Search

This article proposes the variable sampling interval (VSI) exponentially weighted moving average (EWMA) t chart for the process mean using auxiliary information (AI) (denoted as the VSI EWMA t-AI chart). The proposed chart allows different sampling intervals to be adopted based on prior process quality. The average time to signal (ATS) and expected average time to signal (EATS) criteria are used to evaluate the performance of the proposed chart. Numerical analyses are conducted and the results show that the proposed VSI EWMA t-AI chart surpasses the EWMA t-AI chart in the detection of all sizes of shifts in the process mean when there are no changes (or estimation error) in the process standard deviation. Additionally, the proposed VSI EWMA t-AI chart is found to be more robust than the VSI EWMA X ¯ -AI chart when there are changes in the process standard deviation.

PurposeAs an alternative to the standard p and np charts along with their various modifications, beta control charts are used in the literature for monitoring proportion data. These charts in general use average of proportions to set up the control limits assuming in-control parameters known. The purpose of the paper is to propose a control chart for detecting shift(s) in the percentiles of a beta distributed process monitoring scheme when in-control parameters are unknown. Such situations arise when specific percentile of proportion of conforming or non-conforming units is the quality parameter of interest.Design/methodology/approachParametric bootstrap method is used to develop the control chart for monitoring percentiles of a beta distributed process when in-control parameters are unknown. Extensive Monte Carlo simulations are conducted for various combinations of percentiles, false-alarm rates and sample sizes to evaluate the in-control performance of the proposed bootstrap control charts in terms of average run lengths (ARL). The out-of-control behavior and performance of the proposed bootstrap percentile chart is thoroughly investigated for several choices of shifts in the parameters of beta distribution. The proposed chart is finally applied to two skewed data sets for illustration.FindingsThe simulated values of in-control ARL are found to be closer to the theoretical results implying that the proposed chart for percentiles performs well with both positively and negatively skewed data. Also, the out-of-control ARL values for the percentiles decrease sharply with both downward and upward small, medium and large shifts in the parameters. The phenomenon indicates that the chart is effective in detecting shifts in the parameters. However, the speed of detection of shifts varies depending on the type of shift, the parameters and the percentile being considered. The proposed chart is found to be effective in comparison to the Shewhart-type chart and bootstrap-based unit gamma chart.Originality/valueIt is worthwhile to mention that the beta control charts proposed in the literature use average of proportion to set up the control limits. However, in practice, specific percentile of proportion of conforming or non-conforming items should be more useful as the quality parameter of interest than average. To the best of our knowledge, no research addresses beta control chart for percentiles of proportion in the literature. Moreover, the proposed control chart assumes in-control parameters to be unknown, and hence captures additional variability introduced into the monitoring scheme through parameter estimation. In this sense, the proposed chart is original and unique.

This paper surveys the location Y¯ chart based on concomitants of ranked set sampling (RSS), a triangular modification of RSS, which is known as maximum ranked set sampling with unequal sample (MRS...

Made available in DSpace on 2022-04-29T08:33:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 The Cpk is the distance between the process mean and the closest specification limit divided by three times the process standard deviation. The index Cpk was created to quantify the ability of the process to produce products within specified limits, but in recent studies the sample estimator (Formula presented.) of the index Cpk has also been used to control processes and to decide whether a lot should be accepted or not. As a monitoring statistic, the (Formula presented.) has the aim to signal changes in the mean and/or in the variance of the X distribution. It is worth to note that in this new use, the specification limits can no longer be seen as the limits beyond which a product is considered defective; they are now tunning parameters of the control chart, that is, the choice of the specification limits affects the speed with which the Cpk chart signals. In this article, we study the (Formula presented.) distribution. Universidade Estadual Paulista Julio de Mesquita Filho Campus de Guaratingueta Production Universidade Estadual Paulista Julio de Mesquita Filho Campus de Guaratingueta Production

An economic-statistical design is developed for the variable sampling interval (VSI) Poisson exponentially weighted moving average chart, namely the VSI PE chart, where the cost function is minimiz...

In this article we propose a control chart to monitor correlated Poisson variables, which uses a statistic based on the linear combination of Poisson variables considering the methodology of sampling called Multiple Dependent State. In order to analyse the performance of this chart, a friendly software has been developed, which finds the best parameters to optimise the out-of-control average run length (ARL) for a shift that the practitioner wishes to detect as quickly as possible, restricted to a fixed value of in-control ARL. Additionally, some scenarios have been considered in the comparison of performance and a sensitivity analysis was carried out. The results show that the MDSLCP chart has better performance than the LCP chart.

The Adaptive Exponentially Weighted Moving Average (AEWMA) control chart is known to be effective in detecting range of shifts simultaneously. Moreover, the AEWMA chart is known to diminish the ine...

Investigating the effect of measurement errors on the control chart monitoring the ratio of two normal random variables is an important task to facilitate the use of this kind of control chart in practice. Moreover, a deep insight into the problem can help practitioners to find a way to reduce unexpected impacts of measurement errors on the chart performance. This paper provides a study on the performance of the exponentially weighted moving average control chart monitoring the ratio in the presence of measurement errors. We extend the linear covariate error model applied in previous studies to a more general situation, which makes the study more realistic. The numerical results show that although the precision error and the accuracy error have negative influences on the proposed chart performance when these errors are not large these influences are not significant.

XYZ is a company that produces Medical Devices. One of the products produced is 16x16 hydrophilic sterile gauze. One of the factors that affect the quality of a product is the presence of defects in the product and waste which results in a longer lead time for a product. The investigative stages used in this study are using tools: seven tools, Big Picture Mapping, Value Stream Analysis (VALSAT) and then analyzing the causes of defects using the Ishikawa diagram (fishbone chart) where the output of this study is a proposed improvement using the FMEA method ( Failure Mode Effect and Analysis) to eliminate defects and waste that occur. Based on the research results, it is known that the type of waste identified by the calculation of Value Stream Activity and obtained Value Added with a percentage of 84.4%, Non Value Added by 1.0% and Necessary Non Value Added by 14.5%. From the results of production output in December 2019 to November 2020 amounting to 2,650,200 boxes, the number of defects is 1,316 boxes with an average sigma level of 5.16, while in FMEA it is known that the largest defects are spots with an RPN value of 504 and a suggestion for improvement is to place Experienced operators who have gone through the recruitment and training stages on superior qualifications on one machine according to their skills who understand SOPs and operators are given good comfort in the production process. Keywords: Lean Six Sigma, Seven Waste, DMAIC, FMEA, kasa steril hidrofil 16x16.

A setback of the X¯ type charts is that they signal out-of-control when the process standard deviation is not stable, i.e., they are not robust against estimation errors or changes in the process s...

Background The influenza surveillance has been received much attention in public health area. For the cases with excessive zeroes, the zero-inflated Poisson process is widely used. However, the traditional control charts based on zero-inflated Poisson model, ignore the association between influenza cases and risk factors, and thus may lead to unexpected mistakes when implementing monitoring charts. Method In this paper, we proposed risk-adjusted zero-inflated Poisson cumulative sum control charts, in which the risk factors were put to adjust the risk of influenza and the adjustment was made by zero-inflated Poisson regression. We respectively proposed the control chart monitoring the parameters individually and simultaneously. Results The performance of our proposed risk-adjusted zero-inflated Poisson cumulative sum control chart was evaluated and compared with the unadjusted standard cumulative sum control charts in simulation studies. The results show that for different distribution of impact factors and different coefficients, the risk-adjusted cumulative sum charts can generate much less false alarm than the standard ones. Finally, the influenza surveillance data from Hong Kong is used to illustrate the application of the proposed chart. Conclusions Our results suggest that the adjusted cumulative sum control chart we proposed is more accurate and credible than the unadjusted standard control charts because of the lower false alarm rate of the adjusted ones. Even the unadjusted control charts may signal a little faster than the adjusted ones, the alarm they raise may have low credibility since they also raise alarm frequently even the processes are in control. Thus we suggest using the risk-adjusted cumulative sum control charts to monitor the influenza surveillance data to alert accurately, credibly and relatively quickly.

The control chart is the most important tool in the statistical process control to monitor the industrial process. The additional supporting information related to the underlying quality characteri...

In this article, we first propose an exponentially weighted moving average (EWMA) chart for monitoring both the shape and scale parameters of the Weibull distribution. The proposed chart was develo...

BackgroundWrong patient selection errors may be tracked by retract–reorder (RAR) events. The aim of this quality improvement study was to assess the impact of reducing the number of concurrently open electronic health records from 4 to 2 on RAR errors generated by a tele-critical care service.MethodsThe study encompassed 32 months before and 21 months after restriction. Chi-Square test of proportions and T statistical process control chart for rare events were used.ResultsThere were 156 318 orders with 57 RAR errors (36.5/100 000 orders) before restriction, and 122 587 orders with 34 errors (27.7/100 000 orders) after. Rates were not statistically different (P = .20), but analysis was underpowered. When plotted on a T control chart, random variation was detected between RAR errors.ConclusionWe found no significant difference in RAR errors in the tele-critical care setting after open record limitation. Other strategies should be studied to reduce wrong patient selection errors.

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.

A control chart has become a choice of quality practitioners for monitoring the output of industrial and production processes. It is a common practice to develop control charts under normality assu...

When monitoring industrial processes, a Statistical Process Control tool, such as a multivariate Hotelling T 2 chart is frequently used to evaluate multiple quality characteristics. However, research into the use of T 2 charts for survey fieldwork–essentially a production process in which data sets collected by means of interviews are produced–has been scant to date. In this study, using data from the eighth round of the European Social Survey in Belgium, we present a procedure for simultaneously monitoring six response quality indicators and identifying outliers: interviews with anomalous results. The procedure integrates Kernel Density Estimation (KDE) with a T 2 chart, so that historical “in-control” data or reference to the assumption of a parametric distribution of the indicators is not required. In total, 75 outliers (4.25%) are iteratively removed, resulting in an in-control data set containing 1,691 interviews. The outliers are mainly characterized by having longer sequences of identical answers, a greater number of extreme answers, and against expectation, a lower item nonresponse rate. The procedure is validated by means of ten-fold cross-validation and comparison with the minimum covariance determinant algorithm as the criterion. By providing a method of obtaining in-control data, the present findings go some way toward a way to monitor response quality, identify problems, and provide rapid feedbacks during survey fieldwork.

An industrial company requires quality control to maintain quality consistency from the production results so that it is able to compete with other companies in the world market. In the industrial sector, most processes are influenced by more than one quality characteristic. One tool that can be used to control more than one quality characteristic is the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. The graph is used to determine whether the process has been controlled or not, if the process is not yet controlled, the next analysis that can be used is to use the Average Run Length (ARL) with the Markov Chain approach. The markov chain is the chance of today's event is only influenced by yesterday's incident, in this case the chance of the incident in question is the incident in getting a sampel of data on the production process of batik cloth to get a product that is in accordance with the company standards. ARL is the average number of sample points drawn before a point indicates an uncontrollable state. In this study, 60 sample data were used which consisted of three quality characteristics, namely the length of the cloth, the width of the cloth, and the time of the fabric for the production of written batik in Batik Semarang 16 Meteseh. Based on the results and discussion that has been done, the MEWMA controller chart uses the λ weighting which is determined using trial and error. MEWMA control chart can not be said to be stable and controlled with λ = 0.6, after calculating, the value is obtained Upper Control Limit (BKA) of 11.3864 and Lower Control Limit (BKB) of 0. It is known that from 60 data samples there is a T j 2 value that comes out from the upper control limit (BKA) where the amount of 15.70871, which indicates the production process is not controlled statistically. Improvements to the MEWMA controller chart can be done based on the ARL with the Markov Chain approach. In this final project, the ARL value used is 200, the magnitude of the process shift is 1.7 and the r value is 0.28, where the value of r is a constant obtained on the r parameter graph. The optimal MEWMA control chart based on ARL with the Markov Chain approach can be said to be stable and controlled if there is no T j 2 value that is outside the upper control limit (BKA). The results of the MEWMA control chart based on the ARL with the Markov Chain approach show that the process is not statistically capable because the MC pm value is 0.516797 and the MC pmk value is 0.437807, the value indicates a process capability index value of less than 1. Keywords : Handmade batik, Multivariate Exponentially Weighted Moving Average (MEWMA), Average Run Length (ARL), Capability proc e ss .

Tujuan penelitian ini yaitu untuk mengambil keputusan saat yang tepat untuk jual dan beli saham pada IDX 30 dengan menggunakan analisis teknikal atara lain MACD, moving average, dan stochastic oscillator. Penelitian ini adalah penelitian kualitatif deskriptif. Populasi dalam penelitian ini adalah seluruh saham yang terdaftar dalam IDX 30 selama periode 2017-2019 berjumlah 30 perusahaan. Penarikan sampel dilakukan dengan teknik purposive sampling. Lokasi penelitian pada saham yang terdaftar di IDX30 dengan menggunakan software Chart AmiBroket. Analisis data menggunakan analisis teknikal dengan menggunakan tiga indikator yaitu MACD, moving average, dan stochastic oscillator. Berdasarkan hasil penelitian dari ketiga indikator tersebut, MACD menghasilkan profit yang paling besar. Momen yang tepat dalam menggunakan MACD adalah bila garis MACD memotong garis sinyal dari bawah ke atas atau Golden Cross sebagai adanya bullish trend. Kondisi yang memberi sinyal jual bila garis MACD memotong garis sinyal dari atas ke bawah atau Death Cross sebagai adanya bearish trend.

The Shewhart median (denoted as X˜) chart can be used in place of the Shewhart X¯ chart for detecting mean shifts when outliers are present because the sample mean is easily affected by outliers. H...

Recent studies have shown that an adaptive T 2 chart with double sampling and variable sampling interval D S V S I T 2 chart shows a good performance in detecting small to moderate shifts in the me...

Process capability index PCIs have long been used to assess process efficiency in the manufacturing. While PCIs assess the process stability and efficiency, real measurements of product quality depended on estimate the standard deviation for a short-term variance was calculated as the base approach in several previous studies for measuring PCIs. Studies in recent years seek to estimate shift of the process mean from target. In today’s smart manufacturing production climate, manufacturers keep track of production performance. Since the deviance of process mean comparative to the target is different. This allows a more robust norm to be implemented by estimate the relative shift to describe the variance of process in Six Sigma ( SS ). For that purpose, this paper suggests an evaluation method by comparative analysis to four different ways of calculating yield indexes $S_{pk}$ based on six sigma and control information, of $\bar {X}_{S}$ chart. Moreover, this paper extends the univariate process index yield $SSS_{pk}$ to a multi- generalized yield index is called $SSMS_{pk}$ . An established an updated approach to assess univariate and multi-characteristic, based on understanding variation which is critical to quality (CTQ) and focusing on the position and actions of natural process tolerance between defined tolerances limits. To demonstrate the trueness verification proficiency of this approach, this paper includes an industrial case study to assess the process efficiency in Aden’s oil refinery, Yemen. The findings of this study indicated that the indexes which are calculating based on six sigma outperformed existing indices and that this study has important implications for industrial practitioners, researchers and quality control experts interested in the evaluation of process performance.

Objective The establishment of ongoing audits for first-trimester nuchal translucency (NT) measurements is of paramount importance. The exponentially weighted moving average (EWMA) chart has been published as an efficient tool for NT quality control with the advantages of being suitable for real-time long-term monitoring. This study aimed to assess the efficacy of real-time NT quality control using EWMA charts. Materials and methods This was an ongoing prospective study conducted from January 2011 to December 2017 at the Centre for Fetal Medicine Gennet in Prague. The quality of NT measurements was assessed using the NT retrospective distribution parameters and EWMA charts, and the results were presented to the sonographers during collective meetings. Results Overall, 28,928 NT measurements obtained from six sonographers were eligible for the study. Looking at individual EWMA charts, we observed four main outcomes. First, there was a clear improvement in the performance of sonographers with initially poor performances. Second, the performance of sonographers with an initially satisfactory quality was maintained. Third, there was an observed deterioration of the performance without the audits. Last, the sonographers appreciated an unequivocal and straightforward graphical presentation of EWMA curves. Conclusion EWMA proved to be an efficient and suitable tool for real-time monitoring of NT quality and led to an overall improvement of the sonographers’ performance.

PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

Purpose This paper presents economic and economic–statistical designs of the adaptive exponentially weighted moving average (AEWMA) control chart for monitoring the process mean. It also aims to compare the effect of estimated process parameters on the economic performance of three charts, which are Shewhart, exponentially weighted moving average and AEWMA control charts with economic–statistical design. Design/methodology/approach The optimal parameters of the control charts are obtained by applying the Lorenzen and Vance’s (1986) cost function. Comparisons between the economic–statistical and economic designs of the AEWMA control chart in terms of expected cost and statistical measures are performed. Also, comparisons are made between the economic performance of the three competing charts in terms of the average expected cost and standard deviation of expected cost. Findings This paper concludes that taking into account the economic factors and statistical properties in designing the AEWMA control chart leads to a slight increase in cost but in return the improvement in the statistical performance is substantial. In addition, under the estimated parameters case, the comparisons reveal that from the economic point of view the AEWMA chart is the most efficient chart when detecting shifts of different sizes. Originality/value The importance of the study stems from designing the AEWMA chart from both economic and statistical points of view because it has not been tackled before. In addition, this paper contributes to the literature by studying the effect of the estimated parameters on the performance of control charts with economic–statistical design.

Statistical Process Control (SPC) is usually applied to the production process of goods, with the aim of detecting the quality of a production item that is within or beyond the specified specifications. In this study, SPC was applied to the bearing vibration signal to detect the first observable defect on a machine that functions as part of a prognostic tool for maintenance decision making. The detection of damage and prognostic are two important aspects in machine maintenance based on current conditions or better known as Condition (data) Based Maintenance (CBM). This paper discusses the shewhart average level chart and adaptive shewhart average level chart to detect the first observable defect. The shewhart chart is built with two assumptions, i.e. that the data must vary randomly around an established mean and follows a normal distribution. However, the adaptive Shewhart chart there is no need for normal assumption. The exploration of our data shows that the assumption of normality is not fulfilled, so that the Shewhart average level chart is not implemented. The adaptive Shewhart chart shows that the warning line for bearing 1 amounted to 5.547 and 3.631, for bearing 2 amounted to 5.491 and 3.635, for bearing 3 amounted to 5.762 and 3, 573, for bearing 4 of 5.604 and 33.615. The action line for bearing 1 is 6.026 and 3.152, for bearing 2 is 5.955 and 3.171, for bearing 3 is 6.309 and 3.026, for bearing 4 is 6.101 and 3.118. The first observable defect was t = 81 for bearing 1, t = 146 for bearing 2, t = 40 for bearing 3 and t = 61 for bearing 4. The adaptive Shewart chart can be used as a toll to estimate the initiation of transition state from normal to degenerate.

This article proposes a two-sided run sum ratio chart for monitoring the ratio of two normal variables. A Markov chain procedure is applied to evaluate the statistical performance of the chart by u...

We develop a new proportion control chart using the exponentially weighted moving average (EWMA) for the maxima nomination sampling (MNS) method. By using average run length (ARL), we compare it wi...

Control chart is one of the effective statistical tools to overcome the problem of process quality in a production. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is an effective quality control tool in processes with more than one variable and correlated (multivariate). The MEWMA control chart has a weight value (λ) which makes this chart more sensitive in detecting small shifts process mean. The weight (λ) has values ranging from 0 to 1 ( ), where this weight will be given to each data. The MEWMA control chart in this study was used to form a control chart by the product defects percentage of grade B and grade B at PT. Pismatex Textile Industry Pekalongan. In this study, GUI Matlab was formed to assist the computational process in forming MEWMA control charts to control the quality of production at PT. Pismatex Textile Industry Pekalongan. Based on the result, the optimal weight is obtained at the weight value λ = 0.9. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA), Weight (λ), GUI Matlab, Percentage of product defects.

The EWMA-t chart is used to monitor the process mean as it is robust against errors in estimating the process standard deviation or changing standard deviation. In this paper, we propose a double E...

A profile is a relationship between the response variable(s) and the independent variable(s), which describes the quality of a process or product. The profile can be monitored by a process control ...

In the literature, the variable sample size and sampling interval (VSSI) coefficient of variation (CV) chart was shown to have a better statistical performance than the Shewhart (SH) CV chart which...

The aims of this research have investigated the robustness of Cumulative Sum – Tukey’s control chart (CUSUM-Tukey) to detecting process dispersion when the assumption of process distribution has deviated from the normal distribution. The performance of control charts is commonly measured by an out of control Average Run Length (ARL1) by given in control Average Run Length (ARL0) = 370 and 500. The performance of CUSUM-Tukey is compared with Tukey’s control chart (Tukey) and Cumulative Sum control chart (CUSUM) where the best performance of the control chart will give the minimum value of ARL1. The numerical results are figured out by the Monte Carlo simulation method to approximate ARL which found that the CUSUM-Tukey chart is superior to Tukey and CUSUM control charts for all case studies of distributions and several spans for calculating moving range.

The implementation of the X¯ chart requires a large number of samples from an underlying process model, which poses a major problem in short production runs, like in the fast-paced smart manufactur...

The t chart is commonly used as an alternative to the Shewhart mean chart when the process standard deviation is not known in advance. In this paper, we develop variable sampling interval (VSI) bas...

Background: Quality control is an essential part of quality management in medical laboratory. Various methods are available for the analysis of laboratory quality control data, common ones are Levey Jennings chart and Westgard rules. Westgard multi-rules are a set of rules based on combination of criteria to decide whether an analytical run is acceptable or unacceptable. CUSUM uses the cumulative sum of deviations from a target, however, it is rarely used in current medical laboratory practice. We share our experience of using Westgard rules and CUSUM in the analysis of laboratory quality control data. Material and methods: Internal quality control values of 2-year period was included in the present study. Data for platelet count values of normal level control material run on a fully automated haematology analyser (Sysmex XT-2000i) were analysed. A total of 1825 data points were obtained. The data was interpreted by Westgard rules as well as cumulative sum method. The out of control events was analysed. Results: There was 9 incidence of control value outside 3SD (Westgard Rule 13s) which was picked on Levey Jennings chart but missed by CUSUM method. There were 22 instances of shift in mean (bias) which were only picked by CUSUM method. Conclusion: CUSUM was more sensitive for detection of bias whereas random error was picked-up early by Westgard rules. In conclusion we recommend the use of more than one method for analysis of quality control data.

Measurement error always exists in quality control applications and may considerably affect the ability of control charts to detect an out-of-control situation. In this paper, we study the performance of the EWMA median chart using a Markov Chain approach with a linear covariate error model and a corrected formula for the distribution of the sample median. The performance is evaluated through the Average Run Length. Several figures and tables are presented and enumerated to show the statistical performance of the EWMA median control chart for different sources of measurement errors. The positive effect of taking multiple measurements for each item in a subgroup on the performance of the EWMA median chart is also investigated. An example illustrates the use of this study is introduced. The numerical simulation shows that the measurement errors have a negative influence on the proposed chart.

In recent years, control charts monitoring the coefficient of variation (CV), denoted as the ratio of the variance to the mean, is attracting significant attention due to its ability to monitor processes in which the process mean and process variance are not independent of each other. However, very few studies have been done on charts to monitor downward process shifts, which is important since downward process shifts show process improvement. In view of the importance of today’s competitive manufacturing environment, this paper proposes a one-sided chart to monitor the downward multivariate CV (MCV) with variable sample size and sampling interval (VSSI), i.e. the VSSI D MCV chart. This paper monitors the MCV as most industrial processes simultaneously monitor at least two or more quality characteristics, while the VSSI feature is incorporated, as it is shown that this feature brings about a significant improvement of the chart. A Markov chain approach was adopted for designing a performance measure of the proposed chart. The numerical comparison revealed that the proposed chart outperformed existing MCV charts. The implementation of the VSSI D MCV chart is illustrated with an example.

In this study, a robust control chart as an alternative to the Tukey's control chart (TCC) based on the modified trimmed standard deviation (MTSD), namely MTSD-TCC, is proposed. The performance of the proposed and the competing Tukey's control chart (TCC) is measured using different length properties such as average run length (ARL), standard deviation of run length (SDRL), and median run length (MDRL). Also, the study covered normal and contaminated cases. We have observed that the proposed robust control chart (MTSD-TCC) is quite efficient at detecting process shifts. Also, it is evident from the simulation results that the proposed robust control chart (MTSD-TCC) offers superior detection ability for different trimming levels as compared to the Tukey's control chart (TCC) under the contaminated process setups. As a result, it is recommended to use the proposed robust control chart (MTSD-TCC) for process monitoring. An application numerical example using real-life data is provided to illustrate the implementation of the proposed robust control chart (MTSD-TCC) which also supported the results of the simulation study to some extent.

Background: Cancer is one of the most devastating diseases that influence humanity in the modern era. The effect is not confined to morbidity and mortality, but it is also extended to social and economic consequences despite the advancements made in the medical and healthcare fields. The present case overview will focus on the overall long-term assessment of death rates from malignancy from 1960 to 2017 as part of a global study of the mortality trend of this disease. Methods: Statistical analysis and process control methodologies were used to study the death rate trend over this recorded period using statistical programs platform. A combination of techniques that could be used sequentially after database processing and stratification were used including distribution fitting, descriptive statistics, data fitting mathematical pattern, Gaussian Mixture Model (GMM) box plot and Individual-Moving Range (I-MR) trending chart. Results: Two-parameter Weibull distribution (or Weibull 2) was the most appropriate that has fitted the distribution pattern of data and used for the construction of the process-behavior chart. The last two decades of the dataset showed a progressive decline in the mortality rates, which were almost linear with a higher magnitude of a negative slope than that of the initial rising pattern from 1960 until the 1990s. The hump-shaped trend showed underlying two distribution clusters: An initial distribution (I) of 81% of data of higher average mortality rates and minor one (II) that covers the last decade of monitoring record. Discussion: A significant improvement in cancer healthcare was witnessed with a noticeable breakthrough in the last decade in the USA.

This study improves the performance of R control chart for monitoring process dispersion of skewed populations using scaled weighted variance method. This control chart, called Scaled Weighted Variance R control chart (SWV-R) hereafter, the SWV-R control chart compared with Skewness Correction R chart (SC-R) and Weighted Variance R chart (WV-R) in terms of false alarm. In terms of probability of detection rates the proposed SWV-R chart is compared with R chart of the exact method, SC-R and WV-R control charts. The proposed SWV-R control chart reduces to the Shewhart R control chart when the underlying distribution is symmetric. An illustrative example is given to show how the proposed SWV-R control chart is constructed and works simulations study show that the proposed SWV-R control chart has the lower false alarm rates than the SC-R and WV-R control charts, when the underlying distributions are Weibull and gamma. In terms of the probability of detection rates, the proposed SWV-R control chart is closer to R control chart with the exact method than WV-R and almost the same performance as SC-R chart.