A novel data-driven graph-based requirement elicitation framework in the smart product-service system context

Wang, Zuoxu; Chen, Chun‐Hsien; Zheng, Pai; Li, Xinyu; Khoo, Li Pheng

doi:10.1016/j.aei.2019.100983

Cited by 119 publications

(41 citation statements)

References 37 publications

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“…With the connection of SCPs and smart technologies, Smart PSS enables the system to gather and process massive information. A vast amount of product‐sensed and user‐generated data in different life cycle stages are continuously collected by various sensing devices, which are further used to help in decision‐making and development processes 29 …”

Section: Literature Reviewmentioning

confidence: 99%

“…A vast amount of product-sensed and user-generated data in different life cycle stages are continuously collected by various sensing devices, which are further used to help in decision-making and development processes. 29 Correspondingly, Smart PSS is identified as a data-driven value co-creation business paradigm, which is consisted of stakeholders and smart systems, for example, smart sensing, smart products, and smart services. A number of stakeholders are involved in the value creation process, such as the system developers, product manufacturers, service providers, and business partners.…”

Section: Smart Pss and Value Creationmentioning

confidence: 99%

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A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

Zhu

2021

Int J Intell Syst

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Smart product‐service systems (Smart PSS) are receiving increasing attention due to their potentials to satisfy customer needs and create sustainability to meet hybrid concerns. As a value co‐creation business paradigm, various sustainable value propositions (SVPs) and their assessments come from stakeholders' subjective judgments, which values are imprecise, vague, and even inconsistent. The reliability of these values also varies with the confidence and cognitive bias of the respective stakeholder. How to evaluate these co‐creative SVPs to ensure an objective and reliable result under such uncertain environments becomes a critical issue. To fill this gap, this paper proposes a rough‐Z‐number extended multi‐criteria group decision‐making framework integrating the rough‐Z‐number, DEMATEL (decision making trial and evaluation laboratory), and group decision‐making strategy to evaluate the co‐creative SVPs for Smart PSS. A novel rough‐Z‐number is presented to deal with the uncertainty and reliability representation of the stakeholder's individual assessment as well as the aggregation and subjectivity manipulation of these risk assessments from different stakeholders. A rough‐Z‐number extended DEMATEL is then proposed to determine the risk prioritization of the co‐creative SVPs. Experimental results and comparative studies demonstrate the superiority of the proposed rough‐Z‐number‐based DEMATEL in handling the uncertainty and reliability characterization as well as the subjectivity manipulation during the evaluation of the co‐creative SVPs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Smart Pss and Value Creationmentioning

confidence: 99%

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

Zhu

2021

Int J Intell Syst

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“…Recently, some holistic views on data-driven requirements elicitation have been presented: [3] proposed an approach for integrating information originating from different IS using a domain ontology to serve as the baseline for the data model. [27] is similar in the sense that it relies on a domain ontology, with the addition of a high-level proposal for storing the final outcome of the elicited requirements originating into a connected graph. Previous efforts differ from ours as they do not focus on external data sources, nor do they encompass algorithms for data processing.…”

Section: Data-driven Requirements Elicitationmentioning

confidence: 99%

Holistic data-driven requirements elicitation in the big data era

Henriksson

Zdravković

2021

Softw Syst Model

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Digital transformation stimulates continuous generation of large amounts of digital data, both in organizations and in society at large. As a consequence, there have been growing efforts in the Requirements Engineering community to consider digital data as sources for requirements acquisition, in addition to human stakeholders. The volume, velocity and variety of the data make requirements discovery increasingly dynamic, but also unstructured and complex, which current elicitation methods are unable to consider and manage in a systematic and efficient manner. We propose a framework, in the form of a conceptual metamodel and a method, for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources that aims to support data-driven requirements elicitation and management. The usability of the framework is partially validated by an in-depth case study from the business sector of video game development.

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“…Moreover, knowledge engineering approaches (e.g. knowledge graph) should also be adopted appropriately to represent and query the needs from the specific context [7].…”

Section: Fundamentals Of Sustainable Ispssmentioning

confidence: 99%

Industrial smart product‐service system development for lifecycle sustainability concerns

Zheng

Peng

et al. 2020

IET Collaborative Intelligent Manufacturing

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A novel data-driven graph-based requirement elicitation framework in the smart product-service system context

Cited by 119 publications

References 37 publications

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

A rough‐Z‐number‐based DEMATEL to evaluate the co‐creative sustainable value propositions for smart product‐service systems

Holistic data-driven requirements elicitation in the big data era

Industrial smart product‐service system development for lifecycle sustainability concerns

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