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 realworld 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
 PlanDoCheckAct
 Importance of financial analysis of improvements
 Concepts of Design for Six Sigma

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
 AndersonDarling
 ShapiroWilk
 LinMudholkar
 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 longterm 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
 Shortterm
 Longterm
 Steps to perform MSA
 Potential study
 Data collection
 Data analysis
 Effect of averaging multiple measures
 Shortterm study
 Data collection
 Data analysis
 Longterm 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 χ^{2} 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
 Longterm 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
 κ_{max}
 Assessing internal consistency
 Two inspectors, more than two categories
 Posthoc 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
 Longterm 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
 C_{p}
 C_{pk}
 C_{pm}
 What to do if the process is not capable
 Process capability for attribute data
 Process performance analysis
 When to use
 P_{p}
 P_{pk}
 P_{pm}
 C_{p(potential)}
 Variance components
 Sigma measures
 Strengths and weaknesses
 Calculate sigma from:
 zscore
 Defects per million opportunities
 P_{pm}
 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

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
 Xbar and R
 Xbar and s
 X and moving range
 X & MR concerns
 Sensitivity
 Distribution shape
 Autocorrelation
 Handling stratified data
 pcharts
 npcharts
 ccharts
 ucharts
 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
 Mistakeproofing
 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 (PlanDoStandardizeAct)
 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
