Analysis of the relationship between product design and customer preferences using generative AI

Abstract

In the field of Kansei Engineering, there are many studies on methods for designing product aesthetics by analyzing the relationship between customers' preferences for products (like/dislike) and their design using methods such as Hayashi's quantification methods, rough set theory and artificial neural networks (ANN). Rough set theory is the method that extracts decision rules that explains the relationships between decision and condition attributes from the information of the objects having multiple attributes. In the field of Kansei Engineering, customer’s preferences are defined as decision attributes, while aesthetic elements that make up a product are defined as condition attributes, and decision rules that explain the relationships between them are obtained from the results of questionnaire on existing products. Since the rough set theory is suitable for analyzing uncertain and incomplete information such as Kansei evaluation, it is used in many design methods. In this research, in order to further improve the usefulness of the rough set theory in Kansei engineering, a method for analyzing the relationship between customer preferences and product design is investigated, a method for analyzing the relationship between customer preference and product design based on generative AI, which has attracted attention in recent years, and rough sets is investigated.