New product development~(NPD) is a quite risky and uncertain process. In order to reduce the risks and uncertainties, the firms need to evaluate their new product at each step carefully and make accurate decisions. This paper focuses on uncertain go/no-go decisions in the NPD process. To do so, a probabilistic approach is firstly proposed to elicit a probability distribution of the gate-team's judgement. Secondly, a probabilistic approach is proposed to perform group and multicriteria aggregation with the random interpretation of group weights and criteria weights.
Kansei Engineering~(KE) has been developed as a methodology to deal with consumers' subjective impressions and images of a product into the design elements of a product. One central step amongst KE is to generate the Kansei profiles of the products. Kansei is a quite subjective, ambiguous, and uncertain concept, which is frequently represented in linguistic forms. Toward this end, this paper tries to cope thoroughly with the uncertainties of Kansei in KE. A probabilistic approach is proposed to generate Kansei profiles involving the ``individual" uncertainty, ``group" uncertainty, and the partial semantic overlapping of Kansei. Our approach results with a probability distribution on a set of linguistic Kansei labels.
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