A lifecycle-based risk assessment framework for prefabricated building supply chains using G1-entropy weight and cloud matter-element models

Abstract

Purpose: Prefabricated buildings (PB) are increasingly promoted for short construction cycles, environmental benefits and low-carbon characteristics. However, the growing complexity of PB supply chains, including fragmented coordination, information gaps and high interdependency among stakeholders, has introduced significant risks that conventional risk assessment approaches do not adequately address. This study aims to develop a dedicated risk evaluation framework that reflects the distinctive features of PB supply chains and supports more effective management and decision-making. Design/methodology/approach: A comprehensive risk indicator system was developed, consisting of 7 primary and 21 secondary indicators covering internal and external risks across the full lifecycle of PB projects. The G1 method and entropy weight method were combined to determine indicator weights by integrating expert judgment with data-driven variability. Matter-element analysis and cloud modeling were applied to evaluate and visualize risk levels. A real-world PB project in Fuzhou, China, was used as a case study to validate the model. Findings: The results indicated that PB supply chain risks were primarily internal. Component design was identified as the most critical factor influencing overall risk. Among the secondary indicators, design deficiencies had the highest impact. The overall risk level of the case project was classified as low. Sensitivity analysis confirmed the significant influence of design-related factors on supply chain stability, demonstrating the validity and applicability of the proposed framework. Originality/value: This study introduces an integrated and adaptable risk assessment model tailored to PB supply chains. It improves understanding of risk structures in prefabricated construction and provides a practical tool for early identification and proactive mitigation of risks. The findings also support sustainability goals by enabling more efficient resource allocation and reducing the need for rework and waste generation throughout the supply chain.