As Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) methodology has spread across diverse industries, there has been much discussion about what the body of knowledge should contain for Black Belts. It is common to see different “flavors” of Black Belt certifications created within the same company:  transactional Black Belts, manufacturing Black Belts, etc. Each flavor typically has its own training sessions and separate body of knowledge for addressing specific areas within the company. In these environments, Black Belts can become overly specialized if they are not exposed to scenarios outside of their particular area of expertise; they may not experience how transactional, operational, logistical, and other systems interact and impact each other. They may not see how a particular tool that is not featured in one area may have a great impact when applied to a different area. Worse yet, training for transactional environments often gets watered down, with technical content being replaced by less intimidating soft skills and high-level philosophical discussions.

Click here to read the previously published “Six Sigma Black Belt and Body of Knowledge” on iSixSigma.com.

Black Belts should be Six Sigma experts – capable of leading a team of process experts in applying the methodology and corresponding tools to any process in any industry. Such Black Belts should also be able to confidently answer the numerous technical and statistical questions that Green Belts, Champions and management will ask in any Six Sigma effort. In order to accomplish a return to this traditional and ideal Black Belt, there needs to be a universal, highly technical body of knowledge within Six Sigma training for that level.

The updated Black Belt body of knowledge  represents the baseline topics that should be taught in a standard four-week training program. There is a fair amount of technical proficiency that is required as Black Belts should have a great depth of understanding of the tools, and also have the confidence and ability to address the most difficult challenges in any department or industry.

The argument is often made that teaching technical statistical tools such as advanced DOE or regression methods would not be useful for Black Belts involved in transactional or service organizations because such tools are not used in those types of industries. Of course, this is a self-fulfilling prophecy. The reason that many advanced tools are not utilized is because people in those industries are not taught how to use them. There are countless articles and examples throughout the Six Sigma community detailing great successes in applying advanced tools to non-manufacturing environments. A capable Black Belt should have these methods at their disposal.

This Black Belt body of knowledge does not contain dedicated modules to such soft skills as change management, leadership training, project management, etc. This does not mean that these skills are not important – even critical – to being an effective Black Belt. No matter how much technical knowledge Black Belts may have, they are completely ineffective if they cannot lead people and instill changes in the work areas. However, these are general management and leadership skill sets and not unique to Six Sigma. In the limited training hours for a Black Belt program, candidates are better served by gaining the technical knowledge; additional training courses (facilitation, change management, etc.) can be provided through other leadership programs.

Black Belt training should focus on imparting the technical skills that will allow Black Belts to be the sources of Six Sigma expertise across all business units, plants and industries. The body of knowledge for this training as shown here is rigorous. A properly chosen Black Belt candidate will embrace this depth and, as a result, will be well-equipped to develop innovative approaches and solutions to the types of difficult problems for which Six Sigma was intended.

Black Belt Body of Knowledge
Define
  • Introduction to Six Sigma
    • History of Six Sigma
    • Need for Six Sigma
    • Six Sigma metrics
    • DMAIC (Define, Measure, Analyze, Improve, Control) methodology overvie
    • Examples of Six Sigma results
  • Voice of the customer (VOC)
  • CTx (quality, time, cost)
    • Converting VOC to CTQs (critical to quality)
  • SIPOC (supplier, input, process, output, customer)
  • Pareto analysis
  • Project charter
    • Business opportunity
    • Problem statement
    • Objective
    • Primary and secondary metrics
    • Scope
    • Cost of poor quality (COPQ)
    • Project teams
  • Stakeholder analysis
Measure
  • Process mapping
  • Fishbone diagram
  • Graphical tools
    • Histogram
    • Dotplot
    • Boxplot
    • Scatterplot
    • Time series plot
    • Pareto chart
  • Basic statistics and probability
    • Types of data
    • Accuracy versus precision
    • Mean, median, mode
    • Range, interquartile range, variance, standard deviation
    • Sample versus population
    • Percentiles
    • Central limit theorem
    • Confidence intervals
  • Process distributions
    • Normal distribution
    • Exponential, Weibull, lognormal
    • Binomial, Poisson
  • Lean concepts
    • Value stream, flow
    • Batch versus single-piece flow
    • Seven forms of waste
    • Push versus pull systems
    • Kanbans, work cells
    • Supply chain, just-in-time
    • 5S (sort, straighten, shine, standardize, sustain) and visual management
    • Standard work
    • OEE (overall equipment effectiveness)
  • Sampling and data collection
    • Sampling bias
    • Sampling techniques:  random, stratified random, systematic, rational subgrouping
    • Power and sample size calculations
  • Process capability
    • Process stability
    • Normal capability analysis (Cp, Cpk, Cpm, Pp, Ppk)
    • Non-normal capability analysis
    • Binomial and Poisson capability analysis
    • Rolled throughput yield (RTY), defect per unit (DPU), defects per million opportunities (DPMO), Sigma level (including shift)
  • Measurement system analysis
    • Variable gage R&R
    • Destructive testing
    • Crossed versus nested designs
    • Attribute gage R&R
Analyze
  • Failure mode and effects analysis (FMEA)
  • Multi-vari analysis
  • Inferential probability distributions
    • Normal
    • Chi-square
    • True, False
    • Binomial, Poisson
  • Hypothesis testing
    • Anderson-Darling normality test
    • One-sample t-test
    • Two-sample t-test
    • Paired t-test
    • One-way analysis of variance (ANOVA)
    • One-sample test for variation
    • Two-sample test for variation
    • Test for equal variance
    • One-sample sign
    • Mood’s median test
    • One-proportion test
    • Two-proportion test
    • Chi-squared contingency table
    • One-sample Poisson rate
    • Two-sample Poisson rate
  • General ANOVA
  • Correlation and regression
  • Multiple regression
  • Binary logistic regression
  • Design of experiments (DOE) strategies
  • 2k full factorial DOE
  • DOE center points, blocking, covariates
  • 2k fractional factorial DOE
  • General full factorial DOE
  • Central composite design
Improve
  • Innovative solutions (brainstorming, etc.)
  • Selecting a solution (Pugh matrix)
  • DOE multiple response optimization
  • Response surface methodology
  • Evolutionary operation (EVOP)
  • Lean tools
    • Lean measures of time:  lead time, takt time, completion time, cycle time
    • Value stream mapping
    • Time value mapping
    • Theory of constraints
    • Load charts/line balancing
    • Spaghetti chart
  • Queuing theory
  • Improve techniques
    • Self-inspection
    • Training
    • Checklist
    • Process simplification
    • Mistake proofing
  • Implementation and verification (piloting, etc.)
Control
  • Statistical process control
    • I-MR charts
    • Xbar-R charts
    • Xbar-S charts
    • P-charts
    • C-charts
    • U-charts
  • Control plans
    • What, who, where, how often, how much
    • Decision criteria
  • Action plan
  • Management engagement and handoff
  • Project closure