Mitsuo Nagamachi

Kansei Engineering: A new ergonomic consumer-oriented technology for product development

Abstract Kansei Engineering was developed as a consumer-oriented technology for new product development. It is defined as “translating technology of a consumers feeling and image for a product into design elements”. Kansei Engineering (KE) technology is classified into three types, KE Type I, II, and III. KE Type I is a category classification on the new product toward the design elements. Type II utilizes the current computer technologies such as Expert System, Neural Network Model and Genetic Algorithm. Type III is a model using a mathematical structure. Kansei Engineering has permeated Japanese industries, including automotive, electrical appliance, construction, clothing and so forth. The successful companies using Kansei Engineering benefited from good sales regarding the new consumer-oriented products. Relevance to industry Kansei Engineering is utilized in the automotive, electrical appliance, construction, clothing and other industries. This paper provides help to potential product designers in these industries.

Kansei engineering as a powerful consumer-oriented technology for product development

Kansei engineering was founded 30 years ago, as an ergonomics and consumer-oriented technology for producing a new product. When a consumer wants to buy something, he/she will have a kind of feeling and image (kansei in Japanese) in his/her mind. If the consumers feeling could be implemented in the new product, he/she would be more satisfied with the product. Kansei engineering aims at translation of kansei into the product design field including product mechanical function. This is why it is called the consumer-oriented aspect. There are many products in Japan which have applied kansei engineering. Recently, it has also been applied to construction products as well as to community design.

Concepts, methods and tools in Kansei engineering

Trends in product development today indicate that customers will find it hard to distinguish between many products due to functional equivalency. Customers will, therefore, base their decisions on more subjective factors. Moreover, in the future, products will consist, to a higher grade, of a combination of a tangible and intangible part. Kansei Engineering is a tool translating customers feelings into concrete product parameters and provides support for future product design. Presently, a total of six different types of Kansei Engineering are in use. The aim of this paper is to propose a framework in Kansei Engineering to facilitate the understanding of the different types of Kansei Engineering and to open Kansei Engineering for the integration of new tools. The new structure includes the choice of a product domain, which can be described from a physical and a semantic perspective as building a vector space in each. For the latter mentioned space, the Semantic Differential Method is used. In the next step, the two spaces are merged and a prediction model is built, connecting the Semantic Space and the Space of Product Properties together. The resulting prediction model has to be validated using different types of post-hoc tests.

Hybrid Kansei engineering system and design support

Abstract In this paper, we propose the new framework of Kansei Engineering System (KES), called Hybrid KES, which can support both consumer and designer as the decision support system (DSS). Kansei Engineering is defined as “translating technology of a consumers feeling and image for a product into design elements” ( Nagamachi, 1989 ). It is the Kansei Engineering System which we developed to utilize on the product development process using Kansei Engineering techniques. There are two types of KES, one for the consumers decision supporting system called the Forward Kansei Engineering System, and another for the designer supporting system called the Backward Kansei Engineering System. The combined computerized system of the Forward KES and Backward KES must be powerful supporting tools for both users. This paper introduces the structure of the combined system, and proposes the Hybrid KES as the new general framework of Kansei Engineering System. Relevance to industry Hybrid KES consists of Forward KES and Backward KES. The former is the KES in which a designer obtains the demanded design through an input of the Kansei word. In the Backward KES, the designer is able to draw a rough sketch in the computer and the computer system recognizes the pattern of the design inputted by the designer. The combined computerized system of the Forward KES and Backward KES must be powerful supporting tools for both consumer and designer.

Kansei engineering: A study on perception of vehicle interior image

The ‘Kansei’ Engineering method was applied to evaluate vehicle interior image, especially roominess and oppressiveness. A diagnostic system for comfortableness using design elements and dimensions was developed and introduced. Relevance to industry Kansei Engineering is utilized in automotive interior design and evaluation. The analytic method and diagnostic system provide help to the designers and evaluation panelists.

Requisites and practices of participatory ergonomics

Participatory ergonomics is a very powerful technology of ergonomics for realizing workers welfare in the workplace. Successful participatory ergonomics has its requisites and principles. The concern of this paper is to make clear the requisites of participatory ergonomics such as participation, organization, ergonomic tools and job design concept, and the successful practices based on their applications.

An analysis of Kansei structure on shoes using self-organizing neural networks

Kansei engineering is a technology for translating human feelings into product design. Several multivariate analyses are used for analyzing human feelings and building rules. Although these methods are reliable, they require large computing resources. It is difficult for general users to deal with many variables because of small personal computers, and the need for the user to be an expert on statistics. This paper presents an automatic semantic structure analyzer and Kansei expert systems builder using self-organizing neural networks, ART1.5-SSS and PCAnet. ART1.5-SSS is our modified version of ART1.5, a variant of the Adaptive Resonance Theory neural network. It is used as a stable non-hierarchical classifier and a feature extractor, in a small sample size condition. PCAnet performs principal component analysis based on generalized Hebbian algorithm by Sanger (1989). These networks enable quick and automatic rule building in Kansei engineering expert systems. AKSYONN4 system is the automatic builder for Kansei engineering expert systems because it uses self-organizing neural networks. The system enables ‘real-world’ applications of Kansei engineering in product development. Relevance to industry An automatic analysis of human feelings on products and automatic building of Kansei engineering expert systems can increase the prospects of applying Kansei engineering to acceptable product design. Neural networks-based analysis and automatic expert system building enable the on-site analyzing.

Rule-based inference model for the Kansei Engineering System

Abstract Kansei Engineering has been applied to product development for customer satisfaction based on ergonomic technology. The system is composed of three parts such as Kansei analysis, inference mechanism, and presentation technologies. The inference mechanism by which human Kansei is translated into design elements plays an important role in Kansei Engineering. The reasoning logic in the system must satisfy several conditions. First, the whole aspects of design elements must be considered in the reasoning processes. Second, the reasoning logic can make the discrimination between Kansei words selected by customers and other words. Third, the reasoned results must have reliability high enough to reduce the difference between customers image and reasoned design elements. In this paper, we propose a rule-based inference model which will cover the above-mentioned conditions. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected Kansei words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customers feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model. Relevance to industry Rule-based inference model in useful in product design development having complex relationship with human Kansei of any industry. This model can also be made available to evaluation method for the analysis of reasoned results.

An image technology expert system and its application to design consultation

Our concern is to construct an expert system that is able to deal with human image or feeling and to translate them into representations of real design elements. The Image Technology Expert System (ITES) consists of a system controller, a knowledge base, a working memory, a graphic module, and three databases. One of these databases, the Image Technology database, maintains an ergonomic knowledge concerning human image. The system is able to display a picture on a CRT fitting the human image which is decided by an inference engine of the expert system.

A fuzzy rule induction method using genetic algorithm

Abstract Kansei engineering expert systems simulate human perception for the evaluation of product design. A procedure of inducing a fuzzy decision tree for the Kansei engineering system is described for the analysis of driving comfort of automobiles. A method is proposed in this study for inducing the tree based on a genetic algorithm. Linguistic fuzzy rules are acquired by tracing the generated tree from the root node to leaf ones. The results are compared with the model of quantification theory type I which is one of the conventional statistical methods.