OpEx Six Sigma Black Belt is advanced training. Although the participants are learning the more advanced techniques and tools of Six Sigma, this is not the main objective of the course. The practical and effective usage of these tools is the key concept shaping the program’s philosophy. Critical Thinking and the ability to ask suitable questions forms the basis for effective use of knowledge in practice.
Pure data is no substitute for knowledge, and indeed knowledge development is the essence of Six Sigma philosophy. Not even the most sophisticated and advanced methods of analysis will replace good data, which is to say data collected in a way that provides an opportunity to increase our knowledge and improve our products. This is the most important competency of Black Belt.
Training is based on practical tasks which involve the participant having a hands?on experience of discovering the knowledge which we want to transmit to them. We believe that this is the only way to show the practical application of Six Sigma. During training we share our experience. For every subject we present real-life cases which underline the practical application of presented concepts and tools. Training is designed to give all participants the chance to practice each learned concept and tool throughout the course. One training session requires that three to four weeks are dedicated to project work before the participants progress to the subsequent training session. Usually instruction is provided by three trainers, each of whom have second level Master Black Belt Certification and several years of professional experience.
 |  QUESTIONS? CONTACT US:MOBILE:+48 783 191 353 KONTAKT@E-OPEX.PL |
szkolenia@e-opex.pl
For those, who will send their application 30 days prior to the workshop, we offer a discount of 10%!
- Help in defining the Black Belt project
- Ability to consult with a trainer during the whole training, the break between sessions and after the workshop
- The book Leader’s Handbook, P.R. Scholtes ? Polish translation
- OpEx Six Sigma Black Belt certification exam
- Training conducted by two or three experienced trainers
- Additional training materials based on trainee’s individual needs
- A set of training materials
- Username and password for the Client Zone allowing use of many additional materials
- Coffee breaks and lunch
After this training participants will be skilled in:
- Data collection planning, yielding answers to particular question which we have in respect to our processes (Sampling Plan, Design of Experiments)
- Data analysing in Practical, Graphical and Quantitative forms
- Using of Control Charts in a passive way (process monitoring) as well active (problem solving in processes)
- Evaluating Measurement Systems
- Experimentation planning (factor and factor levels selection as well as selection of experimentation strategy)
- Conducting and analysing experiments (Practical, Graphical and Quantitative) and the drawing conclusions based on experimental results
SESSION I (3 days) | FORM OF EXECUTION | ||
|---|---|---|---|
| Project Examples | case study | ||
| Introduction to Six Sigma | lecture | ||
| Critical Thinking, Thought Map, examples of Thought Maps | lecture + case study | ||
| Introduction to Variation (basic statistics) | lecture | ||
| Quincunx: practical exercise which shows why the distinction between common and special cause variation is so important | practical exercise | ||
| Process Mapping, Examples of Process Maps, Rate of Change Table | lecture + case study + exercise | ||
| Introduction to Sampling: Sampling Trees, Box Plots, Range and Mean Charts | lecture + practical exercise | ||
| Approval of the first piece: helicopter | practical exercise | ||
| Measurement System Evaluation | lecture + practical exercise | ||
| Measurement System Evaluation for alternative data | lecture + practical exercise | ||
| Project review |
SESSION II (3 days) | FORM OF EXECUTION | ||
|---|---|---|---|
| Session A refresh | game | ||
| Individual Moving Range Control Charts: Introduction and practical usage | lecture + case study | ||
| Components of Variation Study: Projects Examples, Introduction | lecture + case study | ||
| Sampling based on Helicopter Manufacturing | practical exercise | ||
| Components of Variation study: Sampling Trees, Practical, Graphical & Quantitative Analysis | lecture + practical exercise | ||
| Introduction to Experimentation | lecture | ||
| Project Example ? planned experimentation utilization | case study | ||
| Experimentation methods comparison | lecture | ||
| Full Factorial Design | lecture + practical exercise | ||
| Fractional Factorial Design | lecture + practical exercise | ||
| Helicopter experimentation, sequential approach | lecture + practical exercise | ||
| Project review |
SESSION III (5 days) | FORM OF EXECUTION | ||
|---|---|---|---|
| Experimentation on the broad range of conditions, limitations of conclusions, what is the reason that conclusions drawn from experiment are not valid in reality | practical exercise | ||
| Necessity of noise incorporation into the experiment. Experiment matrix is the planning phase only | lecture + exercise + case study | ||
| Factor Relationship Diagram FRD as main tool for noise management | lecture | ||
| Randomization limitation, consequences of conclusions limitation, ways of analysis | lecture | ||
| Noise management into experiment ? basic strategies and their influence on our knowledge increase and how to analyse: Repeats, Replicates, Blocking | lecture + practical exercise | ||
| Analysis of Variance(ANOVA): Fully Nestet Anova i Crossed ANOVA | lecture | ||
| Project review |
SESSION IV (5 days) | FORM OF EXECUTION | ||
|---|---|---|---|
| Components of Variation Study ? sampling strategies: Nested, Systematic, Crossed | lecture + practical exercise | ||
| Limitations of conclusions and way of analysis dependent on selected sampling strategy | Practical exercise | ||
Noise management into experimentation ? strategies continuation:
| lecture + exercises | ||
| Special causes into the experiment, diagnostics and follow up | lecture | ||
| Data Transformation: when is needed? Which transformation is the best? How to transform the data? | lecture + practical exercise | ||
| Model building: description of y = f(x) + Noise | lecture | ||
| Residuals Analysis | lecture | ||
| Project review |