English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

For continuous growth and sustaining the competitiveness of a company, product developers spend most of their time making crucial decisions to address a great variety of unpredictable and uncontrollable information. Several mathematical approaches have already been adopted to aid the developers in selecting the best product concept for meeting customers’ requirements and exceeding their expectations. However, those methods do not cope with fully revealed developers’ preferences and do not take into account the random distribution of the target values of engineering characteristics (ECs). In this paper, the application of a quality function deployment (QFD)-based model and a stochastic dominance-based method is presented for product concept development. The first phase in the approach is to construct a product planning house of quality (PPHoQ), which is the core and the engine of the entire QFD model. This model depicts the relationship between the customers’ requirements (CRs) and the ECs for a product. The proposed approach addresses both the relationships between CRs and ECs, in addition to the correlations among the ECs. In this study, developers are invited to express their preferences using different types of linguistic terms dependent on their diverse backgrounds and understanding levels of the product. Based on the outcomes of the PPHoQ process, a variety of alternative concepts can be created. The alternatives are then prioritized and ranked in the second phase. The proposed approach facilitates the random distribution with stochastic variables rather than fuzzy methods to obtain more realistic product concept alternatives. Several examples and comparative results further illustrate that unbalanced linguistic terms and stochastic dominance efficiently endow the product concept selection model with uncertain information and the random distribution in a realistic style.

An integrated quality-function-deployment and stochastic-dominance-based decision-making approach for prioritizing product concept alternatives