Hi @ADouyon,
Just to summarize and complete the excellent answers above :
Your DoE is balanced when for any factors, you have the same number of runs done at high and low levels.
This was the classical way to do DoE (before the rise and progress made on computer calculations), because of :
- The simplicity of the factorial design created : it could be generated by hand, and calculations are also realized without difficulties (by hand or with Excel/calculator),
- Nice orthogonality properties, meaning factors are studied and analyzed independently from each others (no correlations),
- Their practical efficiency with a long history of success stories, which is why people tend to stick to it, because balanced designs are here for a long time, in opposition to custom optimal designs which are more recent.
This "classical" approach of balanced design creation can be found for classical designs like Plackett-Burman screening designs (or Hadamard matrices), full or fractional factorial designs, and response surface designs (Box Behnken or Central Composite Design).
But as stated by @P_Bartell, I wouldn't attach too much importance now to the evaluation of balance when creating a design, particularly in custom designs : the designs calculated for optimal designs are flexible, handle constraints, disallowed combinations and a large combination of different factor types, and are (as the name suggest) optimal for a certain criterion (D, I or A).
So as long as your topic/problem is clear, the factors and ranges well identified, constraints (if any) entered, and model terms entered, you won't do any mistake with an optimal design (from the "Custom Design" platform).
Don't hesitate to generate several designs with different runs number, center points, replicate runs, ... in order to compare them and choose the most appropriate one depending on your goal, expected precision and experimental budget.
Victor GUILLER
L'Oréal Data & Analytics
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)