We run a training program on The Certified Six Sigma Black Belt (CSSBB) and enable participants to become a professional who can explain Six Sigma philosophies and principles, including supporting systems and tools. The participant would be able to demonstrate team leadership, understand team dynamics, and assign team member roles and responsibilities. They will have a thorough understanding of all aspects of the DMAIC model in accordance with Six Sigma principles and will have basic knowledge of lean enterprise concepts, are able to identify nonvalue-added elements and activities, and are able to use specific tools post this training.

1. Lean Six Sigma
Black Belt Training
Program
Sure Shot Way to Become
a Master of Six Sigma!

2. What you'll learn
• Prepare for Lean Six Sigma Black Belt
Certifications
• Solve complex problems as a six sigma
proficient Black Belt
• Perform 'real' data analysis using Minitab

3. Who is this course for:
• Green Belt Certification & Green Belt
Level working knowledge
• Certified or Trained Six Sigma Green
Belts
• The participant should have sufficient
exposure to his/her domain or industry,
It is advisable to have a minimum of 5-8
years of industry work experience

4. Command
a Premium in the
Job market!
Deliver Business Results!

5. Insights
• As per Indeed, a job site's survey, Certified black
belt salaries range between $100,000 - $200,000.
Lean Six Sigma Black Belts command a premium in
Job market. Black Belts deliver business results, so
there are 75% more likely to be promoted that one
without, but with similar domain experience.
• The BoK is based on IASSC and ASQ curriculums.
• Instructor is an Accredited Training Associate.
• Every topic is application based. It starts with a
business scenario and Six Sigma concepts are
introduced subsequently. Data Files, Practices
Files, Templates and Minitab Instructions are
included for all the topics.

6. LEAN SIX SIGMA
BLACK BELT
Learning Curriculum

7. • Black Belt leadership
• Expectations from a Black Belt role in market
• Leadership Qualities
• Organizational Roadblocks & Change Management Techniques
• Mentoring Skills
• Basic Six Sigma Metrics
• CTQ Tree, Big Y , CTX
• Including DPU, DPMO, FTY, RTY, Cycle Time, Takt time
• Sigma scores with XL, Z tables, Minitab
• Target setting techniques
• Role of Benchmarking
• Business Process Management System
• BPMS and its elements
• Benefits of practicing BPMS (Process centricity and silos)
• BPMS Application scenarios
• BSC Vs Six Sigma

8. • MSA
• Performing Variable GRR using ANOVA/X-bar R method
• Precision, P/T , P/TV, Cont %, No. of Distinct Categories
• Crossed & Nested Designs
• Procedure to conduct Continuous MSA
• Performing Discrete GRR using agreement methods for binary and ordinal data
• Agreement & Disagreement Scores for part, operator, standard
• Kappa Scores Computation for ordinal data and criteria for acceptance of gage
• Statistical Techniques
• Probability Curve, Cumulative Probability, Inverse Cumulative Probability
(Example and procedure), Shape, Scale and Location parameters
• Types of Distributions ( Normal, Weibull, Exponential, Binomial, Poission) & their
interpretation and application
• Identifying distributions from data
• Central Limit Theorem - Origin, Standard Error, Relevance to Sampling
• Example & Application of Central Limit Theorem

9. • Sampling Distributions
• Degrees of Freedom
• t-distribution - Origin, relevance, pre-requisites, t-statistic computation
• Chi-square distribution - Origin, relevance, pre-requisites, Chi-square statistic
computation, Approximation to discrete data
• F-distribution - Origin, relevance, pre-requisites, F-Statistic and areas of
applications
• Point & Interval estimates - Confidence and Predictive estimates for Sampling
distributions
• Application of Confidence Estimates in decision making
• Sampling of Estimates
• Continuous and Discrete Sample Size Computation for sampling of estimates
• Impact of Margin of Error, standard deviation, confidence levels, proportion
defective and population on sample size
• Sample Size correction for finite population
• Scenarios to optimize Sample Size such as destructive tests, time constraints

10. • Inferential Statistics
• Advanced Introduction to Hypothesis Tests
• Significance and implications of 1 tail and 2 tail
• Types of Risks - Alpha and Beta Risks
• Significance & computation of test statistic, critical statistic, p-value
• Sample Size for Hypothesis Tests
• Sample Size computation for hypothesis tests
• Power Curve
• Scenarios to optimize Sample Size, Alpha, Beta, Delta such as destructive tests
• Hypothesis Tests
• 1Z, 1t, 2t, Paried t Test - Pre-requisites, Components & interpretations
• One and Two Sample Proportion
• Chi-square Distribution
• Ch-square Test for Significance & Good of Fit - Components & interpretations

11. • ANOVA & GLM
• ANOVA - Pre-requisites, Components & interpretations
• Between and Within Variation, SS, MS, F statistic
• 2-way ANOVA - Pre-requisites, Interpretation of results
• Balanced, unbalanced and Mixed factors models
• GLM - Introduction, Pre-requisites, Components & Interpretations
• Correlation & Regression
• Linear Correlation - Theory and computation of r value
• Non-linear Correlation - Spearson's Rho application and relevance
• Partial Correlation - Computing the impact of two independent
variables
• Regression - Multi-linear Components & interpretations
• Confidence and Prediction Bands, Residual Analysis, Building
Prediction Models
• Regression – Logistic(Logit) & Prediction - Components &
interpretations with example

12. • Dealing with Non-normal data
• Identifying Non-normal data
• Box Cox & Johnson Transformation
• Process Capability
• Process Capability for Normal data
• Within Process Capability, Sub-grouping of data
• Decision Tree for Type of Process Capability Study
• Process Capability of Non-normal data - Weibull, Binomial, Poisson Process
Capability and interpretation of results
• Non Parametric Tests
• Mann-Whitney
• Kruskal-Wallis
• Mood’s Median
• Sample Sign
• Sample Wilcoxon

13. • Experimental Design
• DOE terms, (independent and dependent variables, factors, and levels, response,
treatment, error, etc.)
• Design principles (power and sample size, balance, repetition, replication, order,
efficiency, randomization, blocking, interaction, confounding, resolution, etc.)
• Planning Experiments (Plan, organize and evaluate experiments by determining
the objective, selecting factors, responses and measurement methods, choosing
the appropriate design,
• One-factor experiments (Design and conduct completely randomized,
randomized block and Latin square designs and evaluate their results)
• Two-level fractional factorial experiments (Design, analyze and interpret these
types of experiments and describe how confounding affects their use)
• Full factorial experiments (Design, conduct and analyze full factorial experiments)
• Advanced Control Charts
• X-S chart
• CumSum Chart
• EWMA Chart

14. Connect & Enroll!
Ravi.Jagarlapudi@SaiAcuity.com
(+91) 7989863647
Enabling Learning Through Insight!
14
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