A Model On Kansei Engineering Methodology
In Japanese publications, different types of Kansei Engineering are identified and applied in various contexts. Schütte examined different types of Kansei Engineering and developed a general model covering the contents of Kansei Engineering.
Choice of Domain ‘Domain’ in this context describes the overall idea behind an assembly of products, i.e. the product type in general. Choosing the domain includes the definition of the intended target group and user type, market-niche and type, and group of the product in question. Choosing and defining the domain is carried out including existing products, concepts and as yet unknown design solution. From this, a domain description is formulated serving as basis for further evaluation. Schütte describes the processes necessary in detail in a couple of publications.
Span the Semantic Space The expression ‘Semantic Space’ was addressed for the first time by Osgood et al.. He posed that every artifact can be described in a certain vector space defined by semantic expressions (words). This is done by collecting a large number of words that describe the domain. Suitable sources are pertinent literature, commercials, manuals, specification list, experts etc. The number of the words gathered typically varies, depending on the product between 100 and 1000 words. In a second step the words are grouped using manual (e.g. Affinity diagram, compare: Bergman and Klefsjö, 1994) or mathematical methods (e.g. factor and/or cluster analysis, compare: Ishihara et al., 1998). Finally a few representing words are selected from this spanning the Semantic Space. These words are called Kansei words or Kansei Engineering words.
Span the Space of Properties The next step is to span the Space of Product Properties, which is similar to the Semantic Space. The Space of Product Properties collects products representing the domain, identifies key features and selects product properties for further evaluation. The collection of products representing the domain is done from different sources such as existing products, customer suggestions, possible technical solutions and design concepts etc. The key features are found using specification lists for the products in question. To select properties for further evaluation, a Pareto-diagram (compare Bergman and Klefsjö, 1994) can assist the decision between important and less important features. Synthesis In the synthesis step, the Semantic Space and the Space of Properties are linked together, as displayed in Figure 3. Compared to other methods in Affective Engineering, Kansei Engineering is the only method that can establish and quantify connections between abstract feelings and technical specifications. For every Kansei word a number of product properties are found, affecting the Kansei word.
Synthesis The research into constructing these links has been a core part of Nagamachi’s work with Kansei Engineering in the last few years. Nowadays, a number of different tools is available. Some of the most common tools are :
- Category Identification
- Regression Analysis /Quantification Theory Type I
- Rough Sets Theory
- Genetic Algorithm
- Fuzzy Sets Theory
Model building and Test of Validity After doing the necessary stages, the final step of validation remains. This is done in order to check if the prediction model is reliable and realistic. However, in case of prediction model failure, it is necessary to update the Space of Properties and the Semantic Space, and consequently refine the model. The process of refinement is difficult due to the shortage of methods. This shows the need of new tools to be integrated. The existing tools can partially be found in the previously mentioned methods for the synthesis. Software Tools for Kansei Engineering Kansei Engineering has always been a statically and mathematically advanced methodology. Most types require good expert knowledge and a reasonable amount of experience to carry out the studies sufficiently. This has also been the major obstacle for a widespread application of Kansei Engineering. In order to facilitate application some software packages have been developed in the recent years, most of them in Japan. There are two different types of software packages available: User consoles and data collection and analysis tools. User consoles are software programs that calculate and propose a product design based on the users' subjective preferences (Kanseis). However, such software requires a database that quantifies the connections between Kanseis and the combination of product attributes. For building such databases, data collection and analysis tools can be used. This part of the paper demonstrates some of the tools. There are many more tools used in companies and universities, which might not be available to the public. User consoles
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