Fuzzy Alpha-cuts to capture customer requirements in improving product development

Abstract

Quality Function Deployment is a tool to develop and design the quality in product and improve competitiveness advantages in the market. In developing new products and projects, we receive the needs from the customer, pass it around a corporate communication circle, and eventually return it to the customer in the form of the new product. First, needs and languages received from customer are often ambiguous, imprecise, and uncertain causing deviated studied results, and in a disregarding of the voice of customer. Second, to improve quality and solve the uncertainty in product development, numerous researchers try to apply the fuzzy set theory to product development. Their models usually focus only on customer requirements or on engineering characteristics. The subsequent stages of product design are rarely addressed. The correlation between engineering characteristics and benchmarking analysis disregarded in most of Quality Function Deployment practices related researches. This commonly upsets the consequences to delay and failed project development. Aiming to solve these three issues, the objective of this paper is to improve the accuracy of Quality Function Deployment, optimize and develop the customer requirements approach to attenuate risks in subsequent phases and in manufacturing process to increase industrial performance. This approach is based on Fuzzy sets theory and Alpha-cut operations, Pairwise comparison method, and fuzzy ranking and clustering method, and on theory of inventive problems solving (TRIZ).