18 research outputs found

    Evaluation of the Sigma Quality level for Serum Iron determination by two colorimetric methods, Ferrozine and Ferene S

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    Iron plays important functions in the body such as the formation and functioning of hemoglobin and it’s disorders are among the most common diseases of human1. It is essential to ensure that its levels determination through laboratory tests are accurate and precise. The participation of laboratories in the External Quality Assessment (EQA) allows the increases of the quality level of the laboratory results and improvement of its performance.2This study was developed in the Portuguese Nacional EQA Program (PNAEQ) concerning the laboratories results from the Clinical Chemistry Scheme. The main objective of this study was to evaluate and improve the sigma quality level regarding the Iron parameter and reduce the variability of the laboratories results participating in the EQA program of Clinical Chemistry of the Nacional External Quality Assessment Program (PNAEQ). The mean Sigma quality level indicated that the Ferrozine method had a better performance compared with Ferene S method. Half of the control samples had a sigma quality level higher than 3.0, which is set as the minimum acceptable quality.3 Despite of the improved of the Sigma quality level in the Pilot Test, the results demonstrated a need to improved the analytical process performance and to identified more potential causes and implement new improvement actions. It becomes necessary to raise awareness with the laboratories, improving the Pilot Test participation frequency, resulting in a recurrent and current assessment of the laboratory activity performance. Developing Six Sigma projects on a periodic basis is important for continuously and progressively increasing the level of Sigma quality in laboratory examinations. The main advantage of quality assessment on the sigma scale is providing evidence of overall laboratory performance, taking into account random and systematic errors.N/

    Implementation of MEWMA Control Chart in Equipment Condition Monitoring

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    The progressive degradation of presently operating electro-mechanical systems is a certain future fact. To minimize losses, maintenance costs and eventual replacements, condition monitoring should be applied to critical equipment (Condition Based Maintenance – CBM). The state of equipment can be predicted at any moment using statistical methods to analyze condition monitoring data. In this paper, collected data are vibration values, obtained at p points (p = 4 for instance) of an experimental equipment, forming p variables. When independence condition does not hold, it is suggested modeling data with Auto-Regressive Integrated Moving Average (ARIMA) models, and using the residues of the estimated model for Phase I. In Phase I, the estimation of parameters is achieved using the Hotelling T control chart; only after applying the defined ARIMA model, the p variables are treated. In Phase II, equipment state is artificially degraded through induced failures and failure prediction obtained using special multivariate control charts for data statistical treatment. Assuming data independence and normality, Multivariate Exponentially Weighted Moving Average Modified (MEWMAM) control charts are applied in Phase II to data collected from an electric pump, controlling the behavior of data using this procedure. In Phase II, for non-independent data the prediction errors from the adjusted model are used instead of original data. To show that the suggested methodology can be applied to propulsion systems, simulated data from a gas turbine are used. Using these methodologies it is possible to run online condition monitoring, and act in time, to minimize maintenance costs and maximize equipment performance.info:eu-repo/semantics/publishedVersio

    Six Sigma methodology to evaluate and improve the results of three parameters of clinical chemistry evaluation quality assessment

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    From the PNAEQ’s Clinical Chemistry Program, three analytes were chosen to evaluate the quality assessment Total Cholesterol, LDL Cholesterol and Triglycerides Results from 2018 to 2020 were studied in order to evaluate and develop actions that allow the participants to reduce the variability of the results and improve their Sigma quality level in order to provide a better service to the patients.N/

    Application of the Six Sigma methodology in the evaluation of the results in Cell Blood Count EQAS Program (PNAEQ)

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    The haemogram is one of the most frequently requested laboratory tests, in hospital and ambulatory It is important in the evaluation of anaemia, polycythaemia, leukaemia, infection, inflammation, among others Therefore, given the importance of the haemogram in the clinical context, an evaluation was performed on the results of the clinical laboratories participating in PNAEQ’s EQA Cell Blood program.N/

    Opportunistic Maintenance Based on CUSUM Control Charts

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    The use of a Ship Maintenance Management System is fundamental for the good performance of equipments and the entire platform. Over the systematic maintenance, the opportunistic maintenance is a concept that aims to minimize outages and costs preventing undesirable failures. To implement this kind of maintenance statistical methodologies must be used. The Cumulative Sum charts have a very good performance applied to processes control in quality control. We proposed the use of Modified Cumulative Sum control charts to equipment maintenance.The data under study are observations of cooling water and oil temperatures from a diesel generator. In the first phase, we will apply traditional control charts, and, in the second phase, the Cumulative charts with a certain Average Run Length will be used. Then we will compare the results and extract conclusions, presenting measures for improvement.info:eu-repo/semantics/publishedVersio

    Control Charts Limits Flexibility Based on the Equipment Conditions

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    Condition Based Maintenance became an important development in industrial and transport equipment maintenance efforts. Many statistical methodologies have been applied in this area. These methodologies are usually applied off-line: after the data is collected. We propose an online, real-time condition monitoring system based on a modified control chart, applied to engine parameters. These charts should be flexible enough and its control limits should reflect the equipment state, the manufacturer specifications and onboard meteorological conditions. In this study we will develop a methodology to specify flexible chart control limits. The experimental equipment is a combined diesel or gas propulsion system. Two phases will be assumed. In phase 1 the equipment and historical data are analyzed, studying historical data, which leads to the definition of equipment parameters. In phase 2, new data is obtained by simulation, and the Exponentially Weighted Moving Average charts are applied considering flexible limits.info:eu-repo/semantics/publishedVersio

    SIX SIGMA BUSINESS SCORECARD APPROACH TO SUPPORT MAINTENANCE PROJECTS IN A COLLABORATIVE CONTEXT

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    According to the traditional perspective the maintenance function is counted as a secondary activity consuming human and financial resources, which is why many companies, use as maintenance strategy for several years, the subcontracting of maintenance services to specialized companies. However, the need to perform maintenance operations on increasingly complex systems requires access to several distinct types of skills that small and medium-sized firms specialized in maintenance services generally do not have. This paper aims to illustrate the role of the Six Sigma Scorecard approach as a management tool for improving quality in service provided in a collaborative context. The article begins by discussing the principles of the Six Sigma and the Balanced Scorecard backgrounds. It is then discussed how the two methodologies can be combined as a management tool of maintenance activities. Finally, it is discussed how this approach can be applied in the assessment of maintenance activities in the context of a collaborative ecosystem

    Implementation of statistical process control in a bottling line in winery industry

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    Introduction: The great demand of the markets has led to situations in which the production systems are characterized by the production of several batches but of small size. This new paradigm requires that adequate techniques be developed, both in terms of planning and in terms of Statistical Process Control (SPC), since there may be situations where it is not possible to collect enough data to properly estimate the parameters of the process (mean and variance). Objectives: Implementation of Statistical Process Control techniques in a wine industry in order to improve its final product. Methods: Whenever there is not enough data to properly estimate the parameters of the process, the suggested approach, is to adopt the developments proposed by Charles Quesenberry. In this case, the statistic of the sample at time i is transformed through the estimations of the process parameters using the information obtained (data) until the instant (i-1). The univariate study of process capability is performed through the capability indices QL and QU. Thus, in this paper, two situations of statistical control are addressed, one in which a univariate study is done, based on Q charts, and another in which the multivariate study is made, based on the MQ charts. Results: This study comprised the implementation of the SPC) of a wine brand that has low production volumes, in the process of bottling wine, which is considered a critical step since some care is needed, such as avoiding the occurrence of microbiological contaminations or oxidation of the wine. Conclusions: Whenever it is not possible to apply traditional control charts the use of the control Q charts (univariate analysis) and the control MQ charts (multivariate analysis) are the most appropriate choice not only for the control of one or more products but also for several sets of quality characteristics

    Implementação do controlo estatístico do processo numa linha de engarrafamento na indústria vinícola

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    Introduction: The great demand of the markets has led to situations in which the production systems are characterized by the production of several batches but of small size. This new paradigm requires that adequate techniques be developed, both in terms of planning and in terms of Statistical Process Control (SPC), since there may be situations where it is not possible to collect enough data to properly estimate the parameters of the process (mean and variance). Objectives: Implementation of Statistical Process Control techniques in a wine industry in order to improve its final product. Methods: Whenever there is not enough data to properly estimate the parameters of the process, the suggested approach, is to adopt the developments proposed by Charles Quesenberry. In this case, the statistic of the sample at time i is transformed through the estimations of the process parameters using the information obtained (data) until the instant (i-1). The univariate study of process capability is performed through the capability indices QL and QU. Thus, in this paper, two situations of statistical control are addressed, one in which a univariate study is done, based on Q charts, and another in which the multivariate study is made, based on the MQ charts. Results: This study comprised the implementation of the SPC) of a wine brand that has low production volumes, in the process of bottling wine, which is considered a critical step since some care is needed, such as avoiding the occurrence of microbiological contaminations or oxidation of the wine. Conclusions: Whenever it is not possible to apply traditional control charts the use of the control Q charts (univariate analysis) and the control MQ charts (multivariate analysis) are the most appropriate choice not only for the control of one or more products but also for several sets of quality characteristics.Introdução: A grande exigência dos mercados tem conduzido a situações em que os sistemas produtivos são caracterizados pela produção de diversos lotes mas de reduzida dimensão. Este novo paradigma exige que sejam desenvolvidas técnicas adequadas, tanto em termos de planeamento como em termos de Controlo Estatístico do Processo (SPC), uma vez que podem existir situações em que não é possível proceder à recolha de dados suficientes para se estimar convenientemente os parâmetros do processo (média e variância). Objectivos: Implementação do Controlo Estatístico do Processo na indústria vitivinícola para melhorar a qualidade do produto final. Métodos: Quando nos confrontamos com situações em que não é possível proceder à recolha suficiente de dados, a abordagem sugerida consiste em adotar os desenvolvimentos propostos por Charles Quesenberry. Nestes casos, a estatística da amostra no instante i é transformada através das estimativas dos parâmetros do processo recorrendo à informação obtida (dados) até ao instante (i-1). O estudo univariado da capacidade do processo é realizada através dos índices de capacidade QL e QU. São abordadas duas situações de controlo estatístico, uma em que é feito um estudo univariado, com base em cartas Q, e outra em que é feito o estudo multivariado, com base nas cartas MQ. Resultados: Este estudo refere-se à implementação do SPC , de uma marca de vinho que possui baixos volumes de produção, no seu processo de engarrafamento, que é considerado que é considerado uma etapa critica uma vez que é necessário ter alguns cuidados como por exemplo o evitar a ocorrência de contaminações microbiológicas ou a oxidação do vinho. Conclusões: Sempre que não seja possível aplicar cartas de controle tradicionais, a utilização das cartas de controlo Q (análise univariada) e as cartas de controlo MQ (análise multivariada) revela-se a escolha mais adequada, não só para o controlo de um ou mais produto como para vários conjuntos de características da qualidade.Introducción: La gran exigencia de los mercados ha conducido a situaciones en que los sistemas productivos se caracterizan por la producción de diversos lotes de reducida dimensión. Este nuevo paradigma exige que se desarrollen técnicas adecuadas tanto en términos de planificación como de control estadístico del proceso (SPC), ya que pueden existir situaciones en las que no es posible proceder a la recogida de datos suficientes para estimar convenientemente los parámetros del proceso (media y varianza). Objetivos: Aplicación del control estadístico del proceso en la industria vitivinícola para mejorar la calidad del producto final. Métodos: Cuando nos enfrentamos con situaciones en que no es posible proceder a la recogida suficiente de datos, el enfoque sugerido consiste en adoptar los desarrollos propuestos por Charles Quesenberry. En estos casos, la estadística de la muestra en el instante i se transforma a través de las estimaciones de los parámetros del proceso recurriendo a la información obtenida (datos) hasta el instante (i-1). El estudio univariado de la capacidad del proceso se realiza a través de los índices de capacidad QL y QU. Se abordan dos situaciones de control estadístico, una en que se realiza un estudio univariado, basado en cartas Q, y otra en que se realiza el estudio multivariado, sobre la base de las cartas MQ. Resultados: Este estudio se refiere a la implementación del SPC, de una marca de vino que tiene bajos volúmenes de producción, en su proceso de embotellado, que se considera que se considera una etapa crítica, ya que es necesario tener algunos cuidados como por ejemplo evitar que se produzcan contaminaciones microbiológicas o la oxidación del vino. Conclusiones: Siempre que no sea posible aplicar cartas de control tradicionales, la utilización de las cartas de control Q (análisis univariado) y las cartas de control MQ (análisis multivariado) se revela la elección más adecuada, no sólo para el control de uno o más producto como para varios conjuntos de características de calidad

    Programa de AEQ de Contagem Celular: sessão de brainstorming

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    1. Técnicas e ferramentas utilizadas na fase Analyze do ciclo DMAIC (metodologia seis sigma) 2. Identificação de possíveis causas que contribuem para a variabilidade dos resultados laboratoriais no programa de AEQ de contagem celular 3. Sugestões de melhorias (fase pré-analítica e pós-analítica) 4. Identificação de ações de melhoria (fase pré-analítica e pós-analítica).N/
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