Customer needs management for product innovation : segmentation modeling and ontology-based approaches
Date of Issue2013
School of Mechanical and Aerospace Engineering
Customer satisfaction has always been important for a product. Attaining customer satisfaction requires identifying genuine customer needs and establishing the relationship between customer needs and product features. Involving innovative customers in the product development process has been considered an effective way for customer needs management. However, previous studies are limited in innovative customer identification. In particular, they ignore the mismatching problems in customer needs representation between customer needs and intangible product characteristics. To address these limitations, there is a need for an integrated framework that is able to cover both the customer domain and product design domain at the stages of customer needs identification and product specification establishment in product development life cycle. This thesis aims at building a novel customer needs management system (CNMS) that provides the link between the customer domain and product design domain. The customer needs management system is established based on an Involvement-Thinking-Feeling (ITF) segmentation model and an ontology-learning customer needs representation (OCNR) system. The ITF model can be used to study the innovative characteristics of customers as well as help identify large groups of customers with different degrees of innovativeness. Using the OCNR system, high level customer needs related to low level product characteristics can be represented by extracting non-taxonomic relations between concepts. Furthermore, the CNMS can also help realize automated customer needs acquisition, translation and representation. Three case studies were conducted to validate the CNMS. The results from these case studies indicated that the ITF model can help identify more innovative customers in an easier way. In particular, 59 innovative customers are identified by the ITF model while at most 25 lead users (i.e., passionate customer) could be found using the lead user criteria. The customer needs ontology derived from the proposed system contained more semantics compared with those from the existing ontology learning systems. Specifically, 204 non-taxonomic relations were extracted from the customer needs ontology while only 24 from the expert-generated ontology. Besides, the hit rate of the customer needs ontology for the concepts and relations were around 80% which is considered high enough so that most of the useful information can be accounted for by the extracted concepts and semantic relations. In addition, the non-taxonomic relations generated by the OCNR system received the lowest noise score compared to those by the existing ontology learning systems. All these findings suggested that the proposed CNMS can provide useful inputs for the succeeding stages in the product development process.
DRNTU::Engineering::Industrial engineering::Engineering management