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Erschienen in: Journal of Intelligent Manufacturing 3/2020

01.03.2019

Integrating customer requirements into customized product configuration design based on Kano’s model

verfasst von: Shuangyao Zhao, Qiang Zhang, Zhanglin Peng, Yu Fan

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2020

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Abstract

Owing to the increasing concerns about customer needs in the current competitive market, the identification and incorporation of customer requirements (CRs) into product configuration designs have raised the interest of both researchers and practitioners. Most of the design methodologies focus on explicit technical domains to define CRs into specific design parameters directly. However, the CRs are so complicated that they are usually expressed in vague, ambiguous language containing uncertain information and are not in the form of well-defined specifications of product attributes and components. Kano’s model provides a qualitative way to classify CRs accurately. However, research contributions are seldom found in terms of quantitatively integrating Kano’s model with product designs. This paper identifies a novel approach based on the quantification of Kano’s model for integrating CRs into product engineering characteristics. Kano’s model is quantified by identifying the relationships between the CRs and customer satisfaction to link the requirements mapping phase and product configuration design phase. The quantitative results derived from Kano’s model are formulated as the multi-objective functions in a mixed non-linear programming model to identify the product configuration solution. For illustrative purposes, an example associated with the configuration design of a material-forming configuration production line is presented to demonstrate the capability of the proposed model.

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Metadaten
Titel
Integrating customer requirements into customized product configuration design based on Kano’s model
verfasst von
Shuangyao Zhao
Qiang Zhang
Zhanglin Peng
Yu Fan
Publikationsdatum
01.03.2019
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 3/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-019-01467-y

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