Lean Six Sigma Black Belt Course

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 Global Certification Bodies such as IA*** and A** curriculums.

Instructor is an Accredited Training Associatee.

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.

CBMG LEAN SIX SIGMA BLACK BELT BoK  

• 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

       

• 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 Theorm

     

• 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, Appoximation 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 Marigin 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

       

• Advanced Graphical Methods     

  • Depicting 1 or 2 variables (with example and procedure)   

  • Dot Plot   

  • Box Plot   

  • Interval Plot   

  • Stem-and-Leaf Plot   

  • Time Series & Run Chart   

  • Scatter Plot   

  • Marginal Plot   

  • Line Plots   

  • Depicting 3 variables  (with example and procedure)   

  • Contour Plot   

  • 3D scatter Plot   

  • 3D Surface Plot   

  • Depicting > 3 Variables  (with example and procedure)   

  • Matrix Plot   

  • Multi Vary Chart

     

• 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

     

• 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 Corrrelation - 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

       

• 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

     

• 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