Table 5.

Selection of DOE tools in analytical quality by design.

Design	Number of variables and usage	Advantage	Disadvantage
Full factorial design	Optimization/2–5 variables	Identifying the main and interaction effect without any confounding	Experimental runs increase with increase in number of variables
Fractional factorial design or Taguchi methods	Optimization/and screening variables	Requiring lower number of experimental runs	Resolving confounding effects of interactions is a difficult job
Plackett-Burman method	Screening/or identifying vital few factors from large number of variables	Requiring very few runs for large number of variables	It does not reveal interaction effect
Pseudo-Monte Carlo sampling (pseudorandom sampling) method	Quantitative risk analysis/optimization	Behavior and changes to the model can be investigated with great ease and speed. This is preferred where exact calculation is possible	For nonconvex design spaces, this method of sampling can be more difficult to employ. Random numbers that can be produced from a random number generating algorithm
Full factorial design	Optimization/2–5 variables	Identifying the main and interaction effect without any confounding	Experimental runs increase with increase in number of variables