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Certificates: "Six Sigma Green Belt" Certificate
Certificate "Statistics and data analysis" -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
Six Sigma Green Belt -
Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
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Language of Instruction: German
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Duration: 8 Weeks
Quality Management – Six Sigma Green Belt
Introduction to Six Sigma (approx. 1 day)
What is Six Sigma? History, Philosophy, and Strategy
DMAIC Project Phases, Objectives, and Roles
Six Sigma in the Context of Lean, Quality Management, and Operational Excellence
Hybrid Project Management with DMAIC for AI Projects
AI-Powered Project Management (approx. 1 day)
Fundamentals of Artificial Intelligence
Applications Along the DMAIC Framework, Limitations, and Risks
Data and Prompt Fundamentals, Context, and Quality Criteria
Governance, Security, the EU AI Act, and the GDPR
AI Dashboard, Information Consolidation, and Quality Gates
Define – Define (approx. 3 days)
Project identification, strategic relevance, and key metric driver tree
Distinction from CIP and PUL (Problem, Cause, and Solution)
Project Profile: Problem Description and SMART Goal Definition
In-scope and out-scope definitions, as well as AI-supported completeness check
LIPOK and Voice of the Customer (VoC)
Affinity Diagram and Kano Model
VoC-to-CTQ transformation, specifications, and acceptance criteria
AI-supported analysis and technical validation of customer requirements
Cost-effectiveness: ROI, TCO, and qualitative benefits
Stakeholder Analysis and AI-Supported Project Planning
Risk Analysis and Preventive Measures
Change management and motivation for change
Communication strategy, elevator pitch, and AI-supported change story
Quality Gate Review: Define
Measure – Measurement (approx. 3.5 days)
Mathematical process model and cause-and-effect chain of the CTQs
Influence analysis using Ishikawa and KSR, as well as an AI-supported metrics matrix
Data Types and Measurement Scales
Operational definition, data collection methods, and sampling strategy
Data collection plan and control using an AI dashboard
Measurement system analysis: bias, repeatability, reproducibility, and gage R&R
Data quality, plausibility checks, and AI-supported data cleaning
Data Visualization, Statistical Metrics, and Machine and Process Capability
AI-Supported Interpretation of Measurement Results and Quality Gate Review Measure
Analyze – Analysis (approx. 3.5 days)
Process Map and Data Map, as well as Value Stream Analysis
Value stream analysis, spaghetti diagram, TIMWOODS, and Makigami
Distinguishing Between Symptoms, Problems, and Causes
Cause-and-effect and Pareto analysis, as well as AI-supported error code clustering
Hypothesis Formulation and AI as a Sparring Partner
Design of Experiments and AI-assisted testing of influencing factors
Correlation and regression analysis, as well as statistical hypothesis testing and group comparisons
AI-Supported Evidence Matrix and FMEA
Quality Gate Review and Analysis
Improve – Innovative Improvement (approx. 3 days)
Creativity Techniques and AI-Supported Solution Development
Lean solution principles as well as technical and organizational measures
Solution approaches using generative AI, automation, and robotics
Regulatory and organizational requirements for AI deployment
AI-specific risk assessment, as well as feasibility and maturity analyses
Solution evaluation
Acceptance and cost-benefit analysis
Implementation plan and pilot program with success and acceptance criteria
Iterative Approach to AI Pilots and Quality Gate Review Improve
Control – Controlling and Sustainable Implementation (approx. 2 days)
Go-Live, Process Stabilization, and Support During the Start-up Phase
Standard Operating Procedures and Controlling Plan
KPIs, statistical process control, control charts, and corrective actions
Balanced Scorecard and AI-Supported Monitoring
Audit Concept
Project completion, handover, and AI-supported documentation
Quality Gate Review Control
Project work, exam preparation, and final exam (approx. 3 days)
Statistics and data analysis
Statistical basics (approx. 6 days)
Measurement theory basics (population, sample, sample types, measurement, scale levels)
Univariate descriptive statistics (frequency distributions, central measures, measures of dispersion, standardization, histograms, bar charts, pie charts, line charts, box plots)
Bivariate descriptive statistics (measures of correlation, correlation coefficients, crosstabs, scatter plots, grouped bar charts)
Basics of inductive inferential statistics (probability distributions, normal distribution, sampling distribution of the mean, significance test, null hypothesis test, significance level, effect size, parameter estimation, confidence intervals, error bar charts, power analysis, sample size)
Data preparation and data cleansing with suitable software
Descriptive analysis
Visualization of statistical results
AI-supported analysis and interpretation of statistical results
Methods for comparing two groups (approx. 5 days)
z-test, t-test for one sample
t-test for independent and related samples
Pretest-posttest designs with two groups
Supporting significance tests (Anderson-Darling test, Ryan-Joiner test, Levene test, Bonett test, significance test for correlations)
Nonparametric methods (Wilcoxon test, sign test, Mann-Whitney test)
Contingency analyses (binomial test, Fisher's exact test, chi-square test, cross-tabulations, measures of association)
Interpretation of test results
AI-supported interpretation of results
Basics of regression analysis (approx. 2 days)
Linear regression
Model interpretation
AI-supported model interpretation
Correlation analysis
Methods for comparing the means of several groups (approx. 3 days)
One-factorial and two-factorial analysis of variance (ANOVA)
Post-hoc analyses
Interpretation of group differences
Multi-factorial analysis of variance (general linear model)
Fixed, random, crossed and nested factors
Multiple comparison methods (Tukey-HSD, Dunnett, Games-Howell)
Interaction analysis
Power analysis for variance analyses
Introduction to Design of Experiments (DoE) (approx. 1 day)
Full factorial and partial factorial experimental designs
Project work (approx. 3 days)
To consolidate the content learned
Presentation of the project results
Changes are possible, the course content is updated regularly.
After completing the course, you will be able to independently and successfully implement Six Sigma projects in accordance with ISO 13053-1 and the DMAIC methodology. You will confidently apply the specific tools and methods of process and data analysis, derive improvements based on data, and consistently use AI as a methodological aid. You will design Quality Gate reviews based on the DMAIC methodology and develop AI-powered dashboards for project management, quality control, and decision support.
You will also understand the basics of statistics, be able to process and evaluate data and present, explain and interpret statistical data analyses and results using graphics.
Project managers, individuals with degrees in engineering, technicians, master craftsmen, as well as skilled workers in the manufacturing and service sectors, and professionals in the fields of quality management, administration, and sales who wish to implement and lead improvement projects using the Six Sigma method.
Users and specialists from social and market research, business administration (marketing, business intelligence), technical areas, production, quality assurance and research in the healthcare sector.
Six Sigma is an internationally standardized and recognized method of quality management and a management tool for process improvement. By obtaining the Green Belt, you demonstrate your mastery of the methodology in your profession. As a Six Sigma project manager, you will be in demand across all industries in medium-sized and large companies, thus enhancing your career profile in the long term.
A sound knowledge of statistics is a valuable additional qualification that is in great demand in industrial research and development, in drug development, in the supervision of medical studies, in finance and insurance, in information technology or in public administration.Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).