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Six Sigma: Define and Measure (Coursera)

Six Sigma: Define and Measure (Coursera)

Key Facts

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Provider
School
Language
Level
Coursera
Technical University of Munich
English
Introductory

By enrolling in this online course you will spend approx. 8 Weeks/3–4 hours per week to learn key concept of Business.

Course Overview

Understand the background and meaning of Six Sigma and the five steps of the DMAIC process improvement flow: Define, Measure, Analyze, Improve and Control. Discuss what “Quality” means and how to identify the Voice of the Customer.

You will learn how to set an improvement project goal, calculate process yield, and identify Critical-to-Quality parameters.

You will learn how to map a process and to use the necessary statistical techniques to establish the baseline performance of a process and to calculate the process capability.

To complement the lectures, we provide interactive exercises, which allow learners to see the statistics “in action.” Learners then master the statistical concepts by completing practice problems. These are then reinforced using interactive case studies, which illustrate the application of the statistics in quality improvement situations.

Upon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.

Course Syllabus

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.

Meet your Instructors

Martin Grunow

Martin Grunow is a professor of production and supply chain management at Technische Universität München and an adjunct professor at Technical University Denmark, where he previously held a professorship in Operations Management. Earlier, he worked at Technical University Berlin and in the R&D department of Degussa, a multinational company producing special chemicals. His research interests are in production and logistics management with a focus on the process, electronics and automotive industries. He has coauthored more than 100 publications amongst others in International Journal of Production Economics, International Journal of Production Research, European Journal of Operational Research, CIRP Annals, Flexible Services and Manufacturing Journal, and OR Spectrum. For the latter two journals, he also acts as an editor. Martin Grunow has been on the program committee and track organizer of more than 30 international conferences and is an associate member of The International Academy of Production Engineering.

Holly Ott

Holly Ott is a professor of Production Management and IT Systems at the University of Rosenheim Applied Sciences and Management, a senior lecturer at the Technical University of Munich and an adjunct professor at Singapore Management University (Singapore), IE Business School (Madrid) and Syracuse University (New York). She holds a Ph.D. in electrical engineering from the University of Virginia and has worked for twelve years in quality and supply chain management in the USA, Europe and Asia for Siemens, Motorola, IBM and Infineon. Her work has been in the areas of device simulation, electrical and reliability testing, and quality and supply chain management. She holds a International Association for Six Sigma Certification (IASSC) Certified Lean Six Sigma Green Belt (ICGB) and is a member of the American Society for Quality (asq.org). Holly is the program chair for the Singapore Semiconductor Industry Association’s Supply Chain Forum and academic director of the TUM Case Centre.

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