
Course Objective:
• Understand and characterize the variability through the graphic representation of data.
• Visually describe a process through process mapping techniques.
• Apply the DMAIC problem-solving process to process improvement at the Black Belt level. • Develop data collection plans and design experiments to test hypothesis tests.
• Interpret the results of statistical tests and draw conclusions based on data and the application of advanced statistical analysis techniques.
• Integrate tools for statistical analysis, software and problem solving methodologies.
• Develop recommendations and control plans to improve processes.
• Complete an out-of-class process improvement project that demonstrates the application of the full DMAIC methodology
Participants are expected to have a Level Six Sigma Green Belt knowledge in concepts and linear statistical models along with their application to data analysis.
Knowledge of prerequisites includes:
• Value Stream Mapping (VSM)
• Descriptive statistics
• SPC
• Short and Long Term Process Capability
• Concepts of population, sampling and treatment of normal distribution
• Simple and multiple linear regression. Pearson correlation
• Hypothesis Tests (Mean, variance and proportions). P-value, Type I error (alpha)
• ANOVA
• Design of Experiments (Factorial, ANOVA of two variables)
• A3 thinking
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