DOE - Design Of Expe

riment

DOE (Design of Experiment) is the most advanced statistical technique for designing and interpreting experimental tests in complex settings by reducing the number of tests to invest in.

DOE aims to reduce the number of trials 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.

DOE gives effective and efficient responses in such contentions as:

• The identification of the best configuration of adjustments on numerically controlled machines,
• modeling of machine settings related to exogenous variabilities (supply, environmental conditions, new higher product standards...)
• the settings of competing factors
• The search for the correct dosages of components of chemical or food formulations

The DOE course aims to take participants step by step in discovering this advanced statistical technique and fully understanding its potential.

OBJECTIVES

Participants in this course will learn how to:

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

TOPICS COVERED

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)

Course dates

The course has a 2-day in-person duration and will alternate between sessions learning-by-doing To theoretical insights.

DOE (Design of Experiment) | 1st Ed. 2024
July 5 and 6, 2024

Register to download Course Schedule