Black Belt Curriculum - Manufacturing
Why do we post our curriculum here where our competitors can
view it? Because we are confident that they do not have the expertise to teach
all these topics, and that even if they did, it would take more than the four
weeks our training takes. Click on the folder icons below to explore the
topics. Compare it to any Six Sigma vendor's curriculum (if they will even let
you see it) and let us know what you conclude.
In addition, we train Champions, Design for
Six Sigma (DFSS) Black Belts, and Green Belts.
- 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
Define Opportunity / Measure Current
State
- Effective teaming tools
- 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
- Nominal
- Ordinal
- Interval
- Ratio
- Absolute
- Discrete vs. Continuous
- Populations and samples
- Random sampling
- Defining "statistic" and "parameter"
- Sampling error
- Run charts
- Frequency distributions
- Frequency polygons
- Probability defined
- Independent vs. dependent events
- Bernoulli processes
- Binomial distribution
- Poisson distribution
- Normal or Gaussian distribution
- Exponential distribution
- Johnson
- Weibull (covered further in reliability)
- Anderson-Darling
- Shapiro-Wilk
- Lin-Mudholkar
- Skewness and Kurtosis
- Exponential testing
- Poisson testing
- Transformations
- Random sampling distributions
- Unbiased
- Efficient
- Consistent
- Sufficient
- Point estimate
- Means (z and t)
- Standard deviation
- Proportions (exact binomial)
- 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 as a process
- Reference value
- Resolution
- Precision
- Accuracy
- Repeatability
- Reproducibility
- Linearity
- Stability
- Measurement system capability (% R&R or P/T)
- Potential
- Short-term
- Long-term
- Steps to perform MSA
- Data collection
- Data analysis
- Effect of averaging multiple measures
- Data collection
- Data analysis
- Data collection
- Data analysis
- Effect of Class I, II, and III destructive tests
- Steps to perform a capability analysis
- Cp
- Cpk
- Cpm
- What to do if the process is not capable
- Process capability for attribute data
- When to use
- Pp
- Ppk
- Ppm
- Cp(potential)
- Variance components
- Strengths and weaknesses
- z-score
- Defects per million opportunities
- Ppm
- Total opportunities
- Defects per unit
- Defects per unit opportunity
- Defects per million opportunities
- Throughput yield
- Defects per unit
- Rolled throughput yield
- Total defects per unit
- Normalized yield
- Defects per normalized unit
- Cycle time
- Uptime
- Mean time between failures
- Asset utilization
- Scientific hypotheses
- Sources of industrial mythology
- Plan-Do-Check-Act cycle (PDCA)
- Location
- Stability
- Variability
- Recurrence rate
- Duration without intervention
- Nominal
- Countable
- Continuous
- Additive
- Interactive
- Primary
- Causal chains
- Root causes
- Certain vs. probabilistic causes
- Common vs. special causes
- Self-evident
- Trouble-shooting
- Trial and error
- Analytical methods
- Knowledge of the causal mechanism
- Benefit of taking action on deeper causes
- Chaos
- Sporadic
- Control
- Incremental improvement
- Breakthrough
- Integrate with Prevention Planning and Analysis (PPA) more on
this in reliability
- Statistical vs. scientific hypothesis
- Hypothesis test
- Statistical significance
- Null hypothesis
- Alternative hypothesis
- Directional / one-tail hypothesis
- Non-directional / two-tail hypothesis
- Alpha (a) error and risk
- Population
- Sample
- Statistic
- Test statistic
- p-value of test statistic
- Strategies for deciding Type II (b) error
- Calculating Type II (b) error and
power
- Power curves
- Hypothesis testing procedure
- How to choose the appropriate statistical test for a data
set
- Importance of sample size calculations
- a - Type I
- b - Type II
- s - Standard deviation
- D - Delta
- Means
- Variance
- Proportion
- Rates
- ANOVA
- Correlations
- Definition
- Hypothesis testing steps
- z-test
- t-test
- Variance (c2)
- Exact binomial
- Sign test for location
- Definitions
- Product moment coefficient (r)
- Coefficient of determination (r2)
- r=0
- Spearman's rank order correlation (rs)
- Other measures (rbi, f,
Cramer's v)
- Linear
- Curve fitting
- Confidence and prediction
- Independence vs. dependence
- Variance known (z)
- Presumed equal
- Presumed unequal
- F-test
- Levene test (ADM)
- ADM(n-1)
- Fisher's Exact
- c2
- Normal approximation (discouraged)
- Wilcoxon-Mann-Whitney Test (U)
- Dependent by nature and design
- Iso-plot
- Paired t-test for means
- Matched pair t-test for variances
- Two-sample sign test
- McNemar's test of change
- Purpose of research
- The scientific method
- Non-experimental
- Experimental
- Variable
- Dependent variable
- Criterion measure
- Independent variable
- Level of a treatment factor
- Single factor experiment
- Factorial experiment
- Fractional factorial experiment
- Experimental unit or test unit
- Population
- Research population
- Inference space
- Sample
- Replication
- Repetition
- Randomization
- Experimental or sampling error
- Statistical inference
- Confounding
- Internal validity
- External validity
- Planning
- Confidence
- Power
- Threats to internal validity
- Designs resisting threats to external validity
- Threats to external validity
- State the Problem or Research Purpose
- State the research question
- State the dependent variable(s)
- Select the associated criterion measure(s)
- Identify and classify all independent variables
- Perform measurement systems studies as appropriate
- Select levels of the incorporated treatment variables
- Select an appropriate experimental design
- Develop the experimental plan
- Run the experiment
- Analyze the results
- Run confirmation studies as required
- Report the findings
- Principles of ANOVA
- Statistical importance
- Bartlett-Box F
- Levene test
- ADM(n-1)
- Fisher's LSD (t-tests)
- Bonferroni
- Tukey HSD and Games & Howell
- Scheffé and Brown-Forsythe
- Post-hocs for dispersion
- Importance of understanding random factors
- Between components variance
- Intraclass correlation coefficient (importance)
- 2x2 factorial designs
- Importance
- With interaction
- Without interaction
- JxK designs
- Random and mixed models
- Nesting
- Blocking
- Unequal n
- Handling empty cells
- Nominal data
- Poisson and count data
- Ordinal data
- Three way analysis
- Three way interactions
- Dispersion analysis
- Random, mixed, and nested models
- More than three-way designs
- Purpose of screening experiments
- Designing and customizing
- Confounding
- Linear graphs
- Post-hoc
- Statistical importance
- % RFC
- Practical importance
- Dispersion
- Pooling
- Creating a 4-level factor
- Creating a 3-level factor
- Interactions of high level factors
- Creating an 8-level factor
- 3N series arrays
- Purpose
- Best Run
- Tentative Best Combination
- Worst Run
- Tentative Worst Combination
- Current design/configuration
- Follow-up investigations
Control Improved Process
- Defining process control
- Process control methods
- Steps to perform PPA
- Ongoing use
- Tools for process study
- Select a characteristic
- Select sampling plan
- Select chart type
- Collect data
- Generate chart
- Assess control
- What to do if the process is not capable
- X-bar and R
- X-bar and s
- Sensitivity
- Distribution shape
- Autocorrelation
- Handling stratified data
- p-charts
- np-charts
- c-charts
- u-charts
- Process performance analysis
- Optimization
- Variation transmission
- Tooling design
- Raw materials and components
- Process settings
- Operational methods
- Generic technology
- Human error
- Types of errors
- 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
- Autocorrelation
- Run tests
- Types of control systems
- Adjustment charts
- PDSA (Plan-Do-Standardize-Act)
- Features of standardization
- Defining standard operating procedure
- Reliability
- Failure
- Lifetime
- MTBF
- MTTF
- Total productive maintenance
- Total asset utilization
- Reliability modelling
- Design specification
- Design reviews
- FMEAs
- Growth Analysis
- Weibull Analysis
- Fault Tree Analysis
- Root Cause Analysis
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