Analizar e incrementar - Parte 1

This module focuses on the ANALYZE phase of Lean Six Sigma, where various techniques from both Six Sigma and Lean are applied to perform an in-depth analysis of processes. The goal is to determine significant factors that influence process behavior and use them to achieve better performance levels. The module covers the importance of hypothesis testing, confidence intervals, and Analysis of Variance (ANOVA) for one and two factors. Students learn statistical inference techniques to identify significant process variables.

This module explores stability in production, which implies general predictability and consistent availability in terms of labor, machines, materials, and methods - the 4 Ms. Students learn how Toyota establishes a consistent and predictable process with these fundamental elements before implementing flow and takt time. The module focuses on the 5S methodology and visual factory concepts, essential Lean tools that help identify critical points in processes. Students understand how these techniques create workplace organization and visual communication systems that support continuous improvement.

This module explores continuous flow tools in Lean manufacturing. In its ideal state, continuous flow means products are processed and transported directly from one process stage to the next, one piece at a time. The module covers how each processing stage works only on the piece needed by the next step just before it's needed, with a batch size of one. Students learn about manufacturing cells, which are arrangements of people, machines, and materials with process stages placed sequentially to enable continuous flow. The module also covers SMED (Single-Minute Exchange of Die) techniques for reducing setup times to support small batch production and flexibility.

This module covers experimental techniques used to determine significant factors in a process and optimize it. Students learn about design of experiments, a pure experimentation technique, and regression analysis, a technique for modeling a process with factors that can vary naturally or experimentally. The module focuses on 2ᵏ factorial designs and simple linear regression models. Through these techniques, students gain the ability to identify significant X factors in a process, understand their relationships, and build predictive models to optimize process performance. The module includes an expert interview and practical exercises in determining significant factors.