ASQ CSSBB Six Sigma Black Belt Certification PROCESS IMPROVEMENT AND SIX SIGMA
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Six Sigma Black Belt ASQ SSBB Body of Knowledge
The ASQ SSBB exam contains questions from many topics. These are listed in the Body of Knowledge reproduced below. This version of BOK includes links to our Six Sigma Glossary.
 I. ENTERPRISE WIDE DEPLOYMENT II. ORGANIZATIONAL PROCESS MANAGEMENT AND MEASURES III. TEAM MANAGEMENT IV. DEFINE V. MEASURE VI. ANALYZE VII. IMPROVE VIII. CONTROL IX. DESIGN FOR SIX SIGMA FRAMEWORKS AND METHODOLOGIES
[SIX SIGMA GLOSSARY INDEX OF TOPICS]
V. MEASURE (26 questions)

A. Process characteristics

1. Input and output variables
Identify these process variables and evaluate their relationship using SIPOC and other tools. (Evaluate)
2. Process flow metrics
Evaluate process flow and utilization to identify waste and constraints by analyzing work in progress (WIP), work in queue (WIQ), touch time, takt time, cycle time, throughput, etc. (Evaluate)
3. Process analysis tools
Analyze processes by developing and using value stream maps, process maps, flowcharts, procedures, work instructions, spaghetti diagrams, circle diagrams etc. (Analyze)

B. Data Collection

1. Types of Data
Define, classify and evaluate qualitative and quantitative data, continuous (variables) and discrete (attributes) data, and recognize opportunities to convert attributes data to variables measures when appropriate. (Evaluate)
2. Measurement Scales
Define and apply nominal, ordinal, interval and ratio measurement scales. (Apply)
3. Sampling Methods
Define and apply the concepts related to sampling (e.g., representative selection, homogeneity, bias, etc.). Select and use appropriate sampling methods (e.g., random sampling, stratified sampling, systematic sampling, etc.) that ensure the integrity of data (Evaluate)
4. Collecting Data
Develop data collection plans, including consideration of how the data will be collected (e.g. check sheets, data coding techniques, automated data collection etc. ) and how it will be used. (Apply)

C. Measurement Systems

1. Measurement Methods
Define and describe measurement methods for both continuous and discrete data. (Understand)
2. Measurement Systems Analysis
Use various analytical methods (e.g., repeatability and reproducibility (R&R), correlation, bias, linearity, precision to tolerance, percent agreement, etc.) to analyze and interpret measurement systems capability for variable and attributes measurement systems. (Evaluate)
3. Measurement systems in the enterprise
Identify how measuring systems can be applied in marketing, sales, engineering, research and development (R&D), supply chain management, customer satisfaction and other functional areas. (Understand)
4. Metrology
Define and describe elements of metrology, including calibration systems, traceability to reference standards, the control and integrity of standards and measurement devices, etc. (Understand)

D. Basic Statistics

1. Basic Terms
Define and distinguish between population parameters and sample statistics (e.g. proportion, mean, standard deviation, etc.). (Apply)
2. Central limit theorem
Describe and use this theorem and apply the sampling distribution of the mean to inferential statistics for confidence intervals, control charts, etc. (Apply)
3. Descriptive statistics
Calculate and interpret measures of dispersion and central tendency and construct and interpret frequency distributions and cumulative frequency distributions. (Evaluate)
4. Graphical Methods
Construct and interpret diagrams and charts, including box-and-whisker plots, run charts, scatter diagrams, histograms, normal probability plots, etc. (Evaluate)
5. Valid statistical conclusions
Define and distinguish between enumerative (descriptive) and analytic (inferential) statistical studies and evaluate their results to draw valid conclusions (Evaluate)

E. Probability

1. Basic concepts
Describe and apply probability concepts such as independence, mutually exclusive events, multiplication rules, complementary probability, joint occurrence of events, etc. (Apply)
2. Commonly used distributions
Describe, apply and interpret the following distributions: normal, Poisson, binomial, chi square, Student's t and F distributions. (Evaluate)
3. Other distributions
Describe when and how to use the following distributions: hypergeometric, bivariate, exponential, lognormal and Weibull. (Apply)

F. Process Capability

1. Process capability indices
Define, select and calculate Cp, Cpk, and assess process capability. (Evaluate)
2. Process performance indices
Define, select, and calculate Pp, Ppk, and Cpm to assess process performance. (Evaluate)
3. Short-term and long-term capability
Describe and use appropriate assumptions and conventions when only short-term data or attributes data are available and when long-term data are available. Interpret the relationship between long-term and short-term capability. (Evaluate)
4. Process capability for non-normal data
Identify non-normal data and determine when it is appropriate to use Box-Cox or other transformation techniques. (Apply)
5. Process capability for attributes data
Calculate the process capability and process sigma level for attributes data. (Apply)
6. Process capability studies
Describe and apply elements of designing and conducting process capability studies, including identifying characteristics and specifications, developing sampling plans and verifying stability and normality. (Evaluate)
7. Process performance vs. specification
Distinguish between natural process limits and specification limits and calculate process performance metrics such as percent defective, parts per million (PPM), defects per million opportunities (DPMO), defects per unit (DPU), process sigma, rolled throughput yield (RTY), etc. (Evaluate)
 DEFINE ANALYZE

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