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A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning
by
Sheng Quan Xie
, Xu, Xun
, Pai Zheng
in
Advanced manufacturing technologies
/ Comparative studies
/ Customers
/ Heterogeneity
/ Manufacturing
/ Product development
/ Quality function deployment
/ Ratings
2019
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A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning
by
Sheng Quan Xie
, Xu, Xun
, Pai Zheng
in
Advanced manufacturing technologies
/ Comparative studies
/ Customers
/ Heterogeneity
/ Manufacturing
/ Product development
/ Quality function deployment
/ Ratings
2019
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Do you wish to request the book?
A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning
by
Sheng Quan Xie
, Xu, Xun
, Pai Zheng
in
Advanced manufacturing technologies
/ Comparative studies
/ Customers
/ Heterogeneity
/ Manufacturing
/ Product development
/ Quality function deployment
/ Ratings
2019
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A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning
Journal Article
A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning
2019
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Overview
Customer requirements (CRs) play a significant role in the product development process, especially in the early design stage. Quality function deployment (QFD), as a useful tool in customer-oriented product development, provides a systematic approach towards satisfying CRs. Customers are heterogeneous and their requirements are often vague, therefore, how to determine the relative importance ratings (RIRs) of CRs and eventually evaluate the final importance ratings is a critical step in the QFD product planning process. Aiming to improve the existing approaches by interpreting various CR preferences more objectively and accurately, this paper proposes a weighted interval rough number method. CRs are rated with interval numbers, rather than a crisp number, which is more flexible to adapt in real life; also, the fusion of customer heterogeneity is addressed by assigning different weights to customers based on several factors. The consistency of RIRs is maintained by the proposed procedures with design rules. A comparative study among fuzzy weighted average method, rough number method and the proposed method is conducted at last. The result shows that the proposed method is more suitable in determining the RIRs of CRs with vague information.
Publisher
Springer Nature B.V
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