, Foz do Iguaçu, PR, Fone (45)99993 4783, e‑mail:jianfrigo@gmail.com2 Centro de Ciências Exatas e Tecnológicas, Programa de Pós Graduação em Engenharia Agrícola (Unioeste), R. Universitária, 2069 - Jardim Universitário CEP 85819-110, Cascavel-PR3 Universidade Federal do Paraná, Campus Palotina R. Pioneiro, 2153 - Dallas, CEP 85950-000, Palotina-PR 1 RESUMO O objetivo deste estudo foi comparar os resultados da utilização dos gráficos de controle de Shewhart para medidas individuais, com os gráficos de controle média móvel exponencialmente ponderada (MMEP) e soma cumulativa (CUSUM), aplicados no controle de qualidade da irrigação. Foram realizados 60 ensaios de irrigação em um sistema por aspersão convencional. As análises do processo de controle de qualidade do sistema de irrigação foram realizadas por meio dos gráficos de Shewhart (Xbarra), gráficos MMEP e CUSUM. Todos os procedimentos para os ensaios de uniformidade da irrigação foram realizados conforme recomendação NBR ISO 7749-2 (ABNT, 2000). Para a avaliação do sistema foi utilizado o Coeficiente de Uniformidade de Christiansen (CUC). O gráfico de controle MMEP apresentou-se bastante suscetível quando utilizado em dados auto correlacionados, com ocorrências de alarmes falsos. Para dados independentes (pelo modelo ARIMA), o gráfico CUSUM foi mais sensível ao detectar as variações ocorridas na irrigação devido à velocidade do vento, quando comparado aos gráficos MMEP e Shewhart para os mesmos dados. Na irrigação por aspersão, relacionando CUC com velocidade do vento, o gráfico de Shewhart foi mais indicado pela simplicidade, robustez e facilidade de interpretação, mesmo na presença de dados que violam a suposição de independência. Os gráficos de controle de Shewhart, MMEP e CUSUM provaram serem ótimas ferramentas estatísticas no estudo da irrigação por aspersão convencional, demonstrando muito bem a variabilidade no processo. Palavras-Chave: água, vento, Coeficiente de Christiansen, Controle de qualidade. FRIGO, J.P.; VILAS BOAS, M.A.; FRIGO, J.P.; FRIGO, E.P.COMPARISON BETWEEN SHEWHART CONTROL CHARTS, CUSUM AND MMEP IN PROCESS OF CONVENTIONAL IRRIGATION SPRINKLER 2 ABSTRACT This study aimed to compare the results of Shewhart control charts use for individual measures with exponentially weighted moving average (MMEP) and cumulative sum (CUSUM) control charts applied in quality control of conventional sprinkler irrigation. Sixty irrigation trials were set up in a conventional sprinkler system. The analyses of the quality control process of the irrigation system were performed by means of Shewhart charts (Xbarra) charts, MMEP and CUSUM. All procedures for testing uniformity of irrigation were performed as recommended by ISO 7749-2 (ABNT, 2000). For the evaluation of the system, it was used Christiansen Uniformity Coefficient (CUC). The control chart MMEP showed to be quite susceptible when used in auto correlated data with instances of false alarms. For independent data (the ARIMA model), the CUSUM tabular chart was more sensitive to detect variations in irrigation due to wind speed, when compared to MMEP and Shewhart charts for the same data. In sprinkler irrigation, relating CUC with wind speed, the Shewhart chart was better due to such features as simplicity, robustness and easiness of interpretation, even in the presence of data that violate the assumption of independence. The Shewhart control charts, CUSUM and MMEP statistics proved to be great tools in the study of irrigation sprinkler, demonstrating very well the variability in the process. Keywords: water, wind, Christiansen coefficient, control charts, Quality control.

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Sun, Qiu Xia, Jian Li Zhao, and Qi Sheng Gao. "Average Run Lengths in Shewhart Type Charts for 2-Order Autoregressive Process." Advanced Materials Research 139-141 (October 2010): 1860–63. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1860.

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In this paper the average run length is adopted as the tool to describe the performance of control charts. The respective methods for calculating the average run length of the modified Shewhart control chart and the Shewhart residual control chart for 2-order autoregressive process are derived and shown in detail. By the proposed approach some numerical results of average run lengths of both Shewhart type charts are formulated and discussed. We analyze and compare that the influence of the correlation coefficients of the 2-order autoregressive process on the performance of both charts based on the estimated data. Several clear and main points of the issue are summed up. Lastly, we give some recommendations for the choice of both Shewhart type control schemes.

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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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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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van der Veer, S. N., K. J. Jager, N. Peek, N. F. de Keizer, and A. Koetsier. "Control Charts in Healthcare Quality Improvement." Methods of Information in Medicine 51, no. 03 (2012): 189–98. http://dx.doi.org/10.3414/me11-01-0055.

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SummaryObjectives: Use of Shewhart control charts in quality improvement (QI) initiatives is increasing. These charts are typically used in one or more phases of the Plan Do Study Act (PDSA) cycle to monitor summaries of process and outcome data, abstracted from clinical information systems, over time. We summarize methodological criteria of Shewhart control charts and investigate adherence of published QI studies to these criteria.Methods: We searched Medline, Embase and CINAHL for studies using Shewhart control charts in QI processes in direct patient care. We extracted methodological criteria for Shewhart control charts, and for the use of these charts in PDSA cycles, from textbooks and methodological literature.Results: We included 34 studies, presenting 64 control charts of which 40 control charts plotted two phases of the PDSA cycle. The criterion to use 10–35 data points in a control chart was least adhered to (48.4% non-adherence). Other criteria were: transformation of the data in case of a skewed distribution (43.7% non adherence), when comparing data from two phases of the PDSA cycle the Plan phase (the first phase) needs to be stable (40.0% non-adherence), using a maximum of four different rules to detect special cause variation (14.1% non-adherence), and setting control limits at three standard deviations from the mean (all control charts adhered).Conclusion: There is room for improvement with regard to the methodological construction of Shewhart control charts used in QI processes. Higher adherence to all methodological criteria will decrease the risk of incorrect conclusions about the process being monitored.

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Pekin Alakoc, N., and A. Apaydin. "A Fuzzy Control Chart Approach for Attributes and Variables." Engineering, Technology & Applied Science Research 8, no. 5 (October 13, 2018): 3360–65. http://dx.doi.org/10.48084/etasr.2192.

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The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

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Diering, Magdalena, Adam Hamrol, and Agnieszka Kujawińska. "Measurement System Analysis Combined with Shewhart’s Approach." Key Engineering Materials 637 (February 2015): 7–11. http://dx.doi.org/10.4028/www.scientific.net/kem.637.7.

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The paper presents new procedure of methodology for statistical assessment of measurement systems variation (methodology known in the literature as Measurement Systems Analysis, MSA). This procedure allows for calculation and monitoring in real time (that is on-line) of measurement system (MS) characteristics which determine its usability for manufacturing process control. The presented solution pointed out the gap in process control, which consists in lack of methods for monitoring measurement processes in the on-line way. Their key point consists of taking samples that are also needed for the process control chart for the needs of the MSA method. This means that the samples are taken directly from the production line and during the production process. The method is combined with the standard procedure of statistical process control (SPC) with the use of process control charts. It is based on two control charts. The first one is called AD-chart (Average Difference chart) and it allows to estimate the variation between the operators and stability of the monitored measurement system. The second control chart illustrates the %R&R index (Repeatability and Reproducibility) and allows to monitor the MS capability.The paper also presents authors’ proposal of guidelines about the reference value for the %R&R index calculation and assessment. Recommendations and guidelines for choosing the reference value are based on two criteria: information about sample and manufacturing process variation and the purpose of using MS (product or process control).

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Aako, O. L., J. A. Adewara, K. S. Adekeye, and E. B. Nkemnole. "X̅ and R control charts based on marshall-olkin inverse log-logistic distribution for positive skewed data." African Journal of Pure and Applied Sciences 1, no. 1 (November 24, 2020): 9–16. http://dx.doi.org/10.33886/ajpas.v1i1.167.

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The fundamental assumption of variable control charts is that the data are normally distributed and spread randomly about the mean. Process data are not always normally distributed, hence there is need to set up appropriate control charts that gives accurate control limits to monitor processes that are skewed. In this study Shewhart-type control charts for monitoring positively skewed data that are assumed to be from Marshall-Olkin Inverse Loglogistic Distribution (MOILLD) was developed. Average Run Length (ARL) and Control Limits Interval (CLI) were adopted to assess the stability and performance of the MOILLD control chart. The results obtained were compared with Classical Shewhart (CS) and Skewness Correction (SC) control charts using the ARL and CLI. It was discovered that the control charts based on MOILLD performed better and are more stable compare to CS and SC control charts. It is therefore recommended that for positively skewed data, a Marshall-Olkin Inverse Loglogistic Distribution based control chart will be more appropriate.

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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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Aslam, Muhammad, Nasrullah Khan, and Mohammed Albassam. "Shewhart Attribute and Variable Control Charts Using Modified Multiple Dependent State Sampling." Symmetry 11, no. 1 (January 5, 2019): 53. http://dx.doi.org/10.3390/sym11010053.

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In this article, modified multiple dependent (or deferred) state sampling control charts for the attribute and the variable quality characteristics are presented. The proposed control charts are designed using the symmetry property of the normal distribution. The control chart coefficients are estimated through simulation at different levels of the parameters using the normal distribution. The proposed control chart scheme is evaluated by calculating the in-control average run lengths and out-of-control average run lengths. Tables are constructed for the selection of parameters for different control limit coefficients under several shift levels for the attribute data as well as the variable data. Examples are included for the practical application of the proposed control chart schemes. The proposed control chart scheme is also compared with the existing control charts. It has been observed that the proposed schemes are better in quick detection of the out-of-control processes.

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Yang, Su Fen, Chung Ming Yang, and Ging Yun Cheng. "Effects of Measurement Error on Process Signal Detection." Applied Mechanics and Materials 263-266 (December 2012): 496–500. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.496.

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Many data comes from a process with variables having non-normal distributions. The commonly used Shewhart variable control charts which depend heavily on the normality assumption should not be applied here. Further, measurement error of an instrument or gauge is an important factor in industry that influences the outcome of a process. Incapability of the instrument or gauge would cause the observation measure deviates from the true value and consequently lead to a false discrimination between good and bad service quality and manufacturing parts. In this study, we propose a new EWMA control chart to monitor the exponentially distributed service time between consecutive events with the measurement error instead of monitoring the number of events in a given time interval. We showed that, in the case of the measurement error following a normal distribution, the performance of the EWMA-M chart is significantly affected. We suggest letting for keeping the chart with the same detection ability; otherwise, the chart’s detection ability would be much poor than the charts with no measurement errors

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Pimenta, Cristie Diego, Messias Borges Silva, Fernando Augusto Silva Marins, and Aneirson Francisco da Silva. "Application of Statistical Monitoring Using Autocorrelated Data and With the Influence of Multicollinearity in a Steel Process." International Journal of Statistics and Probability 10, no. 4 (June 22, 2021): 96. http://dx.doi.org/10.5539/ijsp.v10n4p96.

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The purpose of this article is to demonstrate a practical application of control charts in an industrial process that has data auto-correlated with each other. Although the control charts created by Walter A. Shewhart are very effective in monitoring processes, there are very important statistical assumptions for Shewhart's control charts to be applied correctly. Choosing the correct Control Chart is essential for managers to be able to make coherent decisions within companies. With this study, it was possible to demonstrate that the original data collected in the process, which at first appeared to have many special causes of variation, was actually a stable process (no anomalies present). However, this finding required the use of autoregressive models, multivariate statistics, autocorrelation and normality tests, multicollinearity analysis and the use of the EWMA control chart. It was concluded that it is of fundamental importance to choose the appropriate control chart for monitoring industrial processes, to ensure that changes in processes do not incorporate non-existent variations and do not cause an increase in the number of defective products.

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Shah, Said Farooq, Zawar Hussain, Muhammad Riaz, and Salman Arif Cheema. "Shewhart-Type Charts for Masked Data: A Strategy for Handling the Privacy Issue." Mathematical Problems in Engineering 2020 (September 26, 2020): 1–11. http://dx.doi.org/10.1155/2020/5104753.

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Data privacy is a serious issue and therefore needs our attention. In this study, we propose masking through randomized response techniques (RRTs) to ensure the privacy and thus to avoid falsification. We assume that the process characteristic is of sensitive nature, and due to privacy issue, the actual measurements cannot be shared with the monitoring team. In such situations, the producer is very likely to falsify the measurements. Consequently, the usual control charting techniques will mislead about the process status. We discuss different data masking strategies to be used with Shewhart-type control charts. The usual Shewhart-type control chart appears to be a subchart of the proposed charts. Average run length (ARL) is used as a performance measure of the study proposals. We have evaluated the performance of the proposed charts for different shift sizes and under different intensities of masking. We have also carried out a comparative analysis for various models under varying sensitivity parameters. We have also compared the performance of the proposals with the traditional Shewhart chart. It is observed that the B-L control chart under the RRT model performs better for smaller shifts and for larger shift sizes, the G-B chart under an unrelated question model tperforms better. A real-life application of the study proposal is also considered where monitoring of thickness of paint on refrigerators is of interest.

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Zasadzień, Michał, and Katarzyna Midor. "Statistical Process Control as a Failure Removal Improvement Tool." Acta Technologica Agriculturae 21, no. 3 (September 1, 2018): 124–29. http://dx.doi.org/10.2478/ata-2018-0023.

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Abstract Continuous improvement should be a part of the strategy of every modern company that wishes to meet the requirements posed by the demanding, competitive market. The article presents a concept of applying a tool known from quality engineering, i.e. Shewhart control charts, for the improvement of the maintenance process in a small company providing services for the agricultural and construction industries. The improvement of the process included the reducing of the downtime of belt conveyors due to failures. Using control charts allowed the detection of interferences in the process and defining of their nature. Using other tools such as 5 WHY allowed the identification of the root causes of overly long downtimes and, consequently, formulation and implementation of improvements and preventive measures that were optimal for the organisation. The verification of actions taken has shown their positive impact on the process, which was reflected in the shortening of downtime and, subsequently, streamlining the failure removal process. The paper presents a possibility and validity of utilising quality engineering tools, such as Shewhart’s charts, 5 WHY or Ishikawa diagram, for improving the maintenance processes.

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HUMAN, S. W., and S. CHAKRABORTI. "A UNIFIED APPROACH FOR SHEWHART-TYPE PHASE I CONTROL CHARTS FOR THE MEAN." International Journal of Reliability, Quality and Safety Engineering 17, no. 03 (June 2010): 199–208. http://dx.doi.org/10.1142/s0218539310003755.

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The false alarm probability (FAP) is the metric typically used to design and evaluate the performance of Phase I control charts. It is shown that in situations where the exact or the asymptotic joint p.d.f. of the standardized charting statistics follows a singular standard multivariate normal distribution with a common negative correlation, the FAP of some Shewhart-type Phase I charts for the mean can be expressed as a multiple integral of the joint p.d.f. Hence the required charting constants can be calculated (and the control chart can be implemented) by evaluating this integral. A table with the charting constants is provided for some popular choices of the nominal FAP (denoted FAP0) and the number of Phase I samples, m. The proposed methodology is useful to unify some existing Phase I charts and is illustrated with two charts from the literature: the p chart for the fraction nonconforming and the [Formula: see text] chart for the mean. A summary and some conclusions are provided.

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Huang, Wei-Heng, and Arthur B. Yeh. "A Nonparametric Phase I Control Chart for Monitoring the Process Variability with Individual Observations Based on Empirical Likelihood Ratio." International Journal of Reliability, Quality and Safety Engineering 25, no. 03 (April 23, 2018): 1850015. http://dx.doi.org/10.1142/s0218539318500158.

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Among the statistical process control (SPC) techniques, the control chart has been proven to be effective in process monitoring. The Shewhart chart is one of the most commonly used control charts for monitoring the process mean and variability based on the assumption that the distribution of the quality characteristic is normal. However, in practice, many quality characteristics are not normally distributed. Most of the existing nonparametric control charts are designed for Phase II monitoring. Little has been done in developing the nonparametric Phase I control charts especially for individual observations. In this work, we propose a new nonparametric Phase I control chart for monitoring the scale parameter based on the empirical likelihood ratio test. The simulation results show that the proposed chart is more effective than the existing charts in terms of signal probability. A real example is used to demonstrate how the proposed chart can be applied in practice.

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Shehab, Randa L., Robert E. Schlegel, and Kirby Gilliland. "The Use of Statistical Quality Control Charts to Evaluate Changes in Individual Performance." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 1 (October 1997): 569–73. http://dx.doi.org/10.1177/1071181397041001126.

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An employee's readiness to perform (RTP) has become an important issue facing today's industries. Some industries have turned to cognitive performance testing to provide answers regarding their employee's abilities to work safely and effectively. Such tests are designed to assess the employee's current state of preparedness for work without identifying specific causes for any noted performance impairment. This paper evaluates performance-based RTP tests with regard to the metrics by which performance change is judged. In addition to evaluating a current commonly used metric, several statistical quality control charts were examined as alternate methods for identifying impaired performance. Traditional Shewhart charts were used as well as exponentially weighted moving average charts and cumulative sum charts. A comparative analysis of the various methods revealed that control chart techniques provided superior effectiveness over the current method. Specifically, exponentially weighted moving average charts were effective in evaluating continuous performance measures and Shewhart p charts were effective in evaluating discrete measure data.

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Alghamdi, Nawal G., and Muhammad Aslam. "Improving Benchmarking Student Learning Outcomes Using Repetitive Sampling Control Chart." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7036–39. http://dx.doi.org/10.1166/jctn.2016.5668.

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In recent years research on the application of Shewhart control charts in evaluating the performance of educational programs have gain sufficient grounds. These control charts aid in process understanding and identify changes that indicate either improvement or deterioration in quality of the program. Current research proposes control charts using repetitive sampling on the data taken from Weber State University’s construction management program, which uses the Associate Constructor Level 1 exam as an assessment tool. A code was developed to run the proposed control charts. Both the traditional and proposed charts were plotted using R software. The results indicate that the proposed control charts are comparatively more efficient than the traditional control charts in assessment of educational programs and minimizing false positives. At the end comparison of the benchmark—pass rate and traditional control chart with the proposed control chart has also been elucidated so that the proposed control charts may be readily employed in evaluating any educational program by academic counsellors.

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Noskievičová, Darja, and Tereza Smajdorová. "Proposal of Methodology for Practical Application of Nonparametric Control Charts." Quality Production Improvement - QPI 1, no. 1 (July 1, 2019): 464–71. http://dx.doi.org/10.2478/cqpi-2019-0063.

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Abstract This paper deals with the methodology for practical application of nonparametric control charts. This topic is very important for two reasons: firstly nonparametric control charts are very effective instruments for the realization of the statistical process monitoring phase I due to their robustness against various deviations from the data assumptions that must be met when applying model-based control charts. Secondly nonparametric control charts have very weak SW support and also they are not taught in the frame of training courses not even of the university study programmes. For that reason the practitioners do not know them and do not use them. The paper offers the proposal how to practically apply these control charts which is based on the complex simulation study of various nonparametric control charts performance when various data assumptions have not been met. The study has covered these nonparametric control charts: Shewhart sign control chart, nonparametric EWMA and nonparametric CUSUM control charts, nonparametric progressive mean control chart, control chart based on Mood statistics and robust median absolute deviation control chart. All charts have been studied in condition of not normally distributed data, autocorrelated data and data with nonconstant distribution parameters. The simulations were realized for statistically stable (IC – in control) and also statistically unstable (OC – out of control) processes. For the evaluation of the control charts performance median run length, 0.05-quantile, and 0.95-quantile were used.

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Claro, Fernando A. E., Antonio F. B. Costa, and Marcela A. G. Machado. "Double sampling control chart for a first order autoregressive process." Pesquisa Operacional 28, no. 3 (December 2008): 545–62. http://dx.doi.org/10.1590/s0101-74382008000300008.

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In this paper we propose the Double Sampling control chart for monitoring processes in which the observations follow a first order autoregressive model. We consider sampling intervals that are sufficiently long to meet the rational subgroup concept. The Double Sampling chart is substantially more efficient than the Shewhart chart and the Variable Sample Size chart. To study the properties of these charts we derived closed-form expressions for the average run length (ARL) taking into account the within-subgroup correlation. Numerical results show that this correlation has a significant impact on the chart properties.

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Mughal, Muhammad, Muhammad Azam, and Muhammad Aslam. "An EWMA-DiD Control Chart to Capture Small Shifts in the Process Average Using Auxiliary Information." Technologies 6, no. 3 (July 30, 2018): 69. http://dx.doi.org/10.3390/technologies6030069.

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Among the Statistical Process Control (SPC) techniques, control charts are considered to be high weight-age due to their effectiveness in process variation. As the Shewhart’s charts are not that active in monitoring small and moderate process variations, the statisticians have been making efforts to improve the performance of the control chart by introducing several techniques within the tool. These techniques consist of experimenting with different estimators, different sampling selection techniques, and mixed methodologies. The proposed chart is one of the examples of a mixed chart technique that has shown its efficiency in monitoring small variations better than any of the existing techniques in the specific situation of auxiliary information. To show and compare its performance, average run length (ARL) tables and ARL curves have been presented in the article. An industrial example has also been included to show the practical application of the proposed chart in a real scenario.

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Ion, Roxana A., and Chris A. J. Klaassen. "Non-parametric Shewhart control charts." Journal of Nonparametric Statistics 17, no. 8 (December 2005): 971–88. http://dx.doi.org/10.1080/10485250500393271.

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Nelson, Lloyd S. "Interpreting Shewhart X̄ Control Charts." Journal of Quality Technology 17, no. 2 (April 1985): 114–16. http://dx.doi.org/10.1080/00224065.1985.11978945.

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Nelson, Lloyd S. "Standardization of Shewhart Control Charts." Journal of Quality Technology 21, no. 4 (October 1989): 287–89. http://dx.doi.org/10.1080/00224065.1989.11979187.

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DOKOUHAKI, PERSHANG, and RASSOUL NOOROSSANA. "SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1250083. http://dx.doi.org/10.1142/s0219519412500832.

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In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

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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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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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Frisén, Marianne. "On multivariate control charts." Production 21, no. 2 (February 18, 2011): 235–41. http://dx.doi.org/10.1590/s0103-65132011005000010.

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Industrial production requires multivariate control charts to enable monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. In the literature, several types of multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have been proposed. We review general approaches to multivariate control chart. Suggestions are made on the special challenges of evaluating multivariate surveillance methods.

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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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Magalhães, Maysa Sacramento de, and Francisco Duarte Moura Neto. "Economic-statistical design of variable parameters non-central chi-square control chart." Production 21, no. 2 (June 17, 2011): 259–70. http://dx.doi.org/10.1590/s0103-65132011005000031.

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Production processes are monitored by control charts since their inception by Shewhart (1924). This surveillance is useful in improving the production process due to increased stabilization of the process, and consequently standardization of the output. Control charts keep track of a few key quality characteristics of the outcome of the production process. This is done by means of univariate or multivariate charts. Small improvements in control chart methodology can have significant economic impact in the production process. In this investigation, we propose the monitoring of a single variable by means of a variable parameter non-central chi-square control chart. The design of the chart is accomplished by means of optimizing a cost function. We use here a simulated annealing optimization tool, due to the difficulty of classical gradient based optimization techniques to handle the optimization of the cost function. The results show some of the drawbacks of using this model.

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Gu, Kai, Xin Zhang Jia, and Hai Long You. "A Study on t Chart for Short-Run and Mixed Model Productions." Advanced Materials Research 314-316 (August 2011): 2478–81. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.2478.

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At present a wide variety of mixed products with small batch sizes is one of the manufacturing modes in modern production. It is difficult to apply traditional Shewhart’s control charts efficiently and effectively in such environments. In this paper, we propose a t chart for monitoring a process which has the characteristics of not only being short run but also with mixed product. This new t chart is used to monitor the process mean for two illustrative examples and it is shown that this chart can work effectively in short run manufacturing for mixed product.

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KHOO, MICHAEL B. C., ZHANG WU, and ABDU M. A. ATTA. "A SYNTHETIC CONTROL CHART FOR MONITORING THE PROCESS MEAN OF SKEWED POPULATIONS BASED ON THE WEIGHTED VARIANCE METHOD." International Journal of Reliability, Quality and Safety Engineering 15, no. 03 (June 2008): 217–45. http://dx.doi.org/10.1142/s0218539308003052.

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A synthetic control chart for detecting shifts in the process mean integrates the Shewhart [Formula: see text] chart and the conforming run length chart. It is known to outperform the Shewhart [Formula: see text] chart for all magnitudes of shifts and is also superior to the exponentially weighted moving average chart and the joint [Formula: see text]-exponentially weighted moving average charts for shifts of greater than 0.8σ in the mean. A synthetic chart for the mean assumes that the underlying process follows a normal distribution. In many real situations, the normality assumption may not hold. This paper proposes a synthetic control chart to monitor the process mean of skewed populations. The proposed synthetic chart uses a method based on a weighted variance approach of setting up the control limits of the [Formula: see text] sub-chart for skewed populations when process parameters are known and unknown. For symmetric populations, however, the limits of the new [Formula: see text] sub-chart are equivalent to that of the existing [Formula: see text] sub-chart which assumes a normal underlying distribution. The proposed synthetic chart based on the weighted variance method is compared by Monte Carlo simulation with many existing control charts for skewed populations when the underlying populations are Weibull, lognormal, gamma and normal and it is generally shown to give the most favourable results in terms of false alarm and mean shift detection rates.

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Yang, Su Fen, and Barry C. Arnold. "Signal Detection for Process with Unknown Distribution." Advanced Materials Research 503-504 (April 2012): 1472–75. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.1472.

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Control charts are effective tools for monitoring both manufacturing processes and service processes. Much service data comes from a process with variables having non-normal or unknown distributions. The commonly used Shewhart variable control charts which depend heavily on the normality assumption should not be applied here. Hence, an alternative is desired to handle these types of process data. In this paper, we propose a new Variance Chart based on a simple statistic to monitor process variance shifts. The sampling properties of the new monitoring statistic are explored. A numerical example of service times from a bank service system with a right skewed distribution is used to illustrate the proposed Variance Chart. A comparison with two existing charts is also performed. The Variance Chart showed better ability than those two charts in detecting shifts in the process variance.

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Niezgoda, Janusz. "The Use of Statistical Process Control Tools for Analysing Financial Statements." Folia Oeconomica Stetinensia 17, no. 1 (June 27, 2017): 129–37. http://dx.doi.org/10.1515/foli-2017-0010.

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Abstract This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.

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Maged, Ahmed, Salah Haridy, Mohammad Shamsuzzaman, Imad Alsyouf, and Roubi Zaied. "Statistical Monitoring and Optimization of Electrochemical Machining using Shewhart Charts and Response Surface Methodology." International Journal of Engineering Materials and Manufacture 3, no. 2 (June 4, 2018): 68–77. http://dx.doi.org/10.26776/ijemm.03.02.2018.01.

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The response surface methodology (RSM) and Shewhart control charts have been widely used in manufacturing to reduce variation, improve quality and optimize the output. This article proposes an application of individuals & moving range chart (I&MR) and RSM in electrochemical machining. The Shewhart-type I&MR control chart and RSM are combined together in an effective way to successfully guarantee the statistical control of the surface roughness (Ra) of the items produced by wire electrochemical turning, and meanwhile optimize Ra by exploring the optimal values of the machining parameters including applied voltage, wire feed rate, wire diameter, rotational speed and overlap distance. The conducted experiments reveal that the optimal values of the aforementioned factors are 23.67, 0.5, 0.2, 900 and 0.02, respectively. A second-order regression model is also developed to predict the output (Ra) at different combinations of the input parameters. The developed regression model can predict the output values with a determination coefficient (R2) of 96.9%. The proposed combined scheme of Shewhart charts and RSM can be employed in other manufacturing processes and even in different service sectors to efficiently enhance the performance and reduce the cost.

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Aslam, Muhammad, G. Srinivasa Rao, Muhammad Saleem, Rehan Ahmad Khan Sherwani, and Chi-Hyuck Jun. "Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling." Computational and Mathematical Methods in Medicine 2021 (April 22, 2021): 1–17. http://dx.doi.org/10.1155/2021/6634887.

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More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

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Journal articles on the topic 'Shewhart's control charts'

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