A RULE-BASED APPROACH TO SOFA DESIGN WITH KANSEI ENGINEERING
Yıl 2021,
Cilt: 32 Sayı: 1, 69 - 89, 30.04.2021
Mithat Zeydan
,
Abdurrahman Öcal
Öz
Today, product design has been much more complicated when compared with the past. Shorter product life cycle increased product development cost. In order to stay competitive in the market, a well-designed product should be able to not only meet functionality requirements, but also satisfy consumers’ psychological needs (or feelings). In this study, a rough set based kansei engineering decision support system was developed using Fuzzy AHP (Analytical Hierarchical Process) and PLS (Partial Least Square) approximations. Decision rules for 16 design samples were generated by orthogonal design. Twelve selected samples were tested whether the decision of customers were true or not related with the sample products in terms of taste of customers. We find that qualitative evaluations for product design in kansei engineering are more consistent with the results of consumers rather than quantitative evaluations.
Kaynakça
- Alemi-Ardakani, M., Milani, A.S., Yannacopoulos, S., Shokouhi, G. (2016). On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites. Expert SystemsWithApplications. 46, 426-438.
- Bergman, B., Klefsjö, B. (1990). Statistical Engineering for quality and productivity improvements. European Journal of Engineering Education. 15(3), 257-266.
- Camo Analytics, PLS regression, http://www.camo.com/resources/pls-regression.html (accessed 15 March 2020).
- Chang, D.-Y. (1996). Theory and Methodology: Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research. 95, 649-655.
- Chiu, M.-C., Chen, Y.-W. (2018). The analysis of incomplete dataset using fuzzy c-medoids algorithm with a case study of physical examination dataset. International Journal of Industrial Engineering: Theory, Applications and Practice (IJIETAP). 25(1), 67-76.
- Guo, J.-Y., Chankong, V. (2002). Rough set-based approach to rule generation and rule induction. International Journal of General Systems. 31 (6), 601-617.
- Lin, C., Liu, C., Chen, H., Lin, C. Y., Chang, C. (2013). Consumer preference and image perceptions to classic chairs. International Proceedings of Economics Development and Research. 59 (22), 105-109.
- Ma, M.-Y., Chen, C.-Y., Wu, F.-G. (2007). A design decision-making support model for customized product color combination. Comput. Ind. 58, 504–518.
- Memurlar, https://www.memurlar.net/haber/800103/mobilya-satislarinda-yuzde-30-artis-bekleniyor.html/ (accessed 15 March 2020).
- Minitab18 support, PLS regression, https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/regression/supporting-topics/partial-least-squares-regression/what-is-partial-least-squares-regression/ (accessed 15 March 2020).
- Nagamachi, M. (2011). Kansei/Affective Engineering. F: CRC Press. 31-225.
- Nagamachi, M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of industrial ergonomics. 15, 3-11.
- Nagamachi, M. (1997). Kansei engineering and comfort. International Journal of Industrial Ergonomics. 19, 79-80.
- Nagamachi, M. (2006). Kansei engineering and Rough sets model. RSCTC, LNAI, 4259, Springer. 27-37.
- Nagamachi, M., Okazaki, Y., Ishikawa, M. (2006). Kansei engineering and application of the Rough sets model. In Proceedings of IMechE. 220, 763–768.
- Nishino T. (2005). “Rough sets and Kansei”, in Nagamachi, M. (Ed.), Product Development and Kansei, Kaibundo, Tokyo.
- Nishino, T., Nagamachi, M., Ishihara, S. (2001). Rough set analysis on Kansei evaluation of color. Conference on Affective Human Factors Design. 109-115. Singapore.
- Nishino, T., Nagamachi, M., Tanaka, H. (2005). Variable precision Bayesian Rough sets model and its application to human evaluation data. RSFDGrC, LNAI 3641, Springer. 294-303.
- Nishino, T., Nagamachi, M., Tanaka, H. (2006). Variable precision Bayesian Rough sets model and its application to Kansei engineering, transactions on Rough sets V (International Journal of Rough Set Society), LNCS 4100, Springer. 190-206.
- Okuhara, K., Matsubara, Y., Ueno, N. (2005). Extraction of relationship among Kansei words by expert system using Rough set analysis. In Proceedings of the 2005 International Conference on Active Media Technology. 461-466. Kagawa, Japan.
- Osgood, C., Suci, C., Tannenbaum, P. (1957). The measurement of Meaning. Urbana: University of Illinois Press. 76-124.
- Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences 11, 341–356.
- Pawlak, Z., Skowron A. (2007). Rough sets: Some extensions. Information Sciences. 177, 28-40.
- Pitaktiratham J., Anantavoranich P. (2012). Semantic Questionnaire-Tool for Emotion Research the Integration of Consumer Behavior and Kansei Engineering (Case Study in Furniture Design). International Journal of Science and Engineering Investigations. 1(10), 66-71.
- Polkowski L., Tsumoto S., Lin TY. (2012). Rough set methods and applications: new developments in knowledge discovery in information systems, chapter 4. Physica.
- Rosyidi, C.N., Hermayanti, I., Laksono, P.W., Purwaningrum, L., Susmartini, S., Murakic, S. (2016). Desk and chair design of elementary school using Kansei engineering and Conjoint analysis. Journal of Engineering and Applied Sciences. 11 (11), 2514-2519.
- Roy, R., Reidel, J.C.k.h. (1997). Design and innovation in successful product competition. Technovation, 17(10), 537–548.
- Samson, D., Daft, R.L. (2012). Management: Asia Pacific Edition. United Kingdom: Cengage Learning.
- Schütte, S. (2002). Designing feelings into products: Integrating Kansei engineering methodology in product development. Linkoping: Linkoping Universitet. Thesis 946. Department of Mechanical Engineering, Institute of Technology, Linkoping Universitet.
- Shillito, M.L. (2001), Acquiring, processing, and deploying voice of the customer, CRC press LLC.
- Singh, J., Howell, R.D., Rhoads, G.K. (1990). Adaptive designs for Likert-type data: An approach for implementing Marketing surveys. Journal of Marketing Research. 27 (3), 304-321.
- Taghikhah, F., Pouya, A. (2014). Developing an integrated model to improve the performance of Kansei engineering by PCA and TOPSIS. Advances in Environmental Biology. 8 (12), 1270-1279.
- Wang, T.C., Lee, H.D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications. 36 (5), 8980-8985.
- Yong-jun, Y., Zhong-feng, Z., Rui-lin, H. (2014). Study on design of chair shaping based on Kansei engineering. International Journal of Scientific and Engineering Research. 5 (8), 273-276.
- Zhongfeng, Z., Kai, H., Yongjun, Y. (2013). Research on furniture image modelling design based on Kansei engineering. Journal of Theoretical and Applied Information Technology. 49 (3), 844-849.
A RULE-BASED APPROACH TO SOFA DESIGN WITH KANSEI ENGINEERING
Yıl 2021,
Cilt: 32 Sayı: 1, 69 - 89, 30.04.2021
Mithat Zeydan
,
Abdurrahman Öcal
Öz
Today, product design has been much more complicated when compared with the past. Shorter product life cycle increased product development cost. In order to stay competitive in the market, a well-designed product should be able to not only meet functionality requirements, but also satisfy consumers’ psychological needs (or feelings). In this study, a rough set based kansei engineering decision support system was developed using Fuzzy AHP (Analytical Hierarchical Process) and PLS (Partial Least Square) approximations. Decision rules for 16 design samples were generated by orthogonal design. Twelve selected samples were tested whether the decision of customers were true or not related with the sample products in terms of taste of customers. We find that qualitative evaluations for product design in kansei engineering are more consistent with the results of consumers rather than quantitative evaluations.
Kaynakça
- Alemi-Ardakani, M., Milani, A.S., Yannacopoulos, S., Shokouhi, G. (2016). On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites. Expert SystemsWithApplications. 46, 426-438.
- Bergman, B., Klefsjö, B. (1990). Statistical Engineering for quality and productivity improvements. European Journal of Engineering Education. 15(3), 257-266.
- Camo Analytics, PLS regression, http://www.camo.com/resources/pls-regression.html (accessed 15 March 2020).
- Chang, D.-Y. (1996). Theory and Methodology: Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research. 95, 649-655.
- Chiu, M.-C., Chen, Y.-W. (2018). The analysis of incomplete dataset using fuzzy c-medoids algorithm with a case study of physical examination dataset. International Journal of Industrial Engineering: Theory, Applications and Practice (IJIETAP). 25(1), 67-76.
- Guo, J.-Y., Chankong, V. (2002). Rough set-based approach to rule generation and rule induction. International Journal of General Systems. 31 (6), 601-617.
- Lin, C., Liu, C., Chen, H., Lin, C. Y., Chang, C. (2013). Consumer preference and image perceptions to classic chairs. International Proceedings of Economics Development and Research. 59 (22), 105-109.
- Ma, M.-Y., Chen, C.-Y., Wu, F.-G. (2007). A design decision-making support model for customized product color combination. Comput. Ind. 58, 504–518.
- Memurlar, https://www.memurlar.net/haber/800103/mobilya-satislarinda-yuzde-30-artis-bekleniyor.html/ (accessed 15 March 2020).
- Minitab18 support, PLS regression, https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/regression/supporting-topics/partial-least-squares-regression/what-is-partial-least-squares-regression/ (accessed 15 March 2020).
- Nagamachi, M. (2011). Kansei/Affective Engineering. F: CRC Press. 31-225.
- Nagamachi, M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of industrial ergonomics. 15, 3-11.
- Nagamachi, M. (1997). Kansei engineering and comfort. International Journal of Industrial Ergonomics. 19, 79-80.
- Nagamachi, M. (2006). Kansei engineering and Rough sets model. RSCTC, LNAI, 4259, Springer. 27-37.
- Nagamachi, M., Okazaki, Y., Ishikawa, M. (2006). Kansei engineering and application of the Rough sets model. In Proceedings of IMechE. 220, 763–768.
- Nishino T. (2005). “Rough sets and Kansei”, in Nagamachi, M. (Ed.), Product Development and Kansei, Kaibundo, Tokyo.
- Nishino, T., Nagamachi, M., Ishihara, S. (2001). Rough set analysis on Kansei evaluation of color. Conference on Affective Human Factors Design. 109-115. Singapore.
- Nishino, T., Nagamachi, M., Tanaka, H. (2005). Variable precision Bayesian Rough sets model and its application to human evaluation data. RSFDGrC, LNAI 3641, Springer. 294-303.
- Nishino, T., Nagamachi, M., Tanaka, H. (2006). Variable precision Bayesian Rough sets model and its application to Kansei engineering, transactions on Rough sets V (International Journal of Rough Set Society), LNCS 4100, Springer. 190-206.
- Okuhara, K., Matsubara, Y., Ueno, N. (2005). Extraction of relationship among Kansei words by expert system using Rough set analysis. In Proceedings of the 2005 International Conference on Active Media Technology. 461-466. Kagawa, Japan.
- Osgood, C., Suci, C., Tannenbaum, P. (1957). The measurement of Meaning. Urbana: University of Illinois Press. 76-124.
- Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences 11, 341–356.
- Pawlak, Z., Skowron A. (2007). Rough sets: Some extensions. Information Sciences. 177, 28-40.
- Pitaktiratham J., Anantavoranich P. (2012). Semantic Questionnaire-Tool for Emotion Research the Integration of Consumer Behavior and Kansei Engineering (Case Study in Furniture Design). International Journal of Science and Engineering Investigations. 1(10), 66-71.
- Polkowski L., Tsumoto S., Lin TY. (2012). Rough set methods and applications: new developments in knowledge discovery in information systems, chapter 4. Physica.
- Rosyidi, C.N., Hermayanti, I., Laksono, P.W., Purwaningrum, L., Susmartini, S., Murakic, S. (2016). Desk and chair design of elementary school using Kansei engineering and Conjoint analysis. Journal of Engineering and Applied Sciences. 11 (11), 2514-2519.
- Roy, R., Reidel, J.C.k.h. (1997). Design and innovation in successful product competition. Technovation, 17(10), 537–548.
- Samson, D., Daft, R.L. (2012). Management: Asia Pacific Edition. United Kingdom: Cengage Learning.
- Schütte, S. (2002). Designing feelings into products: Integrating Kansei engineering methodology in product development. Linkoping: Linkoping Universitet. Thesis 946. Department of Mechanical Engineering, Institute of Technology, Linkoping Universitet.
- Shillito, M.L. (2001), Acquiring, processing, and deploying voice of the customer, CRC press LLC.
- Singh, J., Howell, R.D., Rhoads, G.K. (1990). Adaptive designs for Likert-type data: An approach for implementing Marketing surveys. Journal of Marketing Research. 27 (3), 304-321.
- Taghikhah, F., Pouya, A. (2014). Developing an integrated model to improve the performance of Kansei engineering by PCA and TOPSIS. Advances in Environmental Biology. 8 (12), 1270-1279.
- Wang, T.C., Lee, H.D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications. 36 (5), 8980-8985.
- Yong-jun, Y., Zhong-feng, Z., Rui-lin, H. (2014). Study on design of chair shaping based on Kansei engineering. International Journal of Scientific and Engineering Research. 5 (8), 273-276.
- Zhongfeng, Z., Kai, H., Yongjun, Y. (2013). Research on furniture image modelling design based on Kansei engineering. Journal of Theoretical and Applied Information Technology. 49 (3), 844-849.