# Application of Quality Function Deployment for Product Design Concept Selection
## 2 Literature Review  
Concept development consists of a series of divergent and convergent steps. While divergent steps generate various concept alternatives, convergent steps handle how to evaluate and select the best new concepts. This convergent process is defined as 'concept selection' or 'design concept evaluation' by [PERSON] [21]. In divergent steps, any potential concepts are generated after considering CR and designer intentions. The QFD approach, which was discussed by [PERSON] et al., is beneficial tools in this step because it interprets CR into engineering specification (ES) [22]. QFD is a customer-oriented approach to improve quality in NPD as well as ensure that the customer is engaged throughout the product's specification by [PERSON] [23]. In this study, we apply QFD to generate design concepts and use AHP to evaluate the concept competitiveness. There is much existing research on concept selection. The literature review of [PERSON] and [PERSON] classified the concept selection methods into six categories: (a) decision matrices methods, (b) AHP models, (c) uncertainty modeling, (d) economic models, (e) optimization concepts, and (f) heuristics [24]. In detail, [PERSON] in 1991; [PERSON], [PERSON] and [PERSON] in 2017 tell that while decision matrices methods can provide useful insight into qualitative preference over the alternatives, it is not able to compare relative importance of criteria [25; 26]. The strong point of AHP method is that it can evaluate the relative importance by the pair-wise comparison by [PERSON], [PERSON] and [PERSON] [27]; and [PERSON], [PERSON] and [PERSON] [28]. Uncertainty modeling method allows for uncertainties in the concept selection process. [PERSON] et al. state that fussy logic is one of the popular tools adopted for uncertainty modeling [29]; and [PERSON] confirms that economic models use a utility function rather than discrete ratings or fuzzy methods [30]. However, the drawback of this method is not to accommodate coupled decisions. [PERSON] suggested that optimization concept uses the numerical techniques to identify optimal solution [31]. However, this method is difficult and complicated due to the complexity to understand and the utility of the advanced mathematics. The last heuristics method uses the genetic algorithms (GA)--[PERSON] and [PERSON] confirm it [32]. This approach makes the designer view the trade-off between performances and has the merits when searching much large decision space. However, due to the tremendous calculation, the burden on designer is much high and it is also essential to have a high-speed computer. In the recent research, [PERSON] et al. (2015) proposed the integrated analytic hierarchy process (AHP) and compromise ranking method (VIKOR) for design concept evaluation [33]. They illustrate the six design concepts of the lithography tools. [PERSON] and [PERSON] (2014) suggested the integrated approach using data envelopment analysis (DEA) and conjoint analysis (CA) [34]. They used the development burdens as the input criteria and the customer perceived value as the output criteria which conducted by conjoint analysis. They ranked the design concepts by DEA scores and selected the final design concept. They applied this approach to twelve design concepts of smartphone. Starting from 1980, [PERSON] [35; 36; 37] suggested that the AHP is a multi-criteria decision-making framework using hierarchical relationships among the decision levels. Later in 2011, [PERSON], [PERSON], and [PERSON] confirmed that this is a powerful method for solving complicated and unstructured problems that may have interactions and correlations among different objectives and goals [38]. The AHP has two models, the pair-wise comparison method (relative measurement) and the rating method (absolute measurement). In the pair-wise comparison method, the main logic is to assess the relative priorities between two criteria, and this helps decision makers improve their understanding of complicated decisions by dividing the problems into a hierarchical structure.