Six Sigma: Define and Measure

24 h
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  • 8 Sequences
  • Introductive Level

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Week 1: Six Sigma Introduction
Introduction to the Six Sigma Methodology and the DMAIC process improvement cycle. Understand the contributors to the cost of quality. Discuss the difference between defects and defectives in a process and how to calculate process yield, including a comparison of processes of different complexity using the metric DPMO.

Week 2: DEFINE - Defining the Problem
Discuss how to understand customer expectations, using the Kano Model to categorize quality characteristics. Start the first and difficult task of a Six Sigma project, Defining the Problem, and review the key content in a Project Charter.

Week 3: MEASURE - Statistics Review
Review of random variables and probability distributions used commonly in quality engineering, such as Binomial, Poisson, and Exponential. Cover descriptive statistics, emphasizing the importance of clearly communicating the results of your project.

Week 4: MEASURE - Normal Distribution
Learn the characteristics of the Normal Distribution and how to use the Standard Normal to calculate probabilities related to normally distributed variables. Cover the Central Limit Theorem, and how it relates to sampling theory.

Week 5: MEASURE - Process Mapping
Introduce Process Mapping, including SIPOC and Value Stream Mapping. We identify the Critical-to-Quality characteristic for a Six Sigma project

Week 6: MEASURE - Measurement System Analysis
Learn the basics of Measurement Theory and Sampling Plans, including
Precision, Accuracy, Linearity, Bias, Stability, Gage Repeatability & Reproducibility

Week 7: MEASURE - Process Capability
Introduction to Process Capability and the metrics CP/CPK for establishing our baseline process performance.

Week 8: Quality Topics and Course Summary
Cover the basics of Tolerance Design and the risk assessment tool failure Mode and Effects Analysis (FMEA).
Review the complete Six Sigma Roadmap before summarizing and closing the course.


This course reviews basic statistics used in Six Sigma and quality management, applying them to quality problems and methods. Therefore, basic math skills are necessary.


Martin Grunow
Professor of Production and Supply Chain Management
Technical University of Munich

Holly Ott
Senior Lecturer in Operations Managementat Technical University of Munich and Professor Production Management at Rosenheim University
Technical University of Munich


Technische Universität München


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