In the initial stages of development of a new consumer product, a team is generally faced with many options and it is difficult to screen without the risk of missing opportunities to find preferred consumer solutions. When making crucial decisions regarding the next steps to take in product development, it is important to identify suitable statistical tools. Such tools make it possible to combine scientific expertise with data analytics capability and to support the understanding of each factor's relevance in providing an initial indication of possible synergistic effects among different factors. This can generally be achieved with relatively simple techniques, such as factorial designs, and analysis of the results through ANOVA (ANalysis Of VAriance).
This chapter is a guide for developers to properly organize a factorial experimental design through proper randomization, blocking, and replication (Anderson, M. J. and Whitcomb, P. J., 2015). It also provides suggestions on how to reduce variability and analyze data to achieve the best outcome.
A concrete example of product development is provided, looking at an air freshener and consumer preferences regarding constituent oils.
Specifically, this chapter deals with the following:
|Introduction to DOE (Design Of Experiments) and guidelines for planning and conducting experiments
|Factors, levels, and responses
|Screening experiments and factorial designs ...