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Babcock Training Academy

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Babcock Training Academy
Level 10 70 Franklin Street Adelaide  SA  5000

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Lean Six Sigma (LSS) Black Belt Program - Week 3 & 4

Course Description

This 4-week hands-on workshop with Dr Mark Kiemele provides participants with the detailed methodology and tools of Lean Six Sigma to lead their organization toward “World Class” status. The workshop combines the proven and powerful elements of both traditional Lean and traditional Six Sigma into a blended approach. The emphasis is on 1) gaining process and product knowledge with critical thinking 2) reducing variability and non-value added activities and 3) developing leaders in the organization who are focused on enhancing customer value, decreasing defect rates and wasted efforts, reducing cost and cycle time, and generating business growth and breakthrough improvements. The Lean Six Sigma methodology is taught with a “Keep It Simple Statistically” (KISS) approach and makes use of many in-class examples, simulations, and hands-on exercises to ensure that participants have a practical, working knowledge of the tools.

Participants in the Lean Six Sigma Black Belt Training learn the details of the Define, Measure, Analyze, Improve and Control (DMAIC) methodology and tools for achieving better, faster, and lower cost products and processes. We use a knowledge-based approach to “pull” the use of Lean and Six Sigma tools, not push them. Participants learn to select and apply the right tools for a particular problem or question.

As a part of the course, it is expected that participants come with a project to work on so that they can immediately apply their knowledge and realize return on investment on a specific business issue. There is time between the first two weeks of this LSS Black Belt program and the second two weeks (Weeks 3 and 4) to allow the student time to do LSS project work.

Our Lean Six Sigma Black Belt Training uses the “Present/Practice/Apply/Review” strategy. That is, we present tools and methods, give participants the opportunity to practice them in class, and then apply the tools to their project, and finally review the results of the application to the projects.

Target Audience

The intended audience is anyone who desires to become a practitioner of the Lean Six Sigma methodology and tool set and who will be leading Lean Six Sigma projects within an organization.

Participants are expected to have management sponsorship and a project selected prior to attending the training. Projects will be worked on over the duration of the course and should be completed within a reasonable timeframe, typically near the end of the training but no longer than 6 months after the start of the training.

Participants are strongly encouraged to bring a laptop computer to class with Excel 2000(or above) so that they can install the supplied software and practice using it during many in-class exercises. A basic working knowledge of Windows and Excel is helpful. Basic math and algebra skills are also desirable.

Course Outline

Week 3:

Review of DMAIC Tools and Techniques

Additional Practice Using the Tools and Concepts of Lean Six Sigma to Answer Questions and Solve Real World Problems
Workshop / Practice problems

Additional Lean Tools to Define and Measure

Reviewing the Relationship between Lean and Six Sigma Value Stream Mapping (VSM)
Introduction to VSM Software

Analyze Data using Probability Distributions

Basic Concepts of Probability Fact that Probability is Often Not Intuitive
Three Common Distributions and Their Application to Problem Solving Binomial distribution
Poisson distribution Normal distribution How to Transform Non-Normal Data
Using SPC XL for Calculating Probabilities

Regression Modeling to Analyze and Improve

What is Regression and What is it Used For? Terminology Involved in Simple Linear Regression
Intercept Slope Prediction Equation Residual R-Squared
Use of SPC XL for Regression Analysis and Interpretation of Output
Problems with Using Data that is Not from a Designed Experiment

Introduction to the Use of Design of Experiments (DOE) to Analyze and Improve

Purpose of Design of Experiments Key DOE Terminology Experimentation Strategies
Introduction to Basic Graphical and Statistical Analysis of Data Interactions
Introduction to DOE KISS Software and Hands-On Experimentation

Using the Statapult®

DOE 12 Step Process

Two Level Designs

Screening Designs Statapult® Screening Exercise Full and Fractional Factorial Designs
Use and Application of Two Level Designs Transactional DOE Examples
Practice with DOE KISS Software Reasons why Experiments May Fail to Confirm

Three Level Designs

Qualitative vs. Quantitative Factors in DOE Use and Application of Three Level Designs
Screening Designs Full Factorial Designs Box Behnken and Central Composite Designs
Setting Up, Conducting,Analyzing, and Confirming a Quadratic Model Using the Statapult®

Rules of Thumb and DOE Design Selection

Sample Size Guidelines for DOE Selecting the Best Design Determining Statistical Significance
Interpreting R-square, Adjusted R-square, Tolerance and p-Values
Practice Choosing the Correct DOE Design to Use

Week 4:

Quick Review of DMAIC and DOE

Review of the DMAIC Process Mapping Tools to Different Tasks Based on the Type of Data
DOE Rules of Thumb Testing for Relationships between Inputs and Outputs
Use of SimWare software to practice DOE

Additional Lean Improvement Tools

Total Productive Maintenance (TPM) Overall Equipment Effectiveness (OEE) Flow and Pull Kanbans
Inventory Production Sequencing Little's Law

Historical Data Analysis

Review of Orthogonality and its Importance Options for Improving Orthogonality
Problems with Historical Data Strategies for Dealing with Historical Data Historical Data Examples

Additional Hypothesis Tests to Analyze and Improve

Review of Previously Covered Hypothesis Tests Hypothesis Testing with One Sample and Paired Data
Analysis of Variance (ANOVA) for Comparing More than Two Data Sets

DOE Diagnostics and Assessments

Residual Analysis Additional DOE Diagnostics Assessing Lack of Fit using DOE PRO software
Reasons and Potential Solutions for Low R-Squared Values or Lack of Confirmation when Modeling

Advanced DOE Analysis Techniques and Designs

Randomization Techniques and Reasons
Analyzing and Optimizing Multiple Responses using DOE PRO software
Review of Variance Reduction Techniques Robust Design with Exercises
Taking Advantage of Interactions to Reduce Variation

Lean Six Sigma Mini-Project

Capstone Exercise: Hiring Process Simulation
Using the DMAIC Process and Tools to Evaluate the Current State, Collect Baseline Data, Identify
Opportunities for Improvements, and Improve a Simulated Process Measuring Success and Building a Knowledge Notebook

Advanced Black Belt Topics and Training

Sources for Continued Learning Introduction to Design for Six Sigma (DFSS)
Introduction to Discrete-Event Simulation and Using it For Gathering Process Data
Need for High Throughput Testing (HTT) with Software Demonstration


Materials and Text Books

Participants receive the following materials which are integrated and used throughout the class:

Participant Guide


Knowledge-Based Management by Kiemele, Murrow and Pollock (Air Academy Press and Associates)
Basic Statistics: Tools for Continuous Improvement by Kiemele, Schmidt, and Berdine (Air Academy Press and Associates)
Understanding Industrial Designed Experiments by Schmidt and Launsby (Air Academy Press and Associates)
Lean Six Sigma: A Tools Guide by Adams, Kiemele, Pollock, and Quan (Air Academy Press and Associates)


SPC XL DOE Pro SimWare Pro

Course Details

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Please register your interest in this course by contacting Babcock on +61 (0)8 8440 1498

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