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3P for Manufacturing
Many manufacturing organizations struggle to launch new products or ramp up new processes smoothly and quickly. The production preparation process, or 3P, offers a powerful approach to achieving product and process innovations. Learn the key differences between traditional development approaches and 3P, its main benefits, and when it’s commonly used. Follow the step-by-step process to develop and test process alternatives, build“moonshine” equipment, study the 7 flows, apply the 16 catchphrases, and develop innovative products and processes.
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Análisis de Varianza (ANOVA)
Análisis de Varianza, o ANOVA por sus siglas en inglés, es un método para comparar las medias de tres o más muestras con el fin de determinar si al menos una de ellas difiere estadísticamente de las demás. Aprenda los conceptos clave de estadísticas como efectos principales, interacción, influencia, grados de libertad, estadístico F, R cuadrado y cómo seguir el proceso paso a paso para realizar ANOVAs de un factor y de dos factores.
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Mejora Continua y Control
La fase final de un proyecto DMAIC es la de Controlar. Su propósito es mantener las soluciones implementadas y prevenir la recurrencia del problema. Para ello, es necesario actualizar documentos y estándares, llevar a cabo capacitaciones y comunicaciones sobre los cambios, y comprometerse con el monitoreo continuo y la mejora del proceso. En este curso, aprenderá cuáles son los entregables clave y las actividades de la fase de Controlar que permiten alcanzar estos objetivos.
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Instrucción de Trabajo (JI) para Facilitadores
Instrucción de Trabajo (JI) es un elemento fundamental de Lean y el primer curso de TWI (Capacitación Dentro de la Industria). Su propósito es lograr que las personas estén completamente capacitadas y comiencen a aportar valor a la organización en el menor tiempo posible, mientras se previenen problemas que podrían surgir por una instrucción deficiente. La Instrucción de Trabajo (JI) establece un único método óptimo para realizar un trabajo y permite que un facilitador transfiera conocimientos al aprendiz de forma rápida, al mismo tiempo que verifica su comprensión mediante un método simple de cuatro pasos. La versión para facilitadores de este curso de Instrucción de Trabajo (JI) puede utilizarse para prepararse para su propio curso presencial de 5 días. Usted podrá observar un curso de Instrucción de Trabajo (JI) en vivo, de 5 días, desarrollarse frente a sus ojos, mientras Ron Pereira y Roger Bilas guían a 10 estudiantes a lo largo de todo el curso de 10 horas. Verá cómo los estudiantes aprenden y progresan durante la semana, y también observará cómo Roger y Ron los apoyan a lo largo del proceso. Una vez que haya completado este curso para facilitadores, podrá usar la versión para aprendices de este curso para impartir su propio curso de Instrucción de Trabajo (JI) de 5 días.
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Análisis del Sistema de Medición (MSA)
La variación total de un sistema es la suma de la variación real de un producto, proceso o sistema, y la variación del sistema de medición. El Análisis del Sistema de Medición (MSA, por sus siglas en inglés) es un método estadístico utilizado para evaluar si la variación dentro de nuestro sistema de medición es aceptable. Aprenda cuáles son las cinco características de un sistema de medición, la diferencia entre el MSA de datos de atributos y de datos variables, y los pasos para realizar un Estudio R&R.
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Practical Problem Solving
Problem-solving is at the heart of continuous improvement and business excellence. Eight-step practical problem-solving is a structured but flexible approach that guides teams through problem definition, cause analysis, and the development and implementation of countermeasures. Learn the many investigative techniques, statistical tools, and guidelines that form this framework.
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Developing Improvement Project Plans
As we move from the problem definition and root cause analysis phases of a problem-solving project to the improvement and implementation phase, we ask the question,“What actions can we take to address the root cause?” Learn the five main activities project leaders must complete to answer this question, develop a plan, and put it into action.
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Design of Experiments
DOE is a framework for planning and conducting experiments to study the effects of multiple input variables, or factors, on an output variable, or a response. DOE is widely used in scientific research, problem solving, and to understand complex phenomena. Learn how and when to use screening DOEs, fractional DOEs, full factorial DOEs, optimization DOEs, and how DOE fits into the Six Sigma DMAIC approach.
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Process Capability
The statistical technique of Process Capability Analysis allows us to evaluate the ability of a process to consistently meet customer specifications or tolerances. It does this by comparing the spread and centering of the process distribution to the specified limits. Learn the elements and steps of Process Capability Analysis and its importance for continuous improvement and Six Sigma.
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Inferential Statistics
Having a solid understanding of the scientific method and how to study a small sample of data in order to make decisions about an entire population is what can separate excellent Continuous Improvement Practitioners from average ones. The inferential statistics course dives into the central limit theorem, z‑scores, graphical analysis, normality, and how to conduct various statistical hypothesis tests.
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Addressing Micromanagement
Effective leadership requires a balance between focus on the big picture and attention to detail, and between firm guidance and empowering people to take ownership of their work. Too much guidance and attention can become micromanagement that harms performance. Learn how to recognize and address micromanaging behavior.
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Regression
Regression analysis is a statistical tool for modeling the relationship between a dependent variable, often referred to as the response variable, and one or more independent variables, also called predictors or factors. Regression analysis plays a key role in analyzing data as part of a Six Sigma DMAIC Project.
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Project Selection
One of the main responsibilities of Six Sigma practitioners is to deliver impactful projects for their organizations. This requires the skill to select the right topic, plan the project, and lead the team. This all begins with Project Selection, a set of structured activities to identify the business need, consider customer input, clearly define the problem, set objectives, identify metrics, calculate savings, and more. Learn how to combine these elements into a Project Charter.
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Poniendo en práctica el Control Estadístico de Procesos (SPC)
El Control Estadístico de Procesos (SPC, por sus siglas en inglés) es un método para supervisar, controlar y mejorar los procesos mediante el análisis de sus datos a lo largo del tiempo. El SPC permite lograr procesos consistentes y predecibles al minimizar la variabilidad. Aprenda los cinco pasos para implementar el Control Estadístico de Procesos, cómo crear un plan de muestreo, definir los tamaños de subgrupos y muestras, la diferencia entre datos de atributos y datos variables, cómo seleccionar el tipo adecuado de gráfico de control para su situación y más.
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The Seven Quality Control Tools
The 7 Quality Control Tools are an essential set of graphing techniques that are used to identify and fix problems that undermine product or process quality. The 7 QC Tools provide continuous improvement practitioners with a systematic approach for identifying and troubleshooting quality issues. Learn the seven quality control tools and how to use them within your organization.
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ANOVA
ANOVA, or Analysis of Variance, is a method for comparing the means of three or more samples to determine if at least one of the sample means differs statistically from the others. Learn the key statistics concepts of main effects, interaction, influence, degrees of freedom, the F‑statistic, R‑squared, and how to follow the step-by-step process for conducting one-way and two-way ANOVAs.
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Measurement System Analysis (MSA)
The total variation of a system is the sum of the actual variation of a product, process, or system and the measurement system variation. MSA, or Measurement System Analysis, is a statistical method to analyze whether the variation within our measurement system is acceptable. Learn the five characteristics of a measurement system, the difference between attribute and variable data MSA, and the steps to conduct and perform a Gage R&R.
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Fault Tree Analysis
(Updated Course)-Fault Tree Analysis is a top-down, deductive approach to understanding how a series of lower-level events can result in an undesired event, accident, or equipment failure. This diagram uses a set of symbols to represent formal logic and to assign probabilities. Performing Fault Tree Analysis helps us to understand how specific systems can fail, assign risk rates, and identify the best ways to reduce these risks.
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FMEA AIAG and VDA
(Updated Course)-Failure Mode and Effects Analysis, or FMEA, is a method for evaluating potential technical risks of failures for products or processes. Learn why the AIAG & VDA organizations made major updates to the FMEA approach, the eight major changes to this approach, and how it helps organizations in the automotive industry become more effective at risk management.
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Temas Introductorios
Una breve introducción a la mejora continua. Descubra qué es Lean, por qué es importante y cómo sentar las bases para un viaje Lean exitoso.
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