A dynamic model to analyze the complexity associated with the manufacturing systems and to improve the performance of the process through the Six Sigma philosophy is proposed. The research focuses on the implementation of the system dynamics tool to comply with each of the phases of the DMAIC methodology. In the first phase, define, the problem is articulated, collecting data, selecting the variables, and representing them in a mental map that helps build the dynamic hypothesis. In the second phase, measure, model is formulated, equations are developed, and Forrester diagram is developed to carry out the simulation. In the third phase, analyze, the simulation results are studied. For the fourth phase, improving, the model is validated through a sensitivity analysis. Finally, in control phase, operation policies are proposed. This paper presents the development of a dynamic model of the system of knitted textile production knitted developed; the implementation was done in a textile company in southern Guanajuato. The results show an improvement in the process performance by increasing the level of sigma allowing the validation of the proposed approach.Keywords: Six Sigma, DMAIC, system dynamics, knitting. RESUMENSe propone un modelo dinámico para analizar la complejidad asociada a los sistemas de producción y mejorar el desempeño de los procesos a través de la filosofía Seis Sigma. La investigación se enfoca en la implementación de la herramienta de dinámica de sistemas para dar cumplimiento a cada una de las fases de la metodología DMAIC. En la primera fase, definir, se articula el problema, recolectando los datos, seleccionando las variables y representándolas en un mapa mental que ayuda a construir la hipótesis dinámica. En la segunda fase, medir, se formula el modelo, se desarrollan las ecuaciones y se elabora el diagrama de Forrester para llevar a cabo la simulación. En la tercer fase, analizar, se estudian los resultados obtenidos de la simulación. Para la cuarta fase, mejorar, se valida el modelo a través de un análisis de sensibilidad. Finalmente en la fase de control, se proponen políticas de operación. En este trabajo se desarrolló un modelo dinámico del sistema de producción textil de tejido de punto; la implementación se realizó en una empresa textil del sur de Guanajuato. Los resultados muestran un aumento en el desempeño del proceso incrementando el nivel de sigma, lo que valida el enfoque propuesto.Palabras clave: Seis sigma, DMAIC, dinámica de sistemas, tejido de punto.
Purpose – The purpose of this paper is to document the results of an implementation of the quality function deployment (QFD) tool in a process of a municipal administration involving: request for component support: family farming, urban and backyard agriculture that are part of the Rural Development Directorate of the Moroleón City Hall in the state of Guanajuato, México. Design/methodology/approach – The work is an exploratory study where the application of different quantitative techniques, database analysis, sample collection, field notes, participant observation, workshops and direct interviews were held and conducted to collect data. Findings – From the analysis of the requirements of quality characteristics are identified, simplified, response time reduced, assertiveness maintained and transparency in the resource allocations were assured. According to the calculations, customer expectations were clearly identified. Originality/value – The implementation of quality management methods such as QFD, in a municipal administration was used aimed at improving the services offered to citizens according to their expectations. It is expressed that mayors should be primarily concerned about giving the officials under their direction the necessary training to make their employment joyful with the help and use of the best tools available to provide quality results.
Multivariate control graphics detect signals out of control of the process. These signals are special patterns of joint variation, but they do not allow to determine what types of variation patterns take place in the individual variables. The referred problem has been treated through models of pattern recognition (PR) by Artificial Neural Networks (ANN). There are important advances in solving the problem to univariate cases, but not so in multivariate cases. There is no research which affirms that a single ANN can identify a multivariate out-of-control signal and recognize the special variation types of the variables individually. This research presents a model of PR of special variation in bivariate processes, and is based on an organized assembly of different types of ANN which are activated sequentially. With this work, it is possible to obtain a diagnosis of the bivariate process control that simultaneously recognizes the type of variation of the variables involved. This novel model provides the basis of new knowledge about statistical control of bivariate processes by PR through ANN. The model had two stages of training: experimental and industrial. The first one worked with data generated by Montecarlo simulation and the second one with data from a process that performs manufactured operations on metal bars used in the speed system in automobiles. Keywords: Statistical quality control, Pattern recognition, Artificial neural networks, Perceptron, Backpropagation
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