scholarly journals Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm

2017 ◽  
Vol 50 ◽  
pp. 755-771 ◽  
Author(s):  
Salman Nazari-Shirkouhi ◽  
Abbas Keramati
Keyword(s):  
Data Envelopment Analysis ◽  
Customer Satisfaction ◽  
Product Design ◽  
New Product ◽  
Fuzzy Regression ◽  
Data Envelopment ◽  
Analysis Algorithm ◽  
New Product Design

scholarly journals Rough Kansei Mining Model for Market-Oriented Product Design

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Esra Akgül ◽  
Mihrimah Özmen ◽  
Cem Sinanoğlu ◽  
Emel Kizilkaya Aydoğan
Keyword(s):  
Customer Satisfaction ◽  
Product Design ◽  
New Product Development ◽  
Rough Set ◽  
Design Methodology ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
New Product ◽  
Design Strategies ◽  
New Product Design

Companies need to develop new products towards customer's satisfaction in order to survive in the boom and bust cycle in todays’ economy. The capturing of customer satisfaction depends on customer needs, and generally, understanding emotions has a challenge for designers. Kansei engineering is a type of methodology to help customers and designers analyze needs and emotion for the new product development. Producing new product design with Kansei data increases customer satisfaction and helps to reach market goals. In this study, a market-oriented baby cradle design methodology is proposed, and we obtain the new product strategies with association rule extraction by using rough set theory. To obtain efficient rules, beforehand we selected sales knowledge-related Kansei words with our proposed approach: cost-based and multiclass decision-theoretic rough set (DTRS) attribute reduction. The new product design strategies which are obtained with proposed design methodology are consistent with customer expectations (mood space) and expert opinions (design team).

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