Relationship between statistical methods and design, through Kansei engineering.

Abstract

Both the disciplines of design and statistics have promoted projects and research with clear objectives in their field, but for the other discipline, they have been difficult or challenging to fully understand. In design, there are a large number of projects that provoke a reaction in spectators or users as they have a spectacular scope and impact, but at a statistical level, their results add little value. On the other hand, in some of the models and applications that are often used in statistics, the requirements are highly complex and numerous. This makes it difficult to put theory into practice since experiences or experiments that are so complex and difficult to manage cannot be carried out. In addition, the subsequent reporting process for non-experts is difficult to understand due to a large amount of information as well as on poorly designed presentations at times. After understanding the limitations that the two disciplines face, their ability to work together and turn one another’s weaknesses into a more complete and holistic solution is evident. Kansei engineering is also a good example since it is a complex design tool, and the only way to advance it incorporating the use of data is through collaboration between designers and statisticians. In this paper, the Data Collection Toolkit is presented as a result of applying Kansei Engineering to unite these two disciplines including some methodologies, resources, and tools for designers for each step.