Duration
7 days
Overview
An important component to ensure success of Six Sigma Quality Implementation is to have team members who can support the work of the Black Belts. Green Belts are people work directly with the black belts but do not have the same level of training or responsibility to the project as the black belts. Given the nature of the tasks Green Belts most often execute, these individuals should receive training which focus on the Define, Measure, and Analyze phases of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control).
Contents
- Practical application of statistical concepts learned is afforded through case studies, hands on exercises & assigned projects. This will need minimum 3 days training on Statistics.
- Application of DMAIC (Define, Measure, Analyze, Improve, Control) and data collection to support the selection and completion of a project. This needs 1 day training.
- Selected Training such as:
- Design of Experiments
- Variance Analysis & ANOVA.
- SPC.
Note : The above list of training can be tailored to meet your needs and also modified the necessary training. Please let us know the needed module and we will provide the necessary details for your further understanding an review.
Detailed contents
Define Phase
Day 1 (Fuad/Sim Lam Thong)
Introduction of Six Sigma roadmap
Project selection guidelines
Determine project scope and boundaries
Must define problem statement
Projected annualized savings
Project metrics, Baseline and Entitlement (target saving per project)
Project deliverables
Introduction to Statistic Software, Minitab
Cause & Effect Analysis
Cause and Effect Matrix
Fishbone Diagram
Basics Minitab practice session
Measure Phase
Day 2 (Sabrina Lim)
Failure Mode Effect Analysis (FMEA)
Brief explanation on FMEA principle and techniques
Summarize the definitions, concepts, application options and relationships with other tools
Basic statistics
Describe mean, median, quartiles, mode, standard deviation, range from the data analysis
Define variability in a process
Work with variability (graphically and numerically)
Normality Test
Check data normal or non normal
Normal Probability Plots
Measurement System Analysis
Understand that Measurement is a Process
Compare Measurement System Variability to Process Variability
Identify and quantify the sources of Measurement Variability
Discover opportunities for Measurement System and Total Process Variability improvement
Day 3 (Sabrina Lim)
Process Capability Study
Understand the need for Process Capability study
Quantify Process Capability (long and short term)
Analyze Phase
Overview and Brief introduction on Probability Distribution (type and plotting with Minitab)
Basic nature of data
Discrete Probability Distributions
Discrete Uniform Distribution
Binomial Distribution
Continuous Probability Distributions
Continuous Uniform Distribution
Normal or Gaussian Distribution
Exponential Distribution
Sample size selection
Interval of Estimation
Central Limit Theorem
Hypotheses testing (Variables & Discrete)
Apply various tests such as 1 sample Z test, 1 sample T test, 2 samples T test, 1 proportion Test, 2 proportions Test to compare different populations.
ANOVA (Analysis of Variance)
What is ANOVA?
What is the ‘Sum of Squares’?
Calculate the test statistic
Epsilon Squared
ANOVA by example
Contingency Table
Day 4 (Sharon Wong)
Correlations & Regression
Correlation
Create Scatter Plots
Perform simple Linear Regression
Coefficient of Correlation (R, R2 and adjusted R2)
Regression diagnostics
Improve Phase
Introduction to DOE (Design of Experiment)
What is DOE
To optimize the process variables
DOE Planning
Full Factorial
Describe a full factorial experiment
Define what it meant by factors & levels and explain the notation used for a full factorial experiment design
Explain main effects and interactions
Set up and Analyze full factorial experiment using Minitab
2K Factorial Design
Why do we need to use 2k factorial experiments?
2k Vocabulary & Terminology
Steps for DOE Analysis
2k Standard Order Designs
Calculating main effects
Calculating interactions
Adding Center Points
Adding Blocking
2k Example and Exercise (with Minitab and other related software)
Control Phase
Day 5 (Sharon Wong)
Introduction to Control
Introduction to process monitors and controls
Introduction to current management tools and philosophies
Determine the effects of the informal system
SPC 1 (Variable Charts)
Introduction to SPC
Why we need Statistical Process Control
Implementation and Theory
Control Charts for continuous data
X-bar and R charts
Subgrouping and frequency
Control Chart interpretation
Reaction to Out-of-Control Situations
Other Shewhart charts for continuous data
Control Charts for discrete data
P, NP, C, and U charts
Day 6 (Sharon Wong)
SPC 2 (Attribute Charts)
Attribute Terminology
Discrete or “Attribute” types of data
Defect and Defective
Error proofing
Explain the value of error proofing
Describe situations where error proofing is needed
Implement error- proofed systems
Maintaining the Gains
Understand why 5S Workplace Organization is a key factor in maintaining success
Learn techniques for building organizational memory to make improvements permanent
Day 7 (Trainer to be decided)
Revision
Who Should Attend
This workshop is designed for six sigma improvement team members, supervisors and other executives who will be conducting Six Sigma projects in the lower complexity than the Black Belt projects.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.