BO 202

BO 202 – JMP Training : Design of Experiments (DOE) – 3 days

Introduction
Design of Experiments (DOE) is an off-line quality improvement methodology that dramatically improves industrial products and processes thus enhancing productivity and reducing costs. Input factors are varied in a planned manner to efficiently optimize output responses of interest with minimal variability.
DOE Flowchart
This course will provide delegates with basic DOE knowledge & techniques that have been specifically designed to deal with common process optimization problems that encountered by engineers in industry. These techniques will be demonstrated by using JMP software with actual industrial data.

Learn how to
Explain the fundamental principles of designed experiments
Generate and analyze full factorial, fractional factorial, split-plot and screening designs, including designs with blocking factors.
Create and analyze classic response surface designs
Use the custom design tool

Training Approach
This practical course combines classroom teaching, practical exercises, and group discussion with actual factory based projects to provide a complete action learning experience. The course has been designed to enable all participants leave the training room with a set of new knowledge, tools, skills and direct experience of how to use JMP software to perform process and quality improvement in a real company setting.

Prerequisite: Statistical Data Analysis Through JMP software & Hypothesis Test, ANOVA & Regression Analysis
Training facilities: Computer installed with JMP Software and LCD projector

Course contents
DAY ONE

Section 1 : Introduction to Design of Experiment (DOE)
What is DOE?
Why do a designed experiment?
DOE vs. One-Variable-At-A-Time
The different stages of quality improvement
Types of experiment
The challenges faced by engineers
Steps for DOE
Practical examples

Section 2 : Full Factorial design and response optimization
Factors vs response
Completely randomized design
Techniques to create factorial design
Coded setting and orthogonal design
Replicating & blocking the design
Tree diagram
Meaning of Main Effects and Interactions
DOE modeling
Un-coding the setting
Cube plot
Split plot
Defining factor constraints
Setup of Response optimizer & optimization plot
Interpretation of results and identify necessary improvement actions
Practical application exercise by using JMP software

DAY TWO
Section 3 : Screening Design
Fractional vs full factorial design
Confounding effect
Design resolution
Folding the design
Center points, & residual plots
Plackett –Burman design
Prediction profiler
Desirability function
Practical application exercise by using JMP software
DOE simulation game

DAY THREE
Section 4 : Response Surface Methodology (RSM)
Basic concepts of RSM
Application of RSM
Shape of responses
Central composite design
Box-Behnken design
Contour profiler with high low limits
Response surface plot analysis
Response optimization
Interpretation of results and identify necessary improvement actions
Custom and augment design
Practical application exercise by using JMP software

Who should attend: Anyone who would like to understand and improve process design, such as engineers, scientists and Six Sigma practitioners.

Delivery: Classroom lecture, hands-on practice, simulation game, assignments and case studies.

Duration: 3 days (9am – 5pm)


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