AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products. (January 2016)

• Record Type: Journal Article
• Authors: Kwong, C.K.; Jiang, Huimin; Luo, X.G.
• Is Part Of: Engineering applications of artificial intelligence. Volume 47(2015:Nov.)
• Page Start: 49
• Page End: 60
• Publication Date: 2016-01
• Subjects: Affective design -- Marketing -- NSGA-II -- Fuzzy regression -- Chaos optimization algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.04.001
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In the early stage of product design, particularly for consumer products, affective design, engineering, and marketing issues must be taken into considerationand they are commonly performed respectively by product designers, engineers, and marketing personnel. However, they have different concerns and focuses with regard to the new product design. Thus, these three processes are commonly conducted separately, leading to a sub-optimal and even sub-standard design. Such scenario indicates the need to incorporate the concerns of the three processes in the early stage of product design. However, no study has explored the incorporation of the concerns of the three processes into the product design. In this paper, an artificial intelligence (AI)-based methodology for integrating affective design, engineering, and marketing for defining design specifications of new products is proposed by which the concerns of the three processes can be considered simultaneously in the early design stage. The proposed methodology mainly involves development of customer satisfaction and cost models using fuzzy regression, generation of product utility functions using chaos-based fuzzy regression, formulation of a multi-objective optimization model and its solving using a non-dominated sorting genetic algorithm-II (NSGA-II). A case study was conducted for electric iron design to evaluate the effectiveness of the proposed methodology.