Quality Function Deployment (QFD) is a powerful tool that translates the Voice of the Customer (VoC) into the Engineering Characteristics (ECs), which are those that can be modified in order to meet the desires of the customer. A main objective of QFD is the determination of target values of ECs; however, the conventional QFD aims only empirically at finding these targets, which makes it difficult for the ECs to be optimum. This paper proposes a novel method for determining optimum targets in QFD. Fuzzy numbers are used to represent the imprecise nature of the judgements, and to define more appropriately the relationships between ECs and Customer Attributes (CAs). Constraints such as cost, technical difficulty and market position are considered. An example of a car door is presented to show the application of the method.

Engineering design evaluation is characterized by imprecise (vague) importance and satisfaction levels of criteria, which are better treated as fuzzy variables rather than as subjective crisp variables. Nevertheless, operations on fuzzy numbers tend to increase unnecessarily the imprecision when they are combined for some applications, particularly, when a fuzzy-weighted average (FWA) is calculated. This paper proposes a novel method of operating on fuzzy numbers to obtain a fuzzy-weighted average of desirability levels during design evaluation. The method produces overall desirability levels less imprecise and more realistic than those of the conventional FWA. Furthermore, the balance points of these fuzzy numbers seem to be more credible than those of the FWA. An example is presented, which demonstrates the advantages of the method developed.

As variables in the design evaluation stage are imprecise (fuzzy), tools, such as fuzzy set theory, should be used. This paper presents several fuzzy approaches to design evaluation. Weights of criteria and performance levels are captured by fuzzy numbers, and the overall performance of an alternative is calculated through the new fuzzy weighted average. Different approaches to express and calculate performance are discussed. Two metrics for measuring performance levels per criterion of a solution are proposed. Also, a new type of fuzzy goal is suggested. Finally, a novel way of comparing the overall performance of a design candidate by drawing an aggregate profile of performance is proposed. It is concluded that fuzzy set theory is a powerful and flexible tool for dealing with the imprecision in different types of problems in design evaluation.