Advertisement

Determining of Customer’s Kansei Needs and Product Design Attributes by Rough Set Theory

  • Emel Kizilkaya AydoğanEmail author
  • Esra Akgul
  • Yilmaz Delice
  • Cem Sinanoglu
Conference paper
  • 48 Downloads
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

The trend of new product development taking into account a customer’s feeling and needs has become very important for companies’ development and competition in the market. Kansei engineering is a consumer-oriented technology that seeks to capture the voice of the customer to produce a successful product. This method helps to transform customers feeling into the design parameters. In order to improve customer satisfaction, it is very important to determine the design parameters that make up the product. This paper presents a design support system intended for use in designing new product. A product has a lots of design attributes. For this reason, the product design attributes are reduced with Rough sets theory and main design attributes are obtained for developing a new product. Rough sets theory deals with uncertain or conflicting data. After determining product design attributes, the different products are produced for evaluating customer’s feel with Kansei adjectives. Customer evaluations were conducted using the semantic differential method to examine the relationship between users’ assessments of product and design elements. Kansei results are analyzed by applied Principle Component Analysis to determine the relationships between products and emotions that affect the general preferences of customers. Baby cradle design is taken as a case study; but this method can be used to develop other products. As a result, this paper presents a design support system intended for use in designing new product, so the designed product can fit more closely to the consumers’ desires.

Keywords

Kansei engineering Rough sets theory Principle component analysis Product design 

Notes

Acknowledgments

This work was supported by the Scientific and Technological Research Council of Turkey “TÜBİTAK-TEYDEB” programme with the project number-5170065.

References

  1. 1.
    Nagamachi M (1995) Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int J Ind Ergon 15:3–11.  https://doi.org/10.1016/0169-8141(94)00052-5 CrossRefGoogle Scholar
  2. 2.
    Hsiao SW (1994) Fuzzy set-theory applied to car style design. Int J Veh Des 15:255–278.  https://doi.org/10.1504/IJVD.1994.061860 CrossRefGoogle Scholar
  3. 3.
    Lai HH, Lin YC, Yeh CH, Wei CH (2006) User-oriented design for the optimal combination on product design. Int J Prod Econ 100:253–267.  https://doi.org/10.1016/j.ijpe.2004.11.005 CrossRefGoogle Scholar
  4. 4.
    Poirson E, Depince P, Petiot JF (2007) User-centered design by genetic algorithms: application to brass musical instrument optimization. Eng Appl Artif Intell 20:511–518.  https://doi.org/10.1016/j.dsp.2011.01.007 CrossRefGoogle Scholar
  5. 5.
    Al-Mayyan W, Own HS, Zedan H (2011) Rough set approach to online signature identification. Digital Signal Process 21:477–485.  https://doi.org/10.1016/j.dsp.2011.01.007 CrossRefGoogle Scholar
  6. 6.
    Zhai LY, Khoo LP, Zhong ZW (2009) A rough set based decision support approach to improving consumer affective satisfaction in product design. Int J Ind Ergon 39:295–302.  https://doi.org/10.1016/j.ergon.2008.11.003 CrossRefGoogle Scholar
  7. 7.
    Pawlak Z (1991) Rough sets, theoretical aspects of reasoning about data. Kluwer, DordrechtGoogle Scholar
  8. 8.
    Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Skowron A (ed) Rough fuzzy hybridization. Springer, Berlin, pp 3–98Google Scholar
  9. 9.
    Alisantoso D, Khoo LP, Lee BI, Fok SC (2005) A rough set approach to design concept analysis in a design chain. Int J Adv Manuf Technol 26(5–6):427–435.  https://doi.org/10.1007/s00170-003-2034-y CrossRefGoogle Scholar
  10. 10.
    Pawlak Z (1996) Why rough sets. In: Proceedings of the Fifth International Conference on Fuzzy Systems, vol 2, New Orleans, LA, pp 738–743Google Scholar
  11. 11.
    Aydogan Kızılkaya E (2011) Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 38:3992–3998.  https://doi.org/10.1016/j.eswa.2010.09.060 CrossRefGoogle Scholar
  12. 12.
    Mundt JT, Glenn NF, Weber KT, Prather TS, Lass LW, Pettingill J (2005) Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques. Remote Sens Environ 96:509–517.  https://doi.org/10.1016/j.rse.2005.04.004 CrossRefGoogle Scholar
  13. 13.
    Tian Y, Guo P, Lyu MR (2005) Comparative studies on feature extraction methods for multispectral remote sensing image classification. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 2:1275–1279.  https://doi.org/10.1109/ICSMC.2005.1571322
  14. 14.
    Lei TC, Wan S, Chou TY (2008) The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification—a case study on rice classification. Taiwan Comput Geosci 12:1–14.  https://doi.org/10.1007/s10596-007-9057-7 CrossRefGoogle Scholar
  15. 15.
    Lokman AM (2010) Design and emotion: the Kansei engineering methodology. Malays J Comput 1(1):1–11Google Scholar
  16. 16.
    Osgood CE, Suci CJ (1957) The measurement of meaning. University of Illinois Press, Urbana, ILGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Emel Kizilkaya Aydoğan
    • 1
    Email author
  • Esra Akgul
    • 1
  • Yilmaz Delice
    • 2
  • Cem Sinanoglu
    • 1
  1. 1.Erciyes UniversityKayseriTurkey
  2. 2.Kayseri UniversityKayseriTurkey

Personalised recommendations