Failure mode and effect analysis (FMEA) is one of the most used techniques in risk management due to its potential to solve multidisciplinary engineering problems. The role of experts is fundamental when developing the FMEA; they identify the failure modes by expressing their opinion based on their experience. A relevant aspect is a way in which the experts evaluate to obtain the indicator of the risk priority number (RPN), which is based on qualitative analysis and a table of criteria where they subjectively and intuitively determine the factor level (severity, occurrence, and detection) for each of the failures. With this, imprecision is present due to the interpretation that each one has regarding the failures. Therefore, this research proposes a fuzzy logic evaluation system with a solid mathematical basis that integrates these conditions of imprecision and uncertainty, thus offering a robust system capable of emulating the evaluation form of experts to support and improve decision making. One of the main contributions of this research is in the defuzzification stage, adjusting the centroid method and treating each set individually. With this, the RPN values approximate to the conventional technique were obtained. Simulations were carried out to test and determine the system’s best structure. The system was validated in a textile company in southern Guanajuato. The results demonstrate that the system reliably represents how experts perform risk assessment.

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.

Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. Failure evaluation occurs in a decision-making environment with uncertainty. This study proposes a probabilistic fuzzy system that integrates linguistic and stochastic uncertainty based on a Mamdani-type model to strengthen the FMEA technique. The system is based on analyzing the frequency of failures and obtaining the parameters to determine the probability of occurrence through the Poisson distribution. In addition, the severity and detection criteria were evaluated by the experts and modeled using the Binomial distribution. The evaluation result is a discrete value analogous to the process of obtaining the success or failure of the expert generating the evaluation of 10 Bernoulli experiments. Three fuzzy inference expert systems were developed to combine multiple experts’ opinions and reduce linguistic subjectivity. The case study was implemented in the knitting area of a textile company in the south of Guanajuato to validate the proposed approach. The potential failure of the knitting machinery, which compromises the top tension subsystem’s performance and the product’s quality, was analyzed. The proposed system, which is based on a robust mathematical model, allows for reliable fault evaluation with a simple scale. The classification performed by the system and the one performed by the experts has similar behavior. The results show that the proposed approach supports decision-making by prioritizing failure modes.

In the present work, it is intended to highlight in a specific way the utility and advances of multivariate control graphics for the control of quality in modern production systems. The synthesis addresses the development of the most commonly used multivariate control charts as well as identifying their scope and limitations. Once the restrictions to carry out a statistical quality control through multivariate charts have been identified, the study of different techniques that complement the quality analysis in the production systems monitored under said control techniques is addressed. Keywords: Multivariate statistical control charts

Introducción: Ante la importancia de la industria eléctrica del país por incidir en la mayoría de los procesos productivos de la economía, los retos en los cambios en el mercado energético y las nuevas regulaciones para los productores, surge la necesidad, desde el aspecto de la manufactura de ofrecer herramientas para mejorar los procesos de fabricación de productos eléctricos, la calidad del servicio y aumentar la confiabilidad del sistema. El transformador es uno de los elementos más importantes en el sistema de transmisión de energía, fabricarlo con bajos estándares de calidad representa problemas importantes tanto para los consumidores finales como para el sistema en general. El propósito de este trabajo es establecer condiciones de operación eficientes para la manufactura de bobinas eléctricas, minimizando la variabilidad de las dimensiones y permitir un correcto ensamble de los componentes que integran el transformador monofásico.Método: Se utilizó el análisis de capacidad para evaluar la habilidad del proceso al cumplir con las especificaciones antes y después de las mejoras, además, se desarrolló un Diseño de Experimentos Factorial 2k que permitió establecer condiciones de operación adecuadas en el proceso de manufactura además de determinar la influencia de la tensión del alambre y el proveedor del papel dieléctrico sobre las variables dimensionales de la bobina. Resultados: Con el estudio se encontró que los factores considerados tienen influencia significativa sobre las dimensiones de las bobinas, los niveles altos de cada factor permitieron minimizar las dimensiones, con ello fue posible cumplir con los límites de especificación, se logró mejorar el centrado del proceso en las variables del largo y ancho de la bobina al incrementar sustancialmente los índices de capacidad reales del proceso.Discusión o Conclusión: Los métodos estadísticos utilizados en este trabajo fueron una herramienta eficaz para estudiar el proceso, minimizar las dimensiones, mejorar la eficiencia del producto y los costos por re-trabajos.