Modeling customer satisfaction with new product design using a flexible fuzzy regression-data envelopment analysis algorithm

Abstract

• An integrated fuzzy regression-DEA method is developed for customer satisfaction modeling. • A method is proposed for setting the associated product design attributes. • The 16 FR models will model the relationship between customer satisfaction and new product design. • Proposed algorithm is tested based on a case study in a freezer/refrigerator industry. The success of new products depends greatly on customer satisfaction and meeting the customer needs is vital for new product development. By incorporating customer needs in the design and development process, organizations can improve productivity for their new products and reduce the risks associated with new product markets. Hence, design teams require methods to model customer satisfaction when setting the associated product design attributes. Thus, different approaches have been developed for modeling the relationship between customer satisfaction and product design parameters. In this study, 16 well-known fuzzy regression (FR) models are considered to understand the relationship between customer satisfaction and new product design. The design of FR models is based on the 4Ps marketing mix (product, price, place, and promotion) concept in fuzzy environments. A flexible algorithm is then presented based on the index of confidence, error measures, and data envelopment analysis for selecting the best FR model. The applicability and usefulness of the proposed algorithm is demonstrated experimentally based on an actual case study, where the flexible algorithm is employed to predict customer satisfaction with a new product design in the freezer / refrigerator industry.