Year: 2024
Type: article
Abstract: Globally, the construction sector suffers from low productivity levels due to a large proportion of the workforce consisting of low-skilled laborers. In order to integrate design and construction workflows with the objective of enhancing productivity, there is a considerable need to move from traditional methods to advanced techniques such as Building Information Modeling (BIM). Clear and convincing benefits are key to ensuring the viability of the BIM implementation process, especially for small to medium enterprises (SMEs) that need encouragement to transition to BIM. While the architectural, engineering, and construction (AEC) industry has recently embraced BIM, its adoption has been slow for decades, primarily due to the substantial financial risk, particularly for local SMEs. These SMEs must identify clear and obvious benefits to justify the high initial transition cost from traditional construction approaches to BIM-based methods. The high initial cost of implementing BIM systems in SME businesses limits its implementation among construction professionals. However, one appealing benefit that encourages the implementation and acceptance of BIM in the construction sector is its potential to improve labor productivity. Despite the development of methods to investigate the benefits of BIM in terms of labor productivity, empirical quantification of labor productivity remains challenging and complex due to BIM's innovations, project complexity, and the lack of standard, unified productivity measurements. This study aims to provide guidelines for construction players to understand how BIM is used in projects and apply best practices to prevent project delays and cost overruns while improving communication among project participants and enhancing labor productivity.
Institutions: Universiti Teknologi MARA, Universiti Teknologi Petronas
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Yayasan UTP
OpenAlex ID: W4402320686