Risk transmission and control in the prefabricated construction multi-project supply chain network: a SIS modeling approach
Xu Ren et al.
Abstract
Purpose The prefabricated construction supply chain (PCSC) is vulnerable to disruption from unforeseen events due to the precise coordination and transportation demands among various sections. This study aims to develop a prefabricated construction multi-project supply chain (PCMSC) risk transmission model to simulate and explain the mechanism of risk transmission in PCMSC. Design/methodology/approach This study constructs a complex network based on contract data of prefabricated construction (PC) projects from a well-known Chinese real estate development company. Four indicators – dependence level, resistance capacity, adaptation capacity and resilience – are incorporated into the SIS model to better adapt to the characteristics of PCSC. When unforeseen events occur, risk transmission paths in PCMSC can be simulated, and suppliers with potential risks can be identified. Findings The premise for risk transmission between projects is to have a common supplier. A high supplier localization rate can mitigate the risk of cross-regional transmission. A strong strategic partner can effectively defend against risk transmission from other areas. Moreover, improving enterprise resilience can effectively control risk transmission. Originality/value This study provides theoretical guidance for the holistic and dynamic analysis of risk transmission in the PCMSC. Meanwhile, this study develops an application framework of the epidemic model for supply chain management research, with specific adaptation to the characteristics of PCMSC and validation in practical case scenarios. In practice, PC project owners can apply the model for risk monitoring and control both in regular production and in unforeseen events.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.