Artificial Intelligence in Construction Supply Chains: A Scientometric Review and Future Research Agenda
TL;DR
A data-centric and phased research agenda that emphasizes benchmark datasets, human–AI collaboration, lifecycle economic evaluation, explainable AI, and multi-stakeholder governance is proposed.
Abstract
Despite growing interest in artificial intelligence (AI) applications in the construction industry, the literature still lacks a consolidated understanding of how AI functions across the full spectrum of construction supply chain processes. Existing studies are dispersed across different technologies, project stages, and application contexts, making it difficult to identify the intellectual structure of this field, the main areas of AI application, and the barriers that continue to constrain practical implementation. To address this gap, this study conducts a systematic review of AI applications in construction supply chains by combining scientometric analysis with qualitative content synthesis. A total of 212 journal articles retrieved from Scopus were analyzed using VOSviewer-based scientometric analysis and qualitative content synthesis. The scientometric analysis maps annual publication trends, keyword co-occurrence patterns, co-cited sources, influential documents, and collaboration networks. The qualitative synthesis further examines how AI supports construction supply chain management across three broad themes: procurement and production optimization, logistics and material management, and collaborative decision-making for resilience and sustainability. The findings show that AI has been primarily applied to demand forecasting, resource optimization, logistics coordination, contract and document processing, computer vision-based monitoring, and multi-agent decision support. However, its practical diffusion remains constrained by fragmented and low-quality data, limited empirical validation, high implementation costs, algorithmic opacity, cybersecurity risks, and unresolved governance and liability issues. Based on these findings, this study proposes a data-centric and phased research agenda that emphasizes benchmark datasets, human–AI collaboration, lifecycle economic evaluation, explainable AI, and multi-stakeholder governance. The study contributes to the literature by integrating fragmented AI-related research into a structured knowledge map and by clarifying future pathways for developing intelligent, transparent, and resilient construction supply chains.