Abstract

Prefabricated construction has garnered widespread attention worldwide. As an important part in purchasing management, prefabricated component suppliers (PCSs) undertake the bulk of the work and responsibility in the supply chain from on-site to off-site. Therefore, it is crucial and necessary to establish a decision-making framework to comprehensively evaluate the performance of PCSs. This study proposed a set of performance indicators for PCSs, including component quality, cost, delivery capability, service level, enterprise development potential, and enterprise cooperation potential. A hybrid method was established to evaluate the integrated performance of PCSs based on Analytic Hierarchy Process (AHP)–entropy weight and cloud model. It integrated the AHP and entropy weight method to calculate indicator weights, while the cloud model was employed to transform qualitative characteristics into quantitative ones. To verify the feasibility of this method, an empirical study was subsequently conducted using a typical case from China. The obtained results demonstrate that the overall performance of Supplier A lies at the “good” level, with the similarity index between the comprehensive cloud model and the standard cloud model within the good range, at 0.4045. Among the six primary indicators, quality performance scored the highest at 0.65, meeting the “excellent” standard. It can be seen that the hybrid approach of AHP–entropy weight and cloud model accurately and effectively demonstrates the integrated performance of PCSs. The main aim of this study was to establish a comprehensive system and develop a novel approach for evaluating the performance of PCSs within a hesitant fuzzy environment. The findings of this study can provide guidelines for researchers and the public to evaluate PCS performance, contributing significantly to the fields of supply chain management and construction engineering. Moreover, it provided a practical tool for professionals in the industry to enhance the supplier selection processes.

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