Table 1.

Overview of the Design of Experiments (DoE) techniques.

Techniques	Overview	Methodology	Benefits	Ref
Factorial designs	All factors are assessed as all possible combinations of ‘high’ and ‘low’ levels. Fractional factorial designs can be used to reduce the number of experimental runs.	Usually involve two or more factors assessed at two levels.	Useful for determining the main effects in screening experiments; Straight-forward to design; Robust.	[29]
Latin square	Ideally used for experiments in which it is possible to test subjects individually under every treatment.	Number of experimental conditions is required to equal the number of different labels	High control of the variation from the different experimental runs and labels Better efficiency compared to other techniques.	[34,36]
Taguchi designs	Determination of the best combination of inputs to produce a design or a product.	Determines parameter levels.	Identifies the right input; High-quality product; Robust design perspective.	[30,37]
Response Surface Methodology (RSM)	An offline optimisation method, which usually involves studying two factors. However, this technique can be used to study three or more factors. The method is usually employed in optimisation experiments.	RSM merges mathematical and statistical methods with experimental designs, to develop models that relate to the response and control factors.	Represents relationship between the responses and control factors; Allows response values to be predicted using a range of control factors; Provides optimum values for control variables; Uses statistical testing to determine a significant control variable.	[37,38]