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Industrial Design of Experiments

STAT 321

Winona State University

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Day One – Let’s get started!Course Objectives       outline the basic steps of an industrial

experiment;       design experiments using the

concepts of randomization and blocking;

      design and analyze two level factorial and fractional factorial designs;

      contrast Taguchi's methods with classical methods;

      recognize examples of poor statistical statements and graphics.

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Design of Experiments - Definition:

• A scientific method for designing the collection of information about a phenomenon or process, and then analyzing the information to learn about relations of potentially important variables. Economy and efficiency of data collection have high priorities.

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Advantages of DoE: Process Optimization and Problem Solving

with Least Resources for Most Information.  Allows Decision Making with Defined

Risks.  Customer Requirements --> Process

Specifications by Characterizing Relationships

 Determine effects of variables, interactions, and a math model

 DOE Is a Prevention Tool for Huge Leverage Early in Design

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1. Define the objective of the experiment.2. Choose the right people for the team.3. Identify prior knowledge, then important factors

and responses to be studied.4. Determine the measurement system.5. Design the matrix and data collection

responsibilities for the experiment.6. Conduct the experiment.7. Analyze experiment results and draw

conclusions.8. Verify the findings.9. Report and implement the results.

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No DoE PoorExecution

PoorAnalysis

Why Industrial Experiment Fail

Poor Planning

Poor Design

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Current Industrial Usage

• Industry is training engineers, decision-makers, process owners in quality improvement methods

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Topics in Six Sigma Black Belt training (20 full days):

• Define & Measure Phase - Week 1– Flow chart total process– Create cause & effect diagram– Control chart project metrics– Estimate capability/ performance of project

metrics– Create Pareto charts– Conduct measurement system analysis

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Six Sigma TrainingAnalysis Phase - Week 2

• Create multi-vari charts• Determine confidence intervals for key

metrics• Conduct hypothesis tests ***• Determine variance components• Assess correlation of variables• Conduct regression analysis ***• Conduct analysis of variance

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Six Sigma Training Improvement Phase - Week 3

• Select designed experiment (DoE) factors and levels

• Plan DoE execution

• Conduct DoE

• Implement variability reduction designs & assessments

• Consider response surface methods

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Control Phase - Week 4 Six Sigma Training

• Determine control plan

• Implement control charts

• Consider short run control charts

• Consider CUSUM and moving average control charts

• Consider pre-control

• Mistake-proof processes

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To Receive Six Sigma Black Belt

• 4 weeks of training

• Plus, save your company $100K on an improvement project