Design of experiments, referred to as DOE, is a systematic approach to understanding how process and product parameters affect response variables such as processability, physical properties, or product performance. Unlike quality, mechanical, or process tools, DOE is a purely mathematical tool used to define the importance of specific processing and/or product variables, and how to control them to optimize the system performance while maximizing properties [1][2].
Invented by Ronald A. Fisher in 1935 in his book “The design of experiments” [3], DOE uses statistical methodology to analyze data and predict product property performance under all possible conditions within the limits selected for the experimental design. Next to understanding how a particular variable affects product performance, interactions between different process and product variables are identified. DOE is a technique or procedure to generate the required information with the minimum amount of experimentation [1]. A DoE approach can greatly improve the efficiency in screening for suitable experimental conditions, for example, for cell culture, nutrient complement, factor level, optimization of a process, or robustness testing [4].
Design of experiments is best used to answer questions such as: “What factor contributes most to the problem?” and “what is the most excellent arrangement of aspect values to reduce dissimilarity in response?” [5].
