Kano integrated robust design approach for aesthetical product design: a case study of a car profile

H. C. Yadav, Rajeev Jain (), A. R. Singh and P. K. Mishra
Additional contact information
H. C. Yadav: Indian Veterinary Research Institute
Rajeev Jain: Kalaniketan Polytechnic College
A. R. Singh: National Institute of Technology
P. K. Mishra: Motilal Nehru National Institute of Technology

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 7, No 15, 1709-1727

Abstract: Abstract Visual shape parameters and aesthetic aspects of a product are one of the crucial factors for the success of a product in the market. The type and the value of the shape parameters plays an important role in visual appearance of a product and designers tends to be critical while deciding these parameters. The aesthetic aspect of a product has been matter of concern for researchers with its electromechanical design. The Kano model has been found to be a useful tool to establish the relationship between performance criteria and customer satisfaction. To achieve the desired customer satisfaction weight of each product criteria is determined by using Kano model. This study presents an integrative design approach combining the Kano model, Taguchi method and grey relation analysis to obtain the optimal combination of shape parameters and aesthetic aspects. Prioritized criteria of aesthetic attributes have been abstracted through proposed methodology. A case study has been presented to evolve a profile of a car.

Keywords: Robust design approach; Kano model; Taguchi method; Grey relation analysis; Product design (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-016-1202-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:28:y:2017:i:7:d:10.1007_s10845-016-1202-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-016-1202-5

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().