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A product-service system using requirement analysis and knowledge management technologies

A product-service system using requirement analysis and knowledge management technologies

Zhu, Haihua, Gao, Xiaoyu ORCID: 0000-0001-5625-3654 and Cai, Qixiang (2015) A product-service system using requirement analysis and knowledge management technologies. Kybernetes, 44 (5). pp. 823-842. ISSN 0368-492X (doi:

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– Product-service system (PSS) has been attracting attentions of global manufacturing to providing high-value added services in addition to their traditional product development and manufacturing business. For this reason, it is of great importance to research PSS. The purpose of this paper is to establish a systematic strategy and a system tool for PSS design.


– A requirement-driven product-service system (RdPPS) is developed using requirements analysis and knowledge management technologies. A framework is proposed to support RdPPS by providing tools and methods for requirement analysis and processing, formalization of PSS by ontology-based knowledge representation, reasoning method for PSS solution finding, and solution optimizing and assessing. Finally, the design support strategies for RdPPS are investigated to demonstrate the usability and functioning of the developed system.


– Many conventional design methods did not consider the influence of customer requirements (CRs) during the planning phase of PSS design. Moreover, a broader range of knowledge is required to PSS design, since both products and services are considered.

Research limitations/implications

– This research provides a solid foundation for PSS, and promotes an effective means for PSS design.


– A RdPSS is presented. CRs are considered during the design phase of PSS as well as both product and service knowledge.

Item Type: Article
Uncontrolled Keywords: Product Service Systems, Maintenance, Information Systems
Faculty / Department / Research Group: Faculty of Engineering & Science > Centre for Innovative & Smart Infrastructure
Last Modified: 02 Nov 2016 14:53
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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