UNISON framework for user requirement elicitation and classification of smart product-service system

Abstract

Smart product-service system (smart PSS) has become the manufacturers’ new revenue and sustainable competitiveness sources with the rise of digital servitization. To configure a customer-tailored smart PSS effectively, requirement analysis plays a significant role. Till now, the heterogeneity feature of smart PSS requirements, the usage of user-generated data, and data-driven paradigm have not been considered sufficiently. Hence, this study develops a UNISON framework based on the integration of deep learning approaches for eliciting and evaluating the user requirements for smart PSS with a systematic process, data-driven manner, and quantitative results. Adopting the user-centric perspective, this study employed massive user online reviews as the niche for requirement analysis. The bidirectional long short-term memory (Bi-LSTM) neural network and bi-term topic model (BTM) were employed to extract and elicit the smart PSS requirements in both product and service aspects. Further, a multi-dimensional and automatic requirement evaluation method integrating the sentiment analysis, IPA-Kano model, and opportunity algorithm was proposed for assessing, classifying, and prioritizing the identified requirement items. A smart PSS requirement landscape map was also developed to present the analysis results visually and guide decision-making. An empirical study has been conducted for the smart cleaning robot service system to prove the feasibility of the suggested approach. Enabled by the hybrid application of big data analytics and deep learning techniques, this study can empower the smart PSS provider to understand the user requirements comprehensively and upgrade the smart PSS design effectively.