Lean Six Sigma - Black Belt

Course subjects

Module 1: Define Phase

• The Basics of Six Sigma

  • Meanings of Six Sigma

  • General History of Six Sigma and Continuous Improvement

  • Deliverables of a Lean Six Sigma Project

  • The Problem Solving Strategy Y = f(x)

  • Voice of the Customer, Business, and Employee

  • Six Sigma Roles and Responsibilities

• The Fundamentals of Six Sigma

  • Defining a Process

  • Critical to Quality Characteristics (CTQs)

  • Cost of Poor Quality (COPQ)

  • Pareto Analysis (80:20 rule)

  • Basic Six Sigma Metrics (DPU, DPMO, FTY, RTY Cycle Time, and deriving these metrics)

• Selecting Lean Six Sigma Projects

  • Building a Business Case and Project Charter

  • Developing Project Metrics

  • Financial Evaluation and Benefits Capture

• The Lean Enterprise

  • Understanding Lean

  • The History of Lean

  • Lean and Six Sigma

  • The Seven Elements of Waste: Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting

• 5S: Sort, Straighten, Shine, Standardise, Self-Discipline

Module 2: Measure Phase

• Process Definition

  • Cause and Effect / Fishbone Diagrams

  • Process Mapping, SIPOC, Value Stream Map

  • X-Y Diagram

  • Failure Modes and Effects Analysis (FMEA)

• Six Sigma Statistics

  • Basic Statistics

  • Descriptive Statistics

  • Normal Distributions and Normality

  • Graphical Analysis

• Measurement System Analysis

  • Precision and Accuracy

  • Bias, Linearity, and Stability

  • Gage Repeatability and Reproducibility

  • Variable and Attribute MSA

• Process Capability

  • Capability Analysis

  • Concept of Stability

  • Attribute and Discrete Capability

  • Monitoring Techniques

Module 3: Analyse Phase

• Patterns of Variation

  • Multi-Vari Analysis

  • Classes of Distributions

• Inferential Statistics

  • Understanding Inference

  • Sampling Techniques and Uses

  • Central Limit Theorem

• Hypothesis Testing

  • General Concepts and Goals of Hypothesis Testing

  • Significance; Practical vs. Statistical

  • Risk; Alpha and Beta

  • Types of Hypothesis Test

• Hypothesis Testing with Normal Data

  • 1 and 2 sample t-tests

  • 1 sample variance

  • One Way ANOVA (Tests of Equal Variance, Normality Testing and Sample Size calculation, performing tests and interpreting results)

• Hypothesis Testing with Non-Normal Data

  • Mann-Whitney

  • Kruskal-Wallis

  • Mood’s Median

  • Friedman

  • 1 Sample Sign

  • 1 Sample Wilcoxon

  • One and Two Sample Proportion

  • Chi-Squared (Contingency Tables)

Module 4: Improve Phase

• Simple Linear Regression

  • Correlation

  • Regression Equations

  • Residuals Analysis

• Multiple Regression Analysis

  • Non-Linear Regression

  • Multiple Linear Regression

  • Confidence and Prediction Intervals

  • Residuals Analysis

  • Data Transformation, Box Cox

• Designed Experiments

  • Experiment Objectives

  • Experimental Methods

  • Experiment Design Consideration

• Full Factorial Experiments

  • 2k Full Factorial Designs

  • Linear and Quadratic Mathematical Models

  • Balanced and Orthogonal Designs

  • Fit, Diagnose Model, and Centre Points

• Fractional Factorial Experiments

  • Designs

  • Confounding Effects

  • Experimental Resolution

Module 5: Control Phase

• Lean Controls

  • Control Methods for 5S

  • Kanban

  • Poka-Yoke (Mistake Proofing)

• Statistical Process Control (SPC)

  • Data Collection for SPC

  • I-MR Chart

  • Xbar-R Chart

  • U Chart

  • P Chart

  • NP Chart

  • Xbar-S Chart

  • CuSum Chart

  • EWMA Chart

  • Control Chart Anatomy

  • Subgroups, Impact of Variation, Frequency of Sampling

  • Centre Line and Control Limit Calculations

• Six Sigma Control Plans

  • Cost Benefit Analysis

  • Elements of the Control Plan

  • Elements of the Response Plan