A design method for edge–cloud collaborative product service system: a dynamic event-state knowledge graph-based approach with real case study

Maolin Yang, Yuqian Yang and Pingyu Jiang

International Journal of Production Research, 2024, vol. 62, issue 7, 2584-2605

Abstract: Product service system (PSS) is an effective approach to achieve win–win situation between service providers and consumers, and the design of PSS is the first step of its application. However, on the one hand, PSS is relatively an abstract concept, and what exactly should be considered when designing a PSS still require further exploration; on the other hand, the fast development of information and web technologies bring both opportunities and challenges for PSS design. For example, how to efficiently design the smart & connected products that support remote monitoring and control of service operation, how to build the dynamic service activity flow model that can be intuitively and visually read by human engineers and also can be conveniently deployed on computers, and how to realise edge–cloud collaboration between service providers and consumers. In this regard, a service requirement-oriented four-step generic PSS design method is established, including service mode selection and structured service order generation → smart & connected service product configuration → dynamic event-state knowledge graph-based service activity flow and service resource network configuration → industrial internet-based edge–cloud collaborative service delivering. Finally, a real design case of carbon block grinding and polishing PSS is used for verification.ABBREVIATIONS: AS: assembly service; C: controlling targets or objectives; I: input; M: enabling methods or mechanisms; MRO: maintenance, repair, and operation; MS: maintenance service; O: output; OEE: overall equipment effectiveness; PSS: product service system; PV: processing volume; RxEy-Sz: the relation from Event y to the State z of Service activity x; RxSz-Ey: the relation from the State z of Service activity x to Event y; StateAiCurrent: the current state of Service activity i; StateAiFuturek: the kth possible future state of Service activity i; StateAiHistoryj: the jth historical state of service activity i; StateEy: the state of Event y; Tq: the qth time point in the time line.

Date: 2024
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DOI: 10.1080/00207543.2023.2219345

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