Supply chain management and dispute modeling of construction projects by deep learning and principal component analysis

Abstract

As global economic integration progresses, supply chain management (SCM) has become integral across various sectors. The objective is to bolster SCM proficiency in China’s construction industry and to address the challenges and conflicts within the supply chain. The approach begins with an examination of the construction projects’ supply chain, followed by an analysis of its risk factors. Subsequently, a risk warning indicator system for the construction project’s supply chain is developed. This is followed by assigning weights to the combination of risk warning indicators, culminating in the creation of a comprehensive risk warning model. Finally, the risk warning model is substituted into an affordable housing project for testing. The test results manifest that the comprehensive weights of the first-level risk indicators of the supply chain of the construction project are 0.0475, 0.1299, 0.0686, 0.1439, 0.1099, 0.0526, 0.2067, and 0.2402, respectively. This finding underscores the significance of research in the context of China’s construction industry’s supply chain management (SCM) and risk warning systems. With a maximum correlation of 0.0725 between the risk warning level and the B level, indicating an intermediate warning state, it confirms the reliability of the risk warning model employed in construction project supply chains. Such evidence highlights the model’s relevance and potential as a reference for advancing SCM practices and enhancing risk warning capabilities within China’s construction sector.