A hybrid framework integrating rough-fuzzy best-worst method to identify and evaluate user activity-oriented service requirement for smart product service system. (20th April 2020)

• Record Type: Journal Article
• Authors: Chen, Zhihua; Ming, Xinguo; Zhou, Tongtong; Chang, Yuan; Sun, Zhaohui
• Is Part Of: Journal of cleaner production. Volume 253(2020)
• Publication Date: 2020-04-20
• Subjects: Smart product service system -- User activity-oriented smart service requirement -- Requirement identification -- Requirement evaluation -- Rough-fuzzy best-worst method; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.119954
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: With the wide application of the smart technologies in the field of product service system, the delivery scope of product-extension service can be expanded from the conventional product operation lifecycle to broader user activity cycle. The identification and evaluation of user activity-oriented service requirements are critical to successful design of smart technology-enabled product service offering. The evaluation process of service requirement generally involves the intrapersonal and interpersonal uncertainties that may lead to inaccurate evaluation results, since the evaluation needs to collect linguistic judgements from multiple stakeholders. However, most the existing research contains scant study of the systematic approach to elicitation and evaluation of the smart service requirement under an environment of multiple uncertainties. Therefore, this paper proposes a hybrid framework to identify and prioritize the user activity-oriented service requirements. The proposed identification method is used to map the key activity elements with the smart capabilities for purpose of eliciting the service requirements in user activity cycle. Moreover, a novel rough-fuzzy best-worst method is proposed to prioritize the identified requirements, simultaneously manipulating the intrapersonal and interpersonal uncertainties. The case study results of smart vehicle service system show that sixteen smart service requirements are identified in the self-driving tour with the smart vehicle, and the requirement "alerting the driver's unsafe behavior using informative diagnostic capability" emerges as the most important one in the proposed rough-fuzzy best-worst method. Moreover, the obtained evaluation results present more accuracy and objectivity compared with the crisp-based, fuzzy-based and rough-based best-worst method. Highlights: A framework is proposed to identify and evaluate service requirement of smart PSS. An elicitation method mapping smart capability to service requirement is presented. Fuzzy logic, rough set and best-worst method are integrated to evaluate requirement. Intrapersonal vagueness and interpersonal diversity are simultaneously manipulated. Showed the method's feasibility, accuracy and potentials with smart vehicle PSS.