Kansei Engineering
Encyclopedia
Kansei Engineering is a method for translating feelings and impressions into product parameters, invented in the 1970s by Professor Mitsuo Nagamachi (Dean of Hiroshima International University). Kansei Engineering can "measure" the feelings and shows the relationship to certain product properties. In consequence, products can be designed to bring forward the intended feeling.
It has now been adopted as one of the topics for professional development
by the Royal Statistical Society
.
However, success in a certain market segment does not only require knowledge about the competitors and their products' performance, but also about the impressions the products make on the customer. The latter requirement becomes much more important the more mature the products and the companies are. This means that the customer purchases a product based on more subjective terms such as manufacturer image, brand image, reputation, design, impression etc., although the products seem to be equal. A large number of manufacturers have started development activities to consider such subjective properties so that the product expresses the company image.
This demand triggers the introduction of a new research field dealing with the collection of customers’ hidden subjective needs and their translation into concrete products. Research is done foremost in Asia, namely Japan and Korea. In Europe a network has been forged under the 6th EU framework (compare www.engage-design.org). This network refers to the new research field as “emotional design” or “affective engineering”.
Methods in Affective Engineering
The area of integrating affective values in artifacts is not new at all. Already in the 18th century philosophers such as Baumgarten and Kant established the area of aesthetics. In addition to pure practical values, artifacts always also had an affective component . One example is jewellery found in excavations from the stone ages. Also the period of renaissance is a good example of that.
In the middle of the 19th century, the idea of aesthetics was deployed in scientific contexts. Charles E Osgood developed his Semantic Differentials Method in which he quantified the peoples’ perceptions of artifacts [4]. Some years later, in 1960, Professors Shigeru Mizuno and Yoji Akao developed an engineering approach in order to connect peoples’ needs to product properties. This method was called Quality Function Deployment (QFD). Another method, the Kano model was developed in the field of quality in the early 1980s by Professor Noriaki Kano, of Tokyo University. Kano’s model is used to establish the importance of individual product features for the customer’s satisfaction and hence it creates the optimal requirement for process oriented product development activities. A pure marketing technique is Conjoint Analysis. Conjoint analysis estimates the relative importance of a product’s attributes by analyzing the consumer’s overall judgment of a product or service. A more artistic method is called Semantic description of environments. It is mainly a tool for examining how a single person or a group of persons experience a certain (architectural) environment.
Although all of these methods are concerned with subjective impact, none of them can translate this impact to design parameters sufficiently. This can, however, be accomplished by Kansei Engineering. Kansei Engineering (KE) has been used as a tool for affective engineering. It was developed in the early 70s in Japan and is now widely spread among Japanese companies. In the middle of the 90s, the method spread to the United States, but cultural differences may have prevented the method to enfold its whole potential.
According to Nagasawa, one of the forerunners of Kansei Engineering, there are three focal points in the method:
• How to accurately understand consumer Kansei
• How to reflect and translate Kansei understanding into product design
• How to create a system and organization for Kansei orientated design
The following figure shows how Kansei Engineering works in principle.
Figure 1: Kansei Engineering System (KES).
Figure 2: A general model on Kansei Engineering [13].
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
Another Software package (Kn6) was devleoped at the technical University of Valencia in Spain.
Both software packages improve the collection and evalutation of Kansei data. In this way even users with no specaialist compentence in advanced statistics can use Kansei Engineering
It has now been adopted as one of the topics for professional development
Professional development
Professional development refers to skills and knowledge attained for both personal development and career advancement. Professional development encompasses all types of facilitated learning opportunities, ranging from college degrees to formal coursework, conferences and informal learning...
by the Royal Statistical Society
Royal Statistical Society
The Royal Statistical Society is a learned society for statistics and a professional body for statisticians in the UK.-History:It was founded in 1834 as the Statistical Society of London , though a perhaps unrelated London Statistical Society was in existence at least as early as 1824...
.
Introduction
The design of products on today’s markets often become increasingly complex since they contain more functions and they have to meet more demands on e.g. user-friendliness, manufacturability and ecological consideration. Shortened product life cycles are likely to increase development costs. This contributes to making errors in estimations of market trends very expensive. Companies therefore perform benchmarking studies that compare competitors on strategic-, process-, marketing- and product level. Also, they need a reliable instrument, which can predict the product’s reception on the market before the development cost gets too critical.However, success in a certain market segment does not only require knowledge about the competitors and their products' performance, but also about the impressions the products make on the customer. The latter requirement becomes much more important the more mature the products and the companies are. This means that the customer purchases a product based on more subjective terms such as manufacturer image, brand image, reputation, design, impression etc., although the products seem to be equal. A large number of manufacturers have started development activities to consider such subjective properties so that the product expresses the company image.
This demand triggers the introduction of a new research field dealing with the collection of customers’ hidden subjective needs and their translation into concrete products. Research is done foremost in Asia, namely Japan and Korea. In Europe a network has been forged under the 6th EU framework (compare www.engage-design.org). This network refers to the new research field as “emotional design” or “affective engineering”.
History of (Kansei) Affective Engineering
Nowadays, people want to use products that should be functional at a physical level, usable at a psychological level and should be attractive at a subjective, emotional level. Affective engineering is the study of the interactions between the customer and the product at that third level. It focuses on the relationships between the physical traits of product and its affective influence on the user. Thanks to this field of research, it is possible to gain knowledge on how to design more attractive products and make the customers satisfied.Methods in Affective Engineering
The area of integrating affective values in artifacts is not new at all. Already in the 18th century philosophers such as Baumgarten and Kant established the area of aesthetics. In addition to pure practical values, artifacts always also had an affective component . One example is jewellery found in excavations from the stone ages. Also the period of renaissance is a good example of that.
In the middle of the 19th century, the idea of aesthetics was deployed in scientific contexts. Charles E Osgood developed his Semantic Differentials Method in which he quantified the peoples’ perceptions of artifacts [4]. Some years later, in 1960, Professors Shigeru Mizuno and Yoji Akao developed an engineering approach in order to connect peoples’ needs to product properties. This method was called Quality Function Deployment (QFD). Another method, the Kano model was developed in the field of quality in the early 1980s by Professor Noriaki Kano, of Tokyo University. Kano’s model is used to establish the importance of individual product features for the customer’s satisfaction and hence it creates the optimal requirement for process oriented product development activities. A pure marketing technique is Conjoint Analysis. Conjoint analysis estimates the relative importance of a product’s attributes by analyzing the consumer’s overall judgment of a product or service. A more artistic method is called Semantic description of environments. It is mainly a tool for examining how a single person or a group of persons experience a certain (architectural) environment.
Although all of these methods are concerned with subjective impact, none of them can translate this impact to design parameters sufficiently. This can, however, be accomplished by Kansei Engineering. Kansei Engineering (KE) has been used as a tool for affective engineering. It was developed in the early 70s in Japan and is now widely spread among Japanese companies. In the middle of the 90s, the method spread to the United States, but cultural differences may have prevented the method to enfold its whole potential.
Kansei Engineering Procedure
As mentioned above, Kansei Engineering can be considered as a methodology within the research field of ‘Affective Engineering’. Some researchers have defined the content of the methodology. Shimizu et al. state that ‘Kansei Engineering is used as a tool for product development and the basic principles behind it are the following: identification of product properties and correlation between those properties and the design characteristics’.According to Nagasawa, one of the forerunners of Kansei Engineering, there are three focal points in the method:
• How to accurately understand consumer Kansei
• How to reflect and translate Kansei understanding into product design
• How to create a system and organization for Kansei orientated design
The following figure shows how Kansei Engineering works in principle.
Figure 1: Kansei Engineering System (KES).
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. This model is presented in Figure 2 below.Figure 2: A general model on Kansei Engineering [13].
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
Kansei Engineering software
As described above, Kansei data collection and analysis is often complex and connected with statistical analysis. Depending on which synthesis method is used, different computer software is used. Kansei Engineering Software (KESo) uses QT1 for linear analysis. The concept of Kansei Engineering Software (KESo) Linköping University in Sweden (www.kansei.eu). The software generates online questionnaires for collection of Kansei raw-dataAnother Software package (Kn6) was devleoped at the technical University of Valencia in Spain.
Both software packages improve the collection and evalutation of Kansei data. In this way even users with no specaialist compentence in advanced statistics can use Kansei Engineering