Use Of Control Charts Research Articles

WE-AB-201-02: TPS Commissioning and QA: A Process Orientation and Application of Control Charts

WE-AB-201-02: TPS Commissioning and QA: A Process Orientation and Application of Control Charts

WE-AB-201-01: Treatment Planning System Commissioning and QA: Challenges and Opportunities

WE-AB-201-01: Treatment Planning System Commissioning and QA: Challenges and Opportunities

WE-AB-201-04: The Recommendations of MPPG #5 and Practical Implementation Strategies

WE-AB-201-04: The Recommendations of MPPG #5 and Practical Implementation Strategies

Monitoring the Process Mean When Standards Are Unknown: A Classic Problem Revisited

One of the most common applications in statistical process monitoring is the use of control charts to monitor a process mean. In practice, this is often performed with a Shewhart chart along with a Shewhart R (or an S) chart. Thus, two charts are typically used together, as a scheme, each using the 3-sigma limits. Moreover, the process mean and standard deviation are often unknown and need to be estimated before monitoring can begin. We show that there are three major issues with this monitoring scheme described in most textbooks. The first issue is not accounting for the effects of parameter estimation, which is known to degrade chart performance. The second issue is the implicit assumption that the charting statistics are both normally distributed and, accordingly, using the 3-sigma limits. The third issue is multiple charting, because two charts are used, in this scheme, at the same time. We illustrate the deleterious effects of these issues on the in-control properties of the charting scheme and present a method for finding the correct charting constants taking proper account of these issues. Tables of the new charting constants are provided for some commonly used nominal in-control average run length values and different sample sizes. This will aid in implementing the charting scheme correctly in practice. Examples are given along with a summary and some conclusions. Copyright © 2015 John Wiley & Sons, Ltd.

Kalite Kontrol Grafiklerinden “Shewart, Cusum ve Ewma” nın Bir Üretim İşletmesinde Uygulanması

After the industrial revolution in the 19th century, the concept „quality‟ was borned with the competition in the products and services industry. That producers and consumers from past to present want to use the resources at an optimal level has contributed so much to the quality development. Because of global competition, the concept „quality‟ has gained much importance for the businesses to exist in the product or service market. The aim of this study is to sample the use of control charts which are the quality tools for increasing the levels of products or services. In the conducted study, in order to determine the level of aimed quality level, the control charts for variables (Shewart), cumulative sum quality control charts (CUSUM) and exponentially weighted moving average control charts (EWMA) are used. The differences among them are presented by using different control charts. The control chart for the copper industry will be helpful for the enterprise applications which will be held and theoretical studies

Controlling Lead and Copper Rule Water Quality Parameters

The Lead and Copper Rule (LCR), enacted in 1991, was implemented to reduce exposure to lead and copper at consumers' taps. Water suppliers were required to initiate treatment that would reduce corrosion in the distribution system and thereby limit the release of lead and copper. The LCR required the supplier to initiate this treatment, called optimized corrosion control treatment (OCCT), and to monitor for certain water quality parameters (WQPs) to demonstrate that the OCCT was in place. The intent of this study was to determine how well WQPs can be controlled at actual water treatment plants and in distribution systems. In general, the data analyzed in this study demonstrated that utilities can successfully control these parameters within certain ranges, but variability does occur. The use of control charts for monitoring and responding to demonstrated variability of these WQPs is also presented. The value of control charts in observing trends and reacting to variations where appropriate is described.

The comet assay – how to recognise “good data”

Event Abstract Back to Event The comet assay – how to recognise “good data” William Barfield1* 1 Huntingdon Life Sciences, Genetic Toxicology, United Kingdom Testing of potentially genotoxic materials currently involves use of in vitro assays such as the Ames test (gene mutations), the chromosome aberration assay and in vitro micronucleus assay (predominantly using human peripheral lymphocytes to detect clastogenicity and/or aueuploidy) and the mouse lymphoma assay (gene mutations). In addition, one in vivo assay, predominately the in vivo micronucleus assay, is “normally” required to satisfy regulatory testing requirements and on occasion a second in vivo assay is required to assist in interpretation of results. Following release of the OECD 489 guideline “In vivo mammalian alkaline comet assay” in September 2014 the in vivo comet assay is now considered to be the second in vivo genetic toxicology assay of choice, effectively replacing the use of the UDS assay. The comet assay can be used to detect single strand and double strand breaks by measuring the median %tail intensity under alkaline conditions (pH>13). Following the JaCVAM validation trial, subsequent publication of the OECD 489 guideline recommended using the liver (site of metabolism) and glandular stomach (site of contact) of rats. However, any tissue can be examined if experimental competency with the tissue of interest has been proven. A number of key areas need careful consideration when performing the assay and interpreting the data. Both duration and temperature of tissue preparation have been shown to be critical parameters for obtaining “good data” along with the correct electrophoresis conditions to detect ‘weak’ effects. Study design requires careful thought in terms of animals per group, slides analysed per animal and cells analysed per slide and to facilitate this a special interest group within industry has published recommendations on statistical analysis of the comet assay. The sensitivity of the comet assay within the laboratory can be determined with the use of ‘power curves’ and identifying a high sensitivity could consequently allow a reduction in the use of animals. Furthermore, the OECD 489 guideline stipulates the use of control charts (c-charts or X-bar charts) to monitor the temporal drift of historical control data enabling the laboratory to ensure that the experimental methodology employed is under control. Keywords: Comet, JaCVAM, Tail Intensity, Control charts, Power curves Conference: ICAW 2015 - 11th International Comet Assay Workshop, Antwerpen, Belgium, 1 Sep - 4 Sep, 2015. Presentation Type: Oral Presentation Topic: The comet assay in Regulatory Toxicology Citation: Barfield W (2015). The comet assay – how to recognise “good data”. Front. Genet. Conference Abstract: ICAW 2015 - 11th International Comet Assay Workshop. doi: 10.3389/conf.fgene.2015.01.00054 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 May 2015; Published Online: 23 Jun 2015. * Correspondence: Mr. William Barfield, Huntingdon Life Sciences, Genetic Toxicology, Huntingdon, Cambridgeshire, PE28 4HS, United Kingdom, barfielw@ukorg.huntingdon.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers William Barfield Google William Barfield Google Scholar William Barfield PubMed William Barfield Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Fuzzy rules for fuzzy $overline{X}$ and $R$ control charts

Statistical process control ($SPC$), an internationally recognized technique for improving product quality and productivity, has been widely employed in various industries. $SPC$ relies on the use of control charts to monitor a manufacturing process for identifying causes of process variation and signaling the necessity of corrective action for the process. Fuzzy data exist ubiquitously in the modern manufacturing process, and in this paper, two alternative approaches to control charts are developed for monitoring sample averages and range. These approaches are based on fuzzy mode and fuzzy rules methods, when the measures are expressed by non-symmetric triangular numbers. In contrast to the existing control charts, the proposed approach does not require the use of the defuzzification and this prevents the loss of information included in samples. A numeric example illustrates the performance of the method and interprets the results.

Development of fuzzy U control chart for monitoring defects

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.

Cumulative Sum Charts and Its Healthcare Applications; A Systematic Review

Application of Statistical process control (SPC) is increasing being advocated in health science as a valuable quality improvement tool. The use of control charts in health science related applications differs somewhat from industrial. In SPC, cumulative sum (CUSUM) charts rather than Shewhart chart are used to detect smaller shifts in the process. In order to improve the detection capability relative to that of the Shewhart control charts, several control charts have been developed such as CUSUM charts, Exponentially weighted moving average(EWMA) charts and Sequential probability ratio test (SPRT) etc. Among them, the CUSUM charts are popular and effective. The CUSUM procedure has recently made its appearance in the field of medicine. The main purpose of this paper is to elucidate the common uses of CUSUM charts in healthcare and to describe different forms of CUSUM charts for monitoring performance over time when the outcome is related to health science. DOI: http://dx.doi.org/10.4038/sljastats.v15i1.6793

Multivariate Control Charts for Simultaneous Quality Monitoring of Isoniazid and Rifampicin in a Pharmaceutical Formulation Using a Portable Near Infrared Spectrometer

One of the keys to maintain high manufacturing quality improvement is the use of control charts. In this work, multivariate control charts based on significant principal component (PC) scores and net analytical signal (NAS) were developed to simultaneously monitor the quality of two active pharmaceutical ingredients (API) (isoniazid and rifampicin) in pharmaceutical formulation (laboratory samples and production samples) using a portable near infrared spectrometer. The limits for both multivariate charts were estimated using the quality specifications from the pharmaceutical formulation (± 5% of the nominal content of each API). The use of these multivariate control charts has provided a simple and powerful tool to evaluate the content of pharmaceutical formulations based on isoniazid and rifampicin capsules combined with near infrared spectroscopy. The procedure is rapid and adjustable for monitoring the production of the pharmaceutical preparations produced at UFRN/Brazil toward the process analytical technology (PAT) for the treatment of pulmonary tuberculosis.

Use of control charts for multi-temporal analysis of geodetic auscultation data from dams

Geodesic auscultation can be used to monitor the movement of dam structures by measuring the distance, at different epochs from fixed positions (pillars) to other positions (targets). It is important to identify the targets that present atypical measurements to permit managers to take corrective actions. After fitting a model using the Least Squares Method (LSM), the residuals normally display random behavior. Multivariate control charts are then applied to the residuals of the fitted model from data taken of geodesic survey campaigns conducted at different epochs. Control charts have been widely applied in other fields of research than production processes such as public health, marketing, services. The results show that it is possible for monitoring the multi-temporal stability by the multivariate control charts. The method provides complementary information than the classical univariate statistical analysis.

Application of Statistical Process Control for Structural Health Monitoring of a Historic Building

AbstractThe authors apply a statistical process control framework to support structural health monitoring of the Grace Church building in Charleston, South Carolina. Specifically, they conduct a post-hoc analysis of displacement data acquired via remote monitoring of delamination between two wythes of brick in a clerestory wall. The framework consists of formulation and estimation of statistical models to explain the progression of the measurements under ordinary conditions and use of control charts to detect unusual events. One such event was excessive displacement in September 2011 that led the engineer of record to close the building to public access and order immediate repairs. The analysis also reveals a few unusual events that were not apparent from visual interpretation of the data, including a possible precursor to the aforementioned event.

Use of Control Charts for Identifying Worsening Postoperative Mortality or Serious Morbidity Performance After Colectomy

Control charts are increasingly being used by hospitals as a tool for monitoring and improving health care quality. Our objective was to determine whether control charts of mortality or serious morbidity (M&M) rates after colectomy predict changes in outlier status for risk-adjusted M&M rates using data from a surgical registry, the American College of Surgeons National Surgical Quality Improvement Program. Control charts of monthly M&M rates for 95 hospitals were analyzed for indicators of a performance change in 2009 (vs 2008) using standard rules. Hospitals were also classified as having better, worse, or no change in outlier status for risk-adjusted M&M rates in 2009 (vs 2008). Agreement between these methods was fair (weighted κ = 0.379). There were no hospitals labeled as improving by one method and worsening by the other. Control charts predicted nonworsening performance well (specificity 0.866), but failed to identify 38.5% of hospitals with worsened outlier status. Although we did not demonstrate perfect agreement, our results suggest that these methods are measuring similar constructs of quality and are likely complementary uses of the same clinical data source.

Perdas de grãos e distribuição de palha na colheita mecanizada de soja

Considerando que a colheita é a etapa final de um processo produtivo e que as perdas devem ser mantidas dentro de padrões aceitáveis, as regulagens das colhedoras e as características agronômicas da cultura deverão permitir menores perdas quantitativas para que possa atingir o máximo nível de qualidade e maior sustentabilidade do sistema produtivo. Este trabalho teve como objetivo determinar as perdas quantitativas, distribuição de palha e características operacionais da colhedora por meio do controle estatístico de processo, bem como verificar a qualidade da operação de colheita. O trabalho foi realizado no município de Santa Juliana (MG), com as coordenadas geográficas de referência 19º 18'Se 47º 31'O. A área amostrada constou de 40 pontos distribuídos em malha retangular, equidistantes de 50 m, definidos sequencialmente a partir do momento de início da colheita. Em cada ponto foi determinado o teor de água dos grãos, número de vagens por planta, altura de inserção de primeira vagem, altura de plantas, produtividade, distribuição de palha e características operacionais da colhedora. A perda de grãos ocasionada pela colhedora esteve fora dos limites de controle, porém dentro dos padrões aceitáveis de perdas para cultura da soja. A qualidade da operação de colheita e as regulagens da colhedora foram dependentes da velocidade de deslocamento. A distribuição de palha esteve próxima do desejável para o sistema de semeadura direta. A utilização das cartas de controle foi eficiente na identificação dos pontos fora de controle e na avaliação da qualidade do processo de colheita.

Measuring the validity and reliability of ergonomic checklists

Ergonomic practitioners commonly use observational assessment tools, also known as checklists, to identify job hazards with regard to musculoskeletal disorders. However, it is often difficult to determine how effective such checklists are at identifying jobs in which workers are at risk, which complicates resource allocation. A means of dynamically assessing validity is needed. This paper focuses on a simple technique with which practitioners can assess the probability that a positive checklist indication accurately identifies an at-risk job. The technique can also be used to study the effect of changes to the checklist and determine whether or not they improve the practical utility of the checklist. Similarly, by manipulating the role of different risk factors assessed on the checklist, it may guide hypotheses as to the relative importance of the risk factors. Finally, the paper briefly suggests the use of control charts to assess and manage inter- and intra-rater reliability rather than more traditional assessment methods such as correlations, Cohen's and Fleiss' kappa. The probability that a checklist correctly identifies jobs with regard to risk of musculoskeletal injury is a useful means of assessing the checklist's validity.

A novel experience in the use of control charts for the detection of nosocomial infection outbreaks

OBJECTIVE:This study aims to compare different control charts to monitor the nosocomial infection rate per 1,000 patient-days.METHODS:The control charts considered in this study were the traditional Shewhart chart and a variation of this, the Cumulative Sum and Exponentially Weighted Moving Average charts.RESULTS:We evaluated 238 nosocomial infections that were registered in the intensive care unit and were detected by the Committee for Nosocomial Infection Control in a university hospital in Belo Horizonte, Brazil, in 2004 and 2005. The results showed that the traditional Shewhart chart was the most appropriate method for monitoring periods with large deviations, while the Exponentially Weighted Moving Average and Cumulative Sum charts were better for monitoring periods with smaller deviations of the mean infection rate.CONCLUSION:The ability to detect nosocomial outbreaks was improved by using the information provided by all three different control charts.

Practical Economic Statistical Design of ̅<i>X</i> Chart

This paper developed an understandable economic mathematical model of total cost of quality related to the use of control chart. This model considers both statistical quality and cost criteria. The statistical quality constraints considered in this paper are type I error (α) and type II error (β). The total cost of quality is a function of sample size (n) and sampling frequency (f). The model of total cost can be optimized to determine values of n and f that minimize the total cost of quality, while quality level is satisfied. The model can be solved by common spreadsheet program. The economic statistical model is developed under the real situation of a case study company to make the model realistic.

Expectation Analysis of the Probability of Failure for Water Supply Pipes

The probabilities of failure for cast iron (CI), ductile iron (DI), polyvinyl chloride (PVC), and concrete cylinder (CC) pipes were analyzed to determine the pipe type that failed most consistently at the Honolulu Board of Water Supply (HBWS). In understanding the process of the probability of failure over time, expected failure rates were calculated. A pipe type with a low and consistent probability of pipe failure was preferred for asset management over one that displayed a high and volatile probability of failure. The probability of failure was derived from the failure rate, assuming a Poisson distribution. The data for each pipe type was analyzed and compared by using control charts, operating characteristic (OC) curves, and process capability indexes. The results of the control charts showed that CC pipes were the most stable. According to the OC curves, in which the hypothesis created was that the probability of failure was less than the upper specification limit, CI pipes had the highest probability of accepting the hypothesis when true. PVC pipes showed a low probability of accepting the hypothesis within the specification limits, but also a low probability of false acceptance of the hypothesis. Finally, process capability analysis found that CI and PVC pipes were able to meet their desired failure specification limits. Overall results are mixed—CI pipes performed the best according to OC curves and process capability analysis; PVC pipes were seen as the worst through the use of control charts and OC curves; CC pipes were the most stable according to control charts, but the worst in process capability. A rank order structuring of failure expectations concluded that CI pipes were the best, whereas PVC pipes were the worst. However, this finding does not translate into recommendations for pipe design. It was observed that actual failure rates and expectation analysis are distinct aspects; the expected performance of a pipe is independent of its actual performance.

A process control method based on five-parameter generalized lambda distribution

The use of control charts in statistical quality control, which are statistical measures of quality limits, is based on several assumptions. For instance, the process output distribution is assumed to follow a specified probability distribution (normal for continuous measurements and binomial or Poisson for attribute data) and the process supposed to be for large production runs. These assumptions are not always fulfilled in practice. This paper focuses on the problem when the process monitored has an output which has unknown distribution, or/and when the production run is short. The five-parameter generalized lambda distributions (GLD) which are subject to estimating data distributions, as a very flexible family of statistical distributions is presented and proposed as the base of control parameters estimation. The proposed chart is of the Shewhart type and simple equations are proposed for calculating the lower and upper control limits (LCL and UCL) for unknown distribution type of data. When the underlying distribution cannot be modeled sufficiently accurately, the presented control chart comes into the picture. We develop a computationally efficient method for accurate calculations of the control limits. As the vital measure of performance of SPC methods, we compute ARL’s and compare them to show the explicit excellence of the proposed method.