6σ (Six Sigma) management summarizes the successful experience of total quality management, extracts the essence and most effective methods of process management techniques, and has become a management model for improving enterprise performance and competitiveness. It is a standard for measuring business process capability, a method for continuous business process optimization, and an excellent management philosophy. This course will conduct training focusing on difficult Six Sigma tools based on the company's existing lean and quality management foundation.

• Understand the necessary prerequisites for building a successful Six Sigma program and how to create these conditions
• Understand how to design a successful Six Sigma implementation and how to continuously maintain the Six Sigma improvement philosophy
• Cultivate black belts' ability to coach green belts
• Be able to lead Six Sigma activities

• Project leaders, managers, engineers, etc.

• Define phase
• Overview of Six Sigma
• Project selection
• Voice of customer and CTQ
• SIPOC
• Project charter
• Determine project big Y
• Basic statistics
• Basic graphical analysis tools
• Bubble chart
• Advanced KANO model
• Initial quick win analysis
• Measure phase
• Variable measurement system analysis
• Gauge capability index analysis
• Attribute measurement system
• Non-replicable measurement system
• Non-normal capability analysis
• Nonparametric capability analysis
• Process capability analysis
• Detailed process map
• Cause and effect matrix C&E
• Analysis FMEA
• Quick win analysis
• Key factor screening
• Independence test
• Outlier test
• Analyze phase
• Shainin (comparison) method
• Basic concepts of variance components
• General linear model GLM
• Basics of variation source analysis
• Multivariate analysis
• Central limit theorem
• Basics of confidence intervals
• Basics of hypothesis testing
• One sample T-Test
• Two sample T-Test
• Paired T-test
• One-way ANOVA
• Multiple comparison
• Multi-factor analysis of variance
• Multiple regression analysis
• Binary Logistic regression analysis
• Improve phase
• Screen key causes
• Determine variable relationships
• Basics of experimental design
• Full factorial experimental design
• Full factorial design including center points
• Full factorial design including discrete factors
• Full factorial design including block variables
• Full factorial design for discrete responses
• Fractional factorial design
• Response surface analysis RSM
• Evolutionary operation EVOP
• Basics of robust design
• Taguchi robust design
• Dual response surface robust design
• Control phase
• Routine control charts
• Standard three principles
• SPC (non-normal distribution cases)
• SPC (acceptance control charts)
• Documented processes
• Establish control plan
• Risk assessment
• Mistake-proofing
• Standardization and archiving
• Statistical process control
• Evaluate improvement results
• Documentation
• Sharing and project closure