QFD and Fuzzy Kano model based approach for classification of aesthetic attributes of SUV car profile
Journal of Intelligent Manufacturing, ISSN: 1572-8145, Vol: 31, Issue: 2, Page: 271-284
2020
- 68Citations
- 96Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The aesthetic appearance and features of a product are the most censorious elements for the accomplishment of a product in the industry. An aesthetic is the quality element which adds value to the product design. Product design is a basic need of every manufacturing company in which visual aspects play an important role to enhance the customer satisfaction. Therefore, Quality Function Deployment (QFD) can be considered as an effective tool for translating the customer’s voice into the design of the product and its specifications. The Kano model helps to identify the desires of a product that brings greater satisfaction or dissatisfaction level to the customer. Kano model tells the connection between the product’s attributes and its satisfaction to the customer. For achieving better results, Fuzzy Kano model has been more favorably applied over traditional Kano model. In this work, an approach of Integration of Kano model into QFD has been applied with an aim to examine the customer satisfaction based on aesthetic sentiments. A Sport Utility Vehicle has been selected for the study. The aesthetic attributes have been selected with the help of QFD and their importance and classification have been calculated using both Fuzzy Kano and Traditional Kano model. The result of Fuzzy Kano and Traditional Kano model has also been compared to calculate the effectiveness of the applied approach.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85053598921&origin=inward; http://dx.doi.org/10.1007/s10845-018-1444-5; http://link.springer.com/10.1007/s10845-018-1444-5; http://link.springer.com/content/pdf/10.1007/s10845-018-1444-5.pdf; http://link.springer.com/article/10.1007/s10845-018-1444-5/fulltext.html; https://dx.doi.org/10.1007/s10845-018-1444-5; https://link.springer.com/article/10.1007/s10845-018-1444-5
Springer Science and Business Media LLC
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