A Novel Hybrid Methodology to Study the Risk Management of Prefabricated Building Supply Chains: An Outlook for Sustainability

Tian Zhu () and Guangchen Liu
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Tian Zhu: School of Management, Shenyang Construction University, Shenyang Hunnan Hunnan Road No. 9, Shenyang 110167, China
Guangchen Liu: School of Management, Shenyang Construction University, Shenyang Hunnan Hunnan Road No. 9, Shenyang 110167, China

Sustainability, 2022, vol. 15, issue 1, 1-22

Abstract: The management of the prefabricated building supply chain involves the entire process of prefabricated buildings. There are many uncertain factors, and the risk factors in any link will affect the overall operation of the supply chain. In order to achieve the “dual carbon” goal as soon as possible and promote the sustainable development of the building supply chain, it is very important to study the risk management of the assembly building supply chain. The risk management of the prefabricated building supply chain involves risk recognition, risk prediction, risk assessment, and risk response. In this study, on the basis of literature research, the WBS-RBS (Work Breakdown Structure–Risk Breakdown Structure) method comprehensively uses the working link and risk type of the prefabricated building supply chain to establish an indicator system for risk factors in the prefabricated building supply chain. Then, the risk prediction and evaluation model of the neural network of BP (Back Propagation) through Python software is established to predict the risk of prefabricated building supply chains. After verification, it was found that the accuracy of the training set and test set reached 100% and 96.6667%. The results showed that the BP neural network had good effects on the risk forecast of the prefabricated building supply chain, which provided certain risk predictions for the risk prediction of the prefabricated building supply chain. For reference, on the basis of risk prediction, in order to explore the importance of risk factors to the results of BP neural network prediction results, the characteristic importance algorithm of machine learning replacement features further analyzes the risk factors of the prefabricated building supply chain. Finally, based on the prefabricated construction project of enterprise A, risk prediction and evaluation of its supply chain management were carried out, countermeasures for targeted risks were proposed, and we provided new research on the sustainable development of the assembled building supply chain to provide new research ideas.

Keywords: BP neural network; prefabricated building; supply chain management; sustainable development; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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