Managing logistics customer service under uncertainty: An integrative fuzzy Kano framework

A logistics customer service model is a critical competitive advantage that enhances both customer satisfaction and firm performance. Researchers have developed several models for assessing customer requirements, measuring product performance, and positioning products. However, handling customers’ linguistic preferences and uncertain product attributes remain significant and unresolved problems. In this study, we develop an integrative framework that incorporates the Kano model, fuzzy distances, and 2-tuple fuzzy-linguistic model to manage customer-service logistics more effectively. Following a five-module architecture, we consider numerical, fuzzy, and linguistic data on product attributes and customer requirements. We first evaluate product attributes using utility-value functions and converted into satisfaction scores related to Kano categories. We then consider raw importance assessments to obtain an overall satisfaction score for each market and product. An empirical example illustrates the benefits of this integrative approach. The results show that our proposal can effectively manage logistics customer service, enabling managers to identify targets and formulate competitive strategies to enhance customer satisfaction.