Here is our complete course outline for the Black Belt course. Why do we display to all of our competitors our intellectual property? Because we believe that when you are making a decision to choose a supplier for your training, you should know what you are getting for your money. Our training course provides the depth and breadth necessary to be successful in many different industries when you run into common real-world situations not covered in other "follow the recipe" training approaches. We provide you with sufficient background and understanding of the tools in order to avoid catastrophic decisions made because something in the real world happened to be different from the ivory tower assumptions you might find elsewhere.
We encourage you to compare what you get from our training to any other Black Belt course (if you can even get them to give you an outline of what they are selling). This doesn't make our training the easiest Black Belt course out there.
It just makes it the best.
Background and General Skills
- What is Six Sigma?
- Six Sigma as a business initiative
- Importance of customers
- Customer satisfaction, dissatisfaction, complaints
- Design and Conformance quality
- Drivers of Business Performance
- Continuous improvement vs. breakthroughs
- Plan-Do-Check-Act
- Importance of financial analysis of improvements
- Concepts of Design for Six Sigma
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Define Opportunity / Measure Current State
- Effective teaming tools
- Using data
- Data and measurement
- Research questions
- QCDISME measures
- Defining CTQ, CTC, CTD, CTP
- Yield measures (TY, RTY, normalized yield)
- Key performance indicators (KPIs)
- Balanced scorecard concepts
- Process vs. results measures
- Measurement and measurement scales
- Nominal
- Ordinal
- Interval
- Ratio
- Absolute
- Discrete vs. Continuous
- Introduction to statistical methods
- Populations and samples
- Random sampling
- Defining "statistic" and "parameter"
- Sampling error
- Arranging and presenting data
- Run charts
- Frequency distributions
- Frequency polygons
- Histograms
- Descriptive statistics
- Four aspects of data
- Time
- Shape
- Spread
- Location
- Simple probability
- Probability defined
- Independent vs. dependent events
- Probability distributions
- Bernoulli processes
- Binomial distribution
- Poisson distribution
- Normal or Gaussian distribution
- Exponential distribution
- Distribution approximation
- Johnson
- Weibull (covered further in reliability)
- Distribution fit testing
- Normality testing
- Anderson-Darling
- Shapiro-Wilk
- Lin-Mudholkar
- Skewness and Kurtosis
- Exponential testing
- Poisson testing
- Transformations
- Estimation
- Random sampling distributions
- Criteria for "good" estimators
- Unbiased
- Efficient
- Consistent
- Sufficient
- Point estimate
- Confidence intervals
- Means (z and t)
- Standard deviation
- Proportions (exact binomial)
- Process variation
- Concept of variability
- Sources of variability
- Short- and long-term variability
- Common cause variability
- Special cause variability
- Statistical control
- Control charts identify special causes
- Control chart pattern rules (more on control charts later)
- Process dominance concept
- Purpose of specifications
- Product control cycle
- Taguchi loss function
- Process control cycle
- Process control as constrained variation
- Other process control technologies
- Measurement Systems Analysis (MSA)
- Measurement as a process
- Continuous Gages
- Definitions
- Reference value
- Resolution
- Precision
- Accuracy
- Repeatability
- Reproducibility
- Linearity
- Stability
- Measurement system capability (% R&R or P/T)
- Types of studies
- Potential
- Short-term
- Long-term
- Steps to perform MSA
- Potential study
- Data collection
- Data analysis
- Effect of averaging multiple measures
- Short-term study
- Data collection
- Data analysis
- Long-term study
- Data collection
- Data analysis
- Destructive tests
- Effect of Class I, II, and III destructive tests
- Discrete Gauges
- Terms
- Reliability
- Agreement
- Internal consistency
- Concordance
- Validity
- Concordance with a standard
- Measuring Agreement
- Equality of proportions is not agreement
- Dependent χ2 is not agreement
- McNemar's Test of Change is disagreement, not agreement
- Absolute and Relative Agreement
- Assessing Agreement
- Kappa (κ) is a coefficient of agreement
- Notes on κ
- Procedures for Measurement System Analysis on Discrete Data
- MSA for Discrete Data
- Sample sizes
- Potential study
- Long-term study
- Subjective analysis is insufficient
- Assumptions for discrete gauges
- Sample considerations
- MSA procedure for discrete gauges
- MSA guidelines for discrete gauges
- Two inspectors, two categories
- κmax
- Assessing internal consistency
- Two inspectors, more than two categories
- Post-hoc for more than two categories
- Assessing internal consistency for more than two categories
- Testing for significance of κ
- Testing for κ = 0
- Testing for κ = κ'
- Validity analysis for discrete data
- Agreement vs. validity
- Light's G
- More than two appraisers
- Long-term control of discrete gauges
- Generating control chart limits for κ
- Capability of discrete gauges
- Process Characterization
- Process capability for variable data
- Steps to perform a capability analysis
- Cp
- Cpk
- Cpm
- What to do if the process is not capable
- Process capability for attribute data
- Process performance analysis
- When to use
- Pp
- Ppk
- Ppm
- Cp(potential)
- Variance components
- Sigma measures
- Strengths and weaknesses
- Calculate sigma from:
- z-score
- Defects per million opportunities
- Ppm
- Other Six Sigma measures
- Total opportunities
- Defects per unit
- Defects per unit opportunity
- Defects per million opportunities
- Yield relations
- Throughput yield
- Defects per unit
- Rolled throughput yield
- Total defects per unit
- Normalized yield
- Defects per normalized unit
- Other measures
- Cycle time
- Uptime
- Mean time between failures
- Asset utilization
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Control Improved Process
- Prevention planning and analysis
- Defining process control
- Process control methods
- Steps to perform PPA
- Ongoing use
- Statistical process control (SPC)
- Tools for process study
- Steps to develop a control chart
- Select a characteristic
- Select sampling plan
- Select chart type
- Collect data
- Generate chart
- Assess control
- Assess process capability
- Process performance measures
- What to do if the process is not capable
- X-bar and R
- X-bar and s
- X and moving range
- X & MR concerns
- Sensitivity
- Distribution shape
- Autocorrelation
- Handling stratified data
- p-charts
- np-charts
- c-charts
- u-charts
- Process performance analysis
- Principles of robust product and process design
- Optimization
- Variation transmission
- Tooling design
- Raw materials and components
- Process settings
- Operational methods
- Generic technology
- Mistake-proofing
- Human error
- Types of errors
- Techniques
- Successive check
- Sensors detecting an error
- Sensors detecting a machine condition before failure
- Pins or guides preventing an error
- Correct counts are ensured
- Alarm is used
- Machine is automatically shut down
- Color coding
- Checklist minimizes chance of error
- Automatic measurement prevents an error
- Sequence must be followed in a proper order
- Fixed number of parts is ensured
- Wrong part cannot be selected
- Forcing a part to be oriented correctly
- Ensuring safety
- Automatic control systems
- Dangers of automatic control systems
- Autocorrelation
- Run tests
- Types of control systems
- Adjustment charts
- Principles of standardization
- PDSA (Plan-Do-Standardize-Act)
- Features of standardization
- Defining standard operating procedure
- Reliability methods
- Reliability definitions
- Reliability
- Failure
- Lifetime
- MTBF
- MTTF
- Reliability roadmap
- Improving process for Added Capacity
- Total productive maintenance
- Total asset utilization
- Building New Equipment for Added Capacity
- Reliability modelling
- Design specification
- Design reviews
- Tools
- FMEAs
- Growth Analysis
- Weibull Analysis
- Fault Tree Analysis
- Root Cause Analysis
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