Enhancing construction management and procurement efficiency through a phased digital twin framework for smart infrastructure projects

This study proposes a phased digital twin framework aimed at enhancing construction management and procurement efficiency in smart infrastructure projects. The framework integrates building information modeling (BIM), Internet of Things (IoT) sensors and predictive analytics to facilitate real-time decision-making across the design, execution and procurement phases.

Using structural equation modeling (SEM) for data analysis, the study establishes causal relationships between digital twin adoption and improvements in procurement lead time, resource utilization and cost predictability.

The findings underscore the significance of phased implementation, highlighting that incremental adoption leads to more sustainable outcomes and organizational learning.

Digital twin (DT) technology is emerging as a transformative force in the construction industry, enabling real-time data integration, predictive analysis and lifecycle optimization. However, its application in construction management and procurement remains fragmented, particularly in emerging markets where infrastructure expansion is rapid but constrained by inefficiencies. This research contributes to the construction management literature by offering a structured digitalization roadmap and aligning operational practices with the Sustainable Development Goals (SDGs).

No previous Studies on digital twins relating to construction management and procurement efficiency is done to ours knowledge.