DOE - Design Of Expe

riment

The DOE (Design of Experiments) is the More advanced statistical technique for designing and interpreting experimental tests in complex contexts by reducing the number of tests to invest in. DOE aims to reduce the number of tests by obtaining expertise of the system response as one or more parameters change, even simultaneously. It also makes it possible to identify the best combination of factors for the desired result today and to know which levers to touch to achieve the desired result tomorrow.

Course objectives: optimize processes with DOE experimentation.

Course objectives are:

• Consolidate the statistical tools needed to competently perform a DOE
• Design the evidence plan in the appropriate form
• Apply DOE to a practical tool, with our Learning by Doing approach, until the predictive formula is obtained
• Recognize which parameters to model in order to hit the target without conducting new tests

Program: the test plan, effects analysis and predictive formulas

Basic statistical concepts:

• Normal distribution, position and dispersion parameters
• Hypothesis testing
• Confidence intervals
• Correlation
• ANOVA

DOE: theory and practical experimentation sessions

• Design of the sampling plan
• Screening, full DOE and fractional DOE
• Analysis of the effects of factors and their interactions
• Consolidation with Statapult® (business game)

Target audience: R&D, design and quality. All attendance modes

The course has a 2-day in-person duration and will alternate between sessions learning-by-doing to theoretical insights. The course is aimed at Managers, Technicians, and Professionals involved in Asset Management, Process & Product Development, Quality Management, Risk management, and Continuous Improvement.

Register to download the Summary Course Sheet