Abstract
The precisely perception of key customer requirements (CRs) is critically important for customer collaborative product innovation (CCPI) design. A novel approach is proposed based on the Kano model, interval 2-tuple linguistic representation model, and prospect theory. First of all, a Kano model is constructed to preliminarily screen the relatively important product function attributes. For the uncertain and vague information of CRs, an interval 2-tuple linguistic representation model is proposed to determine the weight of CRs. Then, the comprehensive prospects value is utilized for sorting the innovative programs based on the prospect theory. Finally, a numerical example is given to verify the scientific and validity of the proposed method.
Highlights
With the increasingly fierce market competition, as well as more and more personalized and diversified customer requirements (CRs), it is very critical for the modern manufacturing enterprises to grasp customers’ key requirements in a relatively short time because of the complexity of decision-making problems and the fuzziness of decision-making environments (Liu and Wang [1])
Based on the fuzzy characteristics, as well as the dynamicity, diversity and individuation of CRs, a Kano model was constructed to identify and filter the initial requirements of customers in this paper; secondly, the weights of CRs were determined by the proposed interval 2-tuple linguistic representation model; a comprehensive evaluation for the innovation schemes was given based on the prospect theory
(1) The Kano model focuses on the analysis of factors that affect customer satisfaction
Summary
With the increasingly fierce market competition, as well as more and more personalized and diversified CRs, it is very critical for the modern manufacturing enterprises to grasp customers’ key requirements in a relatively short time because of the complexity of decision-making problems and the fuzziness of decision-making environments (Liu and Wang [1]). Lilien et al [3] compared and analyzed the CCPI process of 3M in the United States and illustrated the clear benefits of customer innovation with actual sales data. Nowadays, this method has been widely used in product design (Song et al [4]; Dahl et al [5]). Chen et al [6] proposed an ontology learning CRs representation system, the system which preprocessed customer statements by language processing tools. Sheng et al [11] took the product service system as the research object, constructed the quality house, and determined the attribute weight of the Customers Customers Customers
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