Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

Yuan, Cheng; Ming, Xinguo; Chen, Zhihua; Zhou, Tongtong; Liao, Xiaoqiang; Song, Wenyan

doi:10.1016/j.aei.2022.101823

Cited by 7 publications

(5 citation statements)

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“…To more systematically describe the service mode under this trend, this study focuses on the concept of a SISE as an extension and improvement of smart IPS 2 and smart PSS under ecological thinking. Unlike smart IPS 2 , which is mainly developed for customers who own industrial products/equipment, SISE is designed around complex industrial contexts [3]. The service components should be identified through business or industrial contexts from economic, social, and environmental perspectives.…”

Section: Sise: a Smart Service Solution For A Complex Industrial Contextmentioning

confidence: 99%

“…The existing research gaps motivated the authors to apply the SISE as an extension and a more result-oriented form of traditional smart IPS 2 and aims to explore the effective method of its modular design. The authors' previous study [3] mentioned that the SISE contains multiple industrial functional levels and has the characteristics of smart service and complex industrial systems. SISE covers two major types of aspects.…”

Section: The Sise Model and An Overview Of The Proposed Frameworkmentioning

confidence: 99%

“…However, this still mainly expands service activities from product-related activities. The SISE can provide a systematic industrial solution and typically involve the service value co-creation at broader levels, including the equipment/product, executive line, factory, and enterprise management [3]. This requires the service designer to construct a novel and effective service blueprint.…”

Section: The Sis Components Identification Using the Sisib (Stage I)mentioning

confidence: 99%

“…This calls for the company to have a holistic context or "ecological" level of thinking in providing industrial services and systematically consider the requirements of the stakeholders. In order to handle the service requirements in complicated smart and connected industrial contexts, this study focuses on the detailed modular design of a smart industrial service ecosystem (SISE) [3]. Previous research related to the SISE concept contains several aspects.…”

Section: Introductionmentioning

confidence: 99%

“…Their research also points out that the smart service ecosystem should go beyond just centering a core smart product. Chang et al [3] systematically elaborated on the concept of SISE, and their research especially emphasized the importance of achieving a detailed service design.…”

Section: Introductionmentioning

confidence: 99%

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Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

et al. 2023

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Compared with the conventional industrial product–service system, the smart industrial service ecosystem (SISE) mentioned in this study contains more service activity according to the characteristics of the industrial context, participation of various stakeholders and smart interconnected technologies. This study proposes a detailed modularization design framework for SISE, which can be referenced in various industrial contexts. Firstly, the context-based smart industrial service identification blueprint (SISIB) is proposed to describe the operation model of SISE and identify the service components. The SISIB can ensure that the designers understand the service and work process of the system and improve or carry out the smart industrial service (SIS) component identification. In the case of this article, SIS components from different industrial levels can be systematically identified. Secondly, smart collaboration and sustainable development principles are proposed for measuring the correlation degree among the service components. Considering the complexity and multi-level distribution nature of service components, the hyperedge concept is presented to realize the correlation comparison among the service components, and the evaluation linguistics is applied to handle the decision uncertainties. With this method, the effective correlation comparison between service components can be formed with few hyperedges. Thirdly, the hypergraph clustering theory is applied to define the SISE service module partition. The triangular fuzzy number is first used in hyperedge strength evaluation to comply with the vague linguistics from service design experts. The normalized hypergraph cut principle is realized using the K nearest neighbors (kNN) algorithm, and with this method, the new unified hypergraph and related Laplace matrix can be obtained. Then, the relevant eigenvalue of that Laplace matrix is gained, and the component clustering visualization is realized using the k-means algorithm. After the clustering is performed, several modular design schemes can be gained. In order to select the best modularization scheme, we referenced the modularity concept and realized the quality measurement for the modular design using hypergraph modularity criteria. Regarding these three steps, a detailed modularization case study for a renewable electricity service ecosystem design is presented to verify the viability and feasibility of the study in service modular design. The result showed that the framework in this study can realize the visible and clearance service component identification in a smart connected multi-level industrial context. The modular design scheme based on hypergraph can also achieve high modularity with a more convenient correlation evaluation.

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Section: Sise: a Smart Service Solution For A Complex Industrial Contextmentioning

confidence: 99%

Section: The Sise Model and An Overview Of The Proposed Frameworkmentioning

confidence: 99%

Section: The Sis Components Identification Using the Sisib (Stage I)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations