Lean Six Sigma Black Belt Course

Description :

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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. 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