New Product Developments

Abstract

As the development of the consuming environment, the way consumers give feedback on product experience changes from passive feedback to active reviews, and the development of artificial intelligence technology brings new possibilities for companies to obtain information on Customer Requirements (CRs) and Market Competition Information (CIs) needed for new product development. In the previous New Product Development (NPD) process, CRs and CIs often need to be corrected through market surveys and questionnaires. To acquire the corrected data from experienced experts without user and designer cognitive bias is very laborious and more subjective. Under the situation of key information shortage for business developers and NPD efficiency & accuracy cannot be guaranteed, online reviews provided a good source for data analysis to enhance the market competition. This study integrates Latent Dirichlet Allocation (LDA), Apriori algorithm, interval grey number, and Quality Function Deployment(QFD) techniques to propose the Latent Dirichlet Allocation-Interval Grey Number Quality Function Deployment (LDA-IGQFD) method, which can use text mining and analysis methods to obtain objective information about CRs and CIs contained in users’ online reviews, transformed them into data and input into Interval Grey Number Quality Function Deployment (IGQFD) to drive product development. LDA-IGQFD can help product developers identify CRs and Engineering Characteristics (ECs) important information and provide suggestions for product development. The aim of the research process under the LDA-IGQFD method is explained with the design of a dishwasher as an example, and the effectiveness and practicality of the proposed method is scientifically verified.