Bridging the semantic gap in customer needs elicitation: A machine learning perspective

The elicitation of customer needs (CNs) is a critical step in product development. However, these needs are often expressed in ambiguous, simple language and not in the form of well-defined specifications, causing a semantic gap in the product development process. Traditional methods to bridge the gap rely heavily on human action. Product development teams need to manually link CNs to product specifications in an ad hoc manner. This may be infeasible for large product variant spaces or evolving product families. We propose a machine learning mechanism to automatically bridge the semantic gap. This task is considered as a classification problem, with CNs being the class. The mapping function from product specifications to CNs is learned from training data by using a support vector machine and decision tree classifier. Given a new product variant, the learnt classifier can determine the needs that the product variant can satisfy. Numerical experiments show that the proposed method can achieve very high mapping accuracy. It can also shield product development teams from the tedious labour of linking CNs to product variants, and thus improve the efficiency of needs elicitation. Copyright © 2017 ICED.