Lean Six Sigma Master Black Belt Online Certification & Training

• 1.0 Introduction to Quality
• 2.0 Quality Leaders (Juran, Deming, Shewhart, Ishikawa) (Videos to Understand)
• 3.0 Cost of Quality (COQ)
• 4.0 Cost of Poor Quality (COPQ) (Videos to Understand)
• 5.0 Optimum Quality Levels
• 6.0 Failure Mode & Effect Analysis (FMEA)
•    6.1 Create Process FMEA (Simulation to Understand)
•    6.2 Create Design FMEA
• 7.0 Key Performance Measures
•    7.1 Key Performance Indictors
•    7.2 Customer Satisfaction
•    7.3 Product Differentiation
•    7.4 Customer Loyalty Metrics
•    7.5 Leading & Lagging Indicators
•    7.6 Create Line of Sight
• 8.0 Key Business Drivers & their Impact
•    8.1 Profit/Margin (Practice to Understand)
•    8.2 Market Share
•    8.3 Net Present Value (NPV)
•    8.4 Cost Benefit Analysis (CBA)
•    8.5 Hard & Soft Benefits (Practice to Understand)
•    8.6 Cost avoidance & Cost reduction (Practice to Understand)
• 9.0 Organisation Goals & Six Sigma
• 10.0 Balanced Score Card& Six Sigma
• 11.0 History & Evolution of Six Sigma
• 12.0 Continuous Improvement
• 13.0 Basics of Six Sigma (Simulation to Understand)
• 14.0 Six SigmaApplications
• 15.0 Types of Six Sigma Projects
•    15.1 DMAIC
•    15.2 DFSS (DMADV/IDOV)
• 16.0 Organization Road Blocks
•    16.1 Organisation Structure & Culture
•    16.2 Common Causes of Six Sigma Failures
•    16.3 Stakeholder Analysis (Six Sigma Impact)
• 17.0 Change Management (Simulation & Videos to Understand)
•    17.1 Basics of Change Management
•    17.2 Readiness Assessment
•    17.3 Communication Plans to Overcome Barriers
• 18.0 Strategic Planning & Deployment
•    18.1 Importance of Lean Six Sigma
•    18.2 Hoshin Kanri (Policy Deployment) (Practice to Understand)
•    18.3 SWOT Analysis (Practice to Understand)
•    18.4 PEST
•    18.5 Business Contingency Planning
• 19.0 Team Management (Simulation & Videos to Understand)
•    19.1 Team Types & Constraints
•    19.2 Team Roles &Responsibilities
•    19.3 Team Member Selection Criteria
•    19.4 Team Success Factors
• 20.0 ProjectTeam Dynamics (Simulation to Understand)
•    20.1 Forming
•    20.2 Storming
•    20.3 Norming
•    20.4 Performing
•    20.5 Team Communication
• 21.0 Team Facilitation
•    21 Motivational Technique
•    22 Team Stages & Development
•    23 Team Communication
•    24 Team Leadership & Models
• 22.0 Team Dynamics (Simulation & Videos to Understand)
•    22.1 Group Behaviour
•    22.2 Meeting Management
•    22.3 Team Decision Making Methods
• 23.0 Team Training (Simulation to Understand)
•    23.1 Need Assessment
•    23.2 Delivery
•    23.3 Evaluation

• 1.0 Voice of Customer & Business(Simulation to Understand)
•    1.1 Collect Customer & Business Voices
•    1.2 Eliminate Vagueness & Ambiguity
•    1.3 VOC Clarity Table
• 2.0 Kano Model (Practice to Understand)
• 3.0 Benchmarking
•    3.1 Competitive
•    3.2 Collaborative
•    3.3 Best Practices
• 4.0 Customer Requirements to Process Requirements
•    4.1 Critical to X (X-Quality, Cost, Safety or any other )
•    4.2 CTQ Drill Down
•    4.3 Quality Function Deployment (Practice to Understand)
• 5.0 Project Section & Prioritisation (Practice to Understand)
• 6.0 Process Owners & Stakeholder Analysis
• 7.0 Project Charter (Practice to Understand)
•    7.1 Business Case
•    7.2 Problem Statement
•    7.3 Project Goal Statement
•    7.4 Project Team
•    7.5 Project Timeline
•    7.6 Project Scope
•    7.7 Expected Benefits
• 8.0 Financial Evaluation & Business Case
• 9.0 Develop Project Metrics
• 10.0 Project Short &Long Terms Gain (Practice to Understand)
• 11.0 Project Risk Analysis
• 12.0 Six Sigma Project Types
• 13.0 ProjectRoles & Responsibilities
•    13.1 Roles of Executive Leadership
•    13.2 Roles of Champion
•    13.3 Roles of Sponsor
•    13.4 Roles of Master Black Belt
•    13.5 Roles of Black Belt
•    13.6 Roles of Green Belt & Team
• 14.0 Project Managements & Analytical tools
•    14.1 Gantt charts
•    14.2 Work Breakdown Structure
•    14.3 Critical Path Method (CPM) (Simulation to Understand)
•    14.4 Project Evaluation & Review Technique
•    14.5 RACI model
•    14.6 Activity Network Diagram
•    14.7 Tree Diagram
•    14.8 Matrix Diagram
• 15.0 Project Scope
• 16.0 SIPOC & Process Mapping (Simulation to Understand)
• 17.0 Project Performance Measurement
•    17.1 Define Performance Measurement
•    17.2 Process Critical Elements
•    17.3 Key Outputs
• 18.0 Project Tool Gate Review

• 1.0 Process Analysis & Documentation
•    1.1 Process Flow Charts
•    1.2 Work Instructions &Gap Analysis
• 2.0 Types of Data & Measurement Scale (Practice to Understand)
•    2.1 Continuous (Variable) Data
•    2.2 Discrete (Attribute) Data
•    2.3 Nominal Data
•    2.4 Ordinal Data
•    2.5 Interval Measurement
•    2.6 Ratio Measurement
• 3.0 Population & Sampling
•    3.1 Basics of Sampling
•    3.2 Calculate Sample size(Practice to Understand)
• 4.0 Type of Samples(Simulation to Understand)
•    4.1 Random Sample
•    4.2 Systematic Sample
•    4.3 Stratified Sample
• 5.0 Basics of Statistics (Simulation to Understand)
•    5.1 Central Tendency
•    5.2 Dispersion
•    5.3 Proportion
• 6.0 Introduction to Statistical Software (Minitab)
•    6.1 Minitab Practice
•    6.2 Descriptive Statistics
•    6.3 Inferential Statistics
• 7.0 Probability
•    7.1 Basic Concepts
•    7.2 Independence Events
•    7.3 Mutually Exclusive Events
•    7.4 Addition & Multiplication Rules
•    7.5 Complimentary Probability
•    7.6 Occurrence of events
• 8.0 Statistical Distributions (Practice to Understand)
•    8.1 Normal
•    8.2 Binominal
•    8.3 Poisson
•    8.4 Chi-Square
•    8.5 Student’s T
•    8.6 F distribution
•    8.7 Hypergeometric
•    8.8 Bivariate
•    8.9 Exponential
•    8.10 Lognormal
•    8.11 Weibull
• 9.0 Probability of Distributions (Practice to Understand)
•    9.1 Frequency Distribution
•    9.2 Cumulative Frequency Distribution
•    9.3 Inverse Cumulative Frequency Distribution
• 10.0 Central Limit Theorem (Simulation to Understand)
• 11.0 Measurement & Data Collection
•    11.1 What is Measurement
•    11.2 Operation Definition
• 12.0 Data Collection Plan (Simulation to Understand)
•    12.1 Check Sheets
•    12.2 Data Coding
•    12.3 Data Cleaning
•    12.4 Data Collection Pitfalls
•    12.5 Avoid Data Collection Pitfalls
•    12.6 Seasonality Effect on Data
•    12.7 Data Collectors Training
• 13.0 Data Mining
• 14.0 Data Preparation
• 15.0 Graphical Analysis (Practice to Understand)
•    15.1 Pareto
•    15.2 Scatter Plot
•    15.3 Box Plot
•    15.4 Histogram
•    15.5 Stem &Leaf Plots
•    15.6 Time Series Plot
•    15.7 Run Chart
•    15.8 Normality (using Minitab)
•    15.9 Graphical Summary
• 16.0 Metrology
•    16.1 Elements of Metrology
•    16.2 Calibration System
•    16.3 Traceability &Reference Standards
•    16.4 Control & Integrity of Standards
• 17.0 Variations& Measurement System Analysis
•    17.1 Understanding Variations (Simulation to Understand)
•    17.2 Measurement System Analysis (MSA)
•       17.2.1 Discrimination
•       17.2.2 Accuracy
•       17.2.3 Precision
•       17.2.4 Stability
•    17.3 GRR for Continuous data (Simulation to Understand)
•    17.4 GRR for Discrete Data (Simulation to Understand)
•    17.5 Destructive Measurement System
•    17.6 Control Charts & Stability (Simulation to Understand)
•       17.6.1 I-Chart
•       17.6.1 I-MR Chart
•       17.6.1 X-Bar R Chart
•       17.6.1 X-Bar S Chart
•       17.6.1 C Chart
•       17.6.1 U Chart
•       17.6.1 NP Chart
•       17.6.1 P Chart
• 18.0 Sporadic problems
• 19.0 Measurement Systems to
•    19.1 Sales & Marketing
•    19.2 Engineering
•    19.3 Supply chain & Management
•    19.4 Research & Development
•    19.5 Customer Satisfaction
• 20.0 Baseline Process Performance (Practice to Understand)
•    20.1 Baseline Discrete Data (DPU, DPO,DPMO)
•    20.2 Baseline Continuous Data (Cp, Cpk, Pp, Ppk, Cpm)
•    20.3 Sigma Value (Short term & Long term)
•    20.4 Sigma Shift (Short term Vs Long term)
• 21.0 Process Capability in Detail (Practice to Understand)
•    21.1 Natural Process Limits & Specification Limits
•    21.2 Design & Conducting Process Capability Studies
•    21.3 Specifications, Sampling Plan, Stability & Normality
•    21.4 Capability for Normal & Non-Normal Data
•    21.5 Process Performance (PPM, DPU, DPMO)
•    21.6 Transformations (Box-Cox & Johnson transformation)
•    21.7 Capability for Discreet Data

• 1.0 Identify Potential Causes (Practice to Understand)
•    1.1 Brain Storming
•    1.2 Affinity Diagram
•    1.3 Cause & Effect Diagram
•    1.4 Five Whys?
•    1.5 Fault tree analysis
• 2.0 Tabular Analysis
• 3.0 Process Analysis
•    3.1 Value Stream Mapping (Recap from Lean)
• 4.0 Data Analysis
• 5.0 NormalCurve & Normality Test(Practice to Understand)
• 6.0 Outlier Analysis
• 7.0 Data Normalisation
• 8.0 Confidence Interval, Risk & P value
• 9.0 Hypothesis Testing -Null & Alternate
•    9.1 Significance of Confidence Level
•    9.2 Significance of Power
•    9.3 Statistical &Practical Significance
•    9.4 Sample Size for Hypothesis Tests
•    9.5 Point & Interval Estimates
•    9.6 Contingency Tables
• 10.0 Alpha & Beta Risks (Practice to Understand)
• 11.0 Hypothesis with Normal Data(Practice to Understand)
•       11.1 One Sample T Test
•       11.2 Two Sample T Test
•       11.3 Paired T Test
•       11.4 One-Way Anova
•       11.5 Test of Variance
•          11.5.1 One Variance Test
•          11.5.2 Two Variance Test
• 12.0 Hypothesis with Non- Normal Data(Practice to Understand)
•       12.1 1 Sample Sign
•       12.2 1 Sample Wilcoxon
•       12.3 Mann – Whitney
•       12.4 Kruskal- Wallis
•       12.5 Mood’s Median
• 13.0 Hypothesis with Discrete Data (Practice to Understand)
•    13.1 1 Proportion
•    13.2 2 Proportions
•    13.3 Chi-Square
• 14.0 Multi Vari Chart (Practice to Understand)
• 15.0 Correlation & its Terminologies (Practice to Understand)
• 16.0 Correlation &Causation (Practice to Understand)
• 17.0 Regression Analysis (Practice to Understand)
• 18.0 Linear & Non-Linear Regression (Practice to Understand)
• 19.0 Simple & Multi-Linear Regression (Practice to Understand)
• 20.0 Residual Analysis (Practice to Understand)
• 21.0 Predicting Modelling using Classification and Regression Tree
• 22.0 Logistic Regression Analysis
• 23.0 Anova
•    23.1 One Way Anova
•    23.1 Two Way Anova
•    23.1 General Linear Model
• 24.0 Chi-Square Test (Practice to Understand)
• 25.0 Multivariate Tools (Practice to Understand)
•    25.1 Factor Analysis
•    25.2 Item Analysis
•    25.3 Discriminant Analysis
•    25.4 Simple & Multiple Correspondence Analysis
• 26.0 Exploratory Data Analysis
• 27.0 Queuing theory
• 28.0 Reliability Theory
• 29.0 Qualitative analysis
• 30.0 Design of Experiments (Practice to Understand)
•    30.1 Need for DOE
• 31.0 Terminologies
•    31.1 Factors, Levels, Response, Treatment
•    31.2 Blocks, Randomisation, Effects & Replication
•    31.3 DOE Plots: Main Effect & Interaction Plots
•    31.4 Confounding
• 32.0 DOE Designs
•    32.1 Full Factorial Experiments (Practice to Understand)
• 33.0 Fractional Factorial (Practice to Understand)
• 34.0 Latin Square Designs
• 35.0 Balanced & Orthogonal Arrays
• 36.0 Taguchi’s Design
• 37.0 Confounding

• 1.0 Generate & Evaluate Ideas (Simulations to Understand)
•    1.1 Brain Storming
•    1.2 SCAMPER
•    1.3 Six Thinking Hats
•    1.4 Benchmarking
•    1.5 doHow
•    1.6 Lean Solutions
•    1.7 TRIZ (Practice to Understand)
• 2.0 Selecting Best Solution(Practice to Understand)
•    2.1 Multi-Voting
•    2.2 Pay-off Matrix
•    2.3 Criteria Matrix
• 3.0 Pugh Matrix
• 4.0 Force Field Analysis
• 5.0 Solution Prioritization matrix
• 6.0 Error Proofing
•    6.1 Prevention & Detection
•    6.2 Mistake Proofing &Examples
• 7.0 Assess Risk FMEA (Recap)
• 8.0 Piloting & Implementation
•    8.1 Pilot Solutions
•    8.2 Pilot Location
•    8.3 Pilot Success Criteria
• 9.0 Implementation
•    9.1 Plan for implementation
•    9.2 Stakeholder Analysis
•    9.3 Communication Plan
•    9.4 Implementation
• 10.0 Change Management
•    10.1 Techniques to gain commitment
•    10.2 Techniques to overcome organizational barriers
•    10.3 Necessary organizational structure for deployment
•    10.4 Communications with management
• 11.0 Organizational culture

• 1.0 What is Control or Sustain?
• 2.0 Types of Control
•    2.1 Process Control
•    2.1 Visual Controls
• 3.0 Different Types of Process controls
• 4.0 Response Plan & Reaction Plan
• 5.0 Automated Process Control
• 6.0 Statistical Process Control (Practice to Understand)
•    6.1 Monitoring, Controlling of Process Performance
•    6.2 Identify & Select Critical Process Parameters
•    6.3 Subgrouping & Rational Subgrouping
•    6.4 SPC- Continuous Data (I-MR, Xbar R, X bar S)
•    6.5 SPC – Discrete Data (C,U,P,NP charts)
• 7.0 Monitoring, Controlling of Process Performance
• 8.0 Identify & Select Critical Process Parameters
• 9.0 Subgrouping & Rational Subgrouping
• 10.0 SPC- Continuous Data (I-MR, Xbar R, X bar S)
• 11.0 SPC – Discrete Data (C, U, P, NP charts)
• 12.0 Uniformly Moving Average Chart
• 13.0 Exponentially Weighted Moving Average
• 14.0 CUSUM chart
• 15.0 Analyse Control Charts
• 16.0 Control Plan
• 17.0 Visual Control
• 18.0 Sustain Improvements
•    18.1 Lesson Learnt
•    18.2 Documentation
•    18.3 Trainings
•    18.4 Ongoing Evaluation
• 19.0 Benefit Computation
• 20.0 Project Closure
•    20.1 Lesson Learnt
•    20.2 Documentation
•    20.3 Trainings
•    20.4 Ongoing Evaluation
• 21.0 Celebration

• 1.0 Common DFSS/ DMADV Methodologies
•    1.1 Define
•    1.2 Measure
•    1.3 Analyze
•    1.4 Design
•    1.5 Validate
• 2.0 Design for X (DFX)
•    2.1 Design Constraints
•    2.2 Design Cost
•    2.3 Design for Manufacturability
•    2.4 Design for Test
•    2.5 Design for Maintainability
• 3.0 Robust Design
•    3.1 Robust Product Design
•    3.2 Tolerance for Design
•    3.3 Statistical Tolerancing
•    3.4 Robust Process Design

• 1.0 Strategies for Implementing Lean Six Sigma
• 2.0 Balance Score Card (Practice to understand)
• 3.0 Pipeline creation of new projects
• 4.0 Develop governance documents, tracking tools,
• 5.0 Project Alignment with a strategic plan
• 6.0. Project alignment with business objectives
• 7.0 Resource planning
• 8.0 Resource development (Train & Mentor)
• 9.0 Belt coaching and mentoring
• 10.0 Project reviews
• 11.0 Team facilitation and meeting management
• 12.0 non-belt coaching and mentoring
• 13.0 Execution
• 14.0 Systems thinking
• 15.0 Organizational culture
• 16.0 Development of Maturity Models
• 17.0 Organizational dynamics
• 18.0 Intervention styles
• 19.0 Interdepartmental conflicts
• 20.0. Leadership and communication
• 21.0 Business Transformation
• 22.0 Business Process Management
• 23.0 RPA Implementation

1.0 Introduction to Artificial Intelligence

   1.1 Overview of AI

      1.1.1 Definition and types of AI (Narrow, General, and Super AI) 

      1.1.2 History and Evolution of AI 

   1.2 Key AI Technologies

      1.2.1 Machine Learning

      1.2.2 Deep Learning

      1.2.3 Natural Language Processing 

      1.2.4 Computer Vision

2.0 Integrating AI with Lean Six Sigma

   2.1 AI in Project Discovery & Definition  

   2.2 AI in Data Collection and Measurement 

   2.3 AI in Data Analysis and Insights 

   2.4 AI in Improvement and Optimization 

   2.5 AI in Identifying Control Mechanism