Design of Experiments (DOE) with Minitab – 3 day

Overview
Design of Experiments is an off-line quality improvement technique that can be employed to dramatically improve industrial products and processes. Through its use, it is possible to isolate the cause and effect linkages between product/process variables and the resulting output measures of function, quality, cost and performance.

This intensive course provides participants with in-depth understanding of the basic principles of experimental designs. Actual examples are emphasized throughout the presentation.  Participants are taught how to use screening designs (fractional factorials) to identify important factors, along with characterization designs (general factorials and 2^k factorials) for assessment of main effects and interaction. Use of games such as helicopters and unique simulation exercises accelerates the learning process and makes the learning process fun for the engineer.

Knowledge of DOE and the ability to effectively use DOE are basic requirements of modern engineering.  This course equips the engineer to effectively plan, execute, analyze and interpret DOE’s.

This course does not teach participant on just how to point and click. The basic concepts underlying each tool are discussed before the use of the software is demonstrated.

Participants :      Managers and Engineers from all engineering disciplines including Process, Research and Development, Quality, Maintenance and Reliability.

Duration                3 days

Prerequisites     Statistical Process Control / Basic Statistics knowledge

Deliverables      

  • Knowledge of Experimental Design Fundamentals.
  • Ability to perform Product/Process Characterization experiments to understanding main effects and interactions of factors.
  • Identify Key Product and Process Parameters for Improvement.
  • Ability to run experiments for Improved Product/Process Performance Relating to Cost, Quality, and Reliability

Course Outline
How Experimental Design is Useful in Industry

  • Using Experimental Design as part of a Total Quality Solution
  • Complementary Relationship between DOE and SPC
  • Attributes of a Well-Designed Experiment 

Review of Basic Statistics Fundamentals

  • Measures of Location and Spread (Mean, Median, Standard Deviation, Variance)
  • Normal Probability Plot/Normal Distribution
  • Hypothesis Testing
  • Confidence Intervals

Single-Factor Experiments

  • Randomized Experiments
  • Blocking and Nuisance Variables
  • Analysis of Variance
  • Simultaneous Multiple Comparisons
  • Residual Analysis for checking assumptions

Factorial Designs for Characterization

  • General Factorial Design
  • 2^k Factorial Design
  • Main Effects and Interactions: Analysis and Interpretation
  • Blocking in Factorial Designs
  • Unreplicated Full Factorial Designs             

2^k-p Fractional Factorials Designs for Screening

  • Hierarchy of Terms and Alias Structure
  • Resolution III, IV and V designs
  • Fold-over Designs
  • Blocking Unreplicated Full/Fractional Factorials
  • Sequential Experimentation 

DOE Planning

  • 10 Step Planning Guide

Live Experimentation

 

 

Always Keep Improving!