Knowledge Sharing and Productivity Improvement: An Agent-Based Modeling Approach
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 7
Abstract
Labor productivity is a major determinant of project performance in construction. Models of labor productivity in construction tend to focus on learning curve theories that assume learning is an individual process with no transfer of knowledge among crew members. This paper seeks to extend theories of individual learning to capture the crew dynamics present on construction sites. Accordingly, this paper presents an agent-based model aimed at deriving the impacts of crew composition and project schedule on knowledge sharing and, thus, on task duration. The proposed model was calibrated using field observations of 201 interactions among 12 construction workers at a construction project in Beirut, Lebanon. The results indicate that more diverse crews witness higher levels of knowledge sharing and greater productivity gains. The results also suggest that schedules keeping all the workers busy eliminate the potential for knowledge sharing and thus only benefit from the baseline gains seen in individual learning. This work contributes to the literature by developing an agent-based model that simulates knowledge sharing in the construction industry at the worker level. The study is limited by its exclusion of multiskilled workers.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request. The observation data from the construction site is available as a matrix with the numbers of interactions between anonymized crew members. The agent-based model is available as code in AnyLogic.
Acknowledgments
The authors would like to thank the American University of Beirut’s University Research Board for funding this study under project number 24709, award 103604.
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Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 7 • July 2020
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©2020 American Society of Civil Engineers.
History
Received: Jul 22, 2019
Accepted: Jan 27, 2020
Published online: May 6, 2020
Published in print: Jul 1, 2020
Discussion open until: Oct 6, 2020
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