date_range Débute le 1 octobre 2017
event_note Se termine le 26 novembre 2017
list 8 séquences
assignment Niveau : Introductif
label Management et Ressources humaines
chat_bubble_outline Langue : Anglais
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Les infos clés

credit_card Formation gratuite
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timer 24 heures de cours

En résumé

Learn how to statistically analyse process data to determine the root cause for process problems and propose solutions and to implement quality management tools, such as FMEA, 8D and the 5 Whys.

Learn how to analyse data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.

You will learn how to perform correlation and regression analyses in order to confirm the root cause and understand how to improve your process and plan designed experiments.

You will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and Failure Modes and Effects Analysis to reduce risk and manage process deviations.

To complement the lectures, learners are provided with 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 Technical University of Munich Lean Six Sigma Yellow Belt Certification, confirming mastery of the fundamentals of Lean Six Sigma to a Yellow Belt level, based on the American Society of Quality's Body of Knowledge for the Certified Six Sigma Yellow Belt. 

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Le programme

Prerequisites : 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.

Week 1: ANALZYE - Inferential Statistics
Review of the Six Sigma Methodology and the DMAIC process improvement cycle and learn the inferential statistics techniques of confidence intervals and hypothesis testing in order to use sample data and draw conclusions about or process centering.
 
Week 2: ANALYZE - Regression and Correlation
Introduction to methods for root cause analysis, including Cause and Effect (Fishbone diagrams) and Pareto Charts. We learn how to perform statistical correlations and regression analyses.
 
Week 3: IMPROVE - Design of Experiments
We plan designed experiments and calculate the main and interaction effects.
 
Week 4: MEASURE - Analysis of Variance
Test the significance of experimental results using an analysis of variance, for both measurement and attribute data.
 
Week 5: CONTROL - SPC and Control Charts
Cover Statistical Process Control & Control Chart Theory, including X-bar and R Charts. and
 
Week 6: CONTROL - Control Charts Introduction
Other control charts, including p-and C- charts and I/MR, and EWMA Charts, are introduced, and review of  the Control and Reponse Plan for Six Sigma projects.
 
Week 7: Quality Tools: FMEA, 8D, 5 Whys
Introduce several important tools used in quality management, including Failure Modes and Effects Analysis, 8 Disciplines and 5 Whys.
 
Week 8: Course Summary and Review.

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Les intervenants

Martin Grunow
Professor of Production and Supply Chain Management
Technische Universität München

Holly Ott
Senior Lecturer in Operations Management
Technische Universität München

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Le concepteur

Technische Universität München (TUM) is one of Europe’s top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of Excellence.
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La plateforme

EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.

EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.

Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.

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