A rough-fuzzy approach integrating best-worst method and data envelopment analysis to multi-criteria selection of smart product service module

The revolutionary development and implementation of smart technologies have triggered the manufacturers' servitization trend towards smart product service system (PSS). Accurate selection of smart product service (SPS) module is critical to successful planning and development of smart PSS concept. This study constructs a list of criteria for SPS module selection from the perspectives of service implementation, value symbiosis and smart capability. The selection can be deemed as a multi-criteria decision-making process including two parts: weight determination of criteria and module ranking, in which the intrapersonal linguistic ambiguousness and interpersonal preference randomness are involved. The best-worst method (BWM) is widely acknowledged as an efficient method for weight determination due to its superiority in quickly finding optimal weight with scant decision data. The data envelopment analysis (DEA) method is proven feasible to prioritize alternatives with cost-based and benefit-based criteria. However, these two methods cannot handle the uncertainties involved in the selection process which may lead to imprecise results. Moreover, the previous research rarely studies simultaneous handling of these two types of uncertainty in the realm of BWM and DEA. Therefore, the current study proposes a novel rough-fuzzy BWM-DEA approach to SPS module selection, with fully capturing both the intrapersonal and interpersonal uncertainties. The application of the proposed approach in the smart vehicle service module selection and the comparisons with other methods demonstrate the validity and effectiveness of the proposed approach. (C) 2020 Elsevier B.V. All rights reserved.