User-oriented design for the optimal combination on product design

Abstract This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product color affect product image individually and as a whole. The concept of Kansei Engineering is used to extract the experimental samples as a data base for Quantitative Theory Type I and neural networks (NNs). The result of numerical analysis suggests that mobile phone makers need to provide various product colors to attract users, in addition to product forms. This paper demonstrates the advantage of using NNs for determining the optimal combination of product form and product color, particularly if the product into design elements. Based on the analysis of NNs, we can use 72 representative product colors of each mobile phone to develop a product color data base consisting of 16777216 (=256×256×256, True-Color model) colors with the associated product image. The design data base provides useful insights to save any amount of money and time for the new product development. The product designers can input a product image to work out an adequate color on a mobile phone. Furthermore, the design data base can be used, in conjunction with computer-aided design system or virtual reality technology, to build a 3D model for facilitating the design process of mobile phones. Although, the mobile phones are chosen as the object of the experimental study, this approach can be applied to other products with various design elements.

[1]  Michael Godfrey,et al.  Efficacy of end-user neural network and data mining software for predicting complex system performance , 2003 .

[2]  Yang-Cheng Lin,et al.  A STUDY ON THE COLOR AND STYLE COLLOCATION OF MOBILE PHONES USING NEURAL NETWORK METHOD , 2001 .

[3]  K. L. Edwards.  Product design: Practical methods for the systematic development of new products: Mike Baxter Chapman & Hall, 1995, ISBN 0-412-63230-6, 308pp, £24.99 , 1995 .

[4]  Bernard C. Jiang,et al.  Fuzzy neural network modeling for product development , 1999 .

[5]  Fabrizio Salvador,et al.  Configuring products to address the customization-responsiveness squeeze: A survey of management issues and opportunities , 2004 .

[6]  Tomio Jindo,et al.  A fuzzy logic analysis method for evaluating human sensitivities , 1995 .

[7]  Yukihiro Matsubara,et al.  An analysis of Kansei structure on shoes using self-organizing neural networks , 1997 .

[8]  Shang H. Hsu,et al.  Perceptual factors underlying user preferences toward product form of mobile phones , 2001 .

[9]  Mitsuo Nagamachi,et al.  Rule-based inference model for the Kansei Engineering System , 1999 .

[10]  A. Kengpol.  Design of a decision support system to evaluate the investment in a new distribution centre , 2004 .

[11]  Mitsuo Nagamachi,et al.  Development of a design support system for office chairs using 3-D graphics , 1995 .

[12]  Kulwant S. Pawar,et al.  Performance measurement for product design and development in a manufacturing environment , 1999 .

[13]  Kazuyuki Shimizu,et al.  Application of artificial neural network and fuzzy control for fed-batch cultivation of recombinant Saccharomyces cerevisiae , 1996 .

[14]  Katja Tornberg,et al.  Activity-based costing and process modeling for cost-conscious product design: A case study in a manufacturing company , 2002 .

[15]  Chien-Hung Wei.  Analysis of artificial neural network models for freeway ramp metering control , 2001, Artif. Intell. Eng..

[16]  C. Osgood,et al.  The Measurement of Meaning , 1958 .

[17]  J. J. Kanet,et al.  Measuring quality from the consumer's perspective: A methodology and its application , 1995 .

[18]  Myung Hwan Yun,et al.  Incorporating user satisfaction into the look-and-feel of mobile phone design , 2003, Ergonomics.

[19]  Jean-François Boujut,et al.  A co-operation framework for product–process integration in engineering design , 2002 .

[20]  Ming-Chuen Chuang,et al.  Expressing the expected product images in product design of micro-electronic products , 2001 .

[21]  Richard Y. K. Fung,et al.  Development of a dynamic data interchange scheme to support product design in agile manufacturing , 2004 .

[22]  Mitsuo Nagamachi,et al.  Kansei engineering: A study on perception of vehicle interior image , 1997 .

[23]  N. Saccani,et al.  Integration techniques in customer-supplier relationships: An empirical research in the Italian industry of household appliances , 2004 .

[24]  Shih-Wen Hsiao,et al.  A morphing method for shape generation and image prediction in product design , 2002 .

[25]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[26]  Ray Eberts,et al.  Neural network-based agents for integrating information for production systems , 1995 .

[27]  J. Hair.  Multivariate data analysis , 1972 .

[28]  Mitsuo Nagamachi,et al.  Kansei engineering as a powerful consumer-oriented technology for product development. , 2002, Applied ergonomics.

[29]  Shang H. Hsu,et al.  A semantic differential study of designers’ and users’ product form perception , 2000 .

[30]  Yukihiro Matsubara,et al.  An automatic builder for a Kansei Engineering expert system using self-organizing neural networks , 1995 .

[31]  Yukihiro Matsubara,et al.  Hybrid Kansei engineering system and design support , 1997 .

[32]  Nigel Cross,et al.  Engineering design methods , 1989 .

[33]  Shih-Wen Hsiao,et al.  A semantic and shape grammar based approach for product design , 1997 .

[34]  Yoshihisa Fujiwara,et al.  Human sensory perception oriented image processing in a color copy system , 1995 .

[35]  D. Towill,et al.  The impact of product life cycle on supply chain strategy , 2003 .

[36]  Tomio Jindo,et al.  Application studies to car interior of Kansei engineering , 1997 .

[37]  Sergio Cavalieri,et al.  Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry , 2004 .

[38]  Richard M. Golden,et al.  Mathematical Methods for Neural Network Analysis and Design , 1996 .