Lean Six Sigma Black Belt Certification 

Domain 1: Define Phase

The Basics of Six Sigma:

Meanings of Six Sigma

General History of Six Sigma & 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 & 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 (a. including DPU, DPMO, FTY, and RTY Cycle Time; deriving these metrics)

 

Selecting Lean Six Sigma Projects:

Building a Business Case & Project Charter

Developing Project Metrics

Financial Evaluation & Benefits Capture

 

The Lean Enterprise:

Understanding Lean

The History of Lean

Lean & Six Sigma

The Seven Elements of Waste(a. Overproduction, Correction, Inventory, Motion, Overprocessing, Conveyance, Waiting.)

5S (a. Sort, Straighten, Shine, Standardize, Self-Discipline)

 

Domain 2: Measure Phase

Process Definition:

Cause & Effect / Fishbone Diagrams

Process Mapping, SIPOC, Value Stream Map

X-Y Diagram

Failure Modes & Effects Analysis (FMEA)

 

Six Sigma Statistics:

Basic Statistics

Descriptive Statistics

Normal Distributions & Normality

Graphical Analysis

 

Measurement System Analysis:

Precision & Accuracy

Bias, Linearity & Stability

Gage Repeatability & Reproducibility

Variable & Attribute MSA

 

Process Capability:

Capability Analysis

Concept of Stability

Attribute & Discrete Capability

Monitoring Techniques

 

Domain 3: Analyze Phase

Patterns of Variation:

Multi-Vari Analysis

Classes of Distributions

 

Inferential Statistics:

Understanding Inference

Sampling Techniques & Uses

Central Limit Theorem

 

Hypothesis Testing:

General Concepts & Goals of Hypothesis Testing

Significance; Practical vs. Statistical

Risk; Alpha & Beta

Types of Hypothesis Test

 

Hypothesis Testing with Normal Data:

1 & 2 sample t-tests

1 sample variance

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

 

Domain 4: Improve Phase

Simple Linear Regression:

Correlation

Regression Equations

Residuals Analysis

 

Multiple Regression Analysis:

Non-Linear Regression

Multiple Linear Regression

Confidence & Prediction Intervals

Residuals Analysis

Data Transformation, Box Cox

 

Designed Experiments:

Experiment Objectives

Experimental Methods

Experiment Design Considerations

 

Full Factorial Experiments:

2k Full Factorial Designs

Linear & Quadratic Mathematical Models

Balanced & Orthogonal Designs

Fit, Diagnose Model, and Center Points

 

Fractional Factorial Experiments:

Designs

Confounding Effects

Experimental Resolution

 

Domain 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 Methods

Control Chart Anatomy

Subgroups, Impact of Variation, Frequency of Sampling

Center Line & Control Limit Calculations

 

Six Sigma Control Plans:

Cost Benefit Analysis

Elements of the Control Plan

Elements of the Response Plan