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Optimal BIM staffing in construction projects using a queueing model
BIM staff plays the important role of providing required information from digital models to project participants. A dilemma is determining the appropriate capacity of BIM staff without sacrificing efficiency in terms of human resource costs. Despite the importance of BIM staffing, its effects on the...
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Published in: | Automation in construction 2020-05, Vol.113, p.103123, Article 103123 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | BIM staff plays the important role of providing required information from digital models to project participants. A dilemma is determining the appropriate capacity of BIM staff without sacrificing efficiency in terms of human resource costs. Despite the importance of BIM staffing, its effects on the productivity and performance of construction projects have rarely been investigated. This study presents a framework for quantifying the impact of BIM staffing. First, this study proposes a method to evaluate the activities of BIM staff by introducing a time-dependent queueing model. Second, the proposed framework is validated with data from three real-world projects in which BIM was applied. The effects of BIM staffing on the waiting time of project participants are quantitatively analyzed using time and cost indicators. The results show that decisions about optimal BIM staffing can be made by considering the waiting costs of project participants.
•A method to evaluate the activities of BIM staff by introducing a time-dependent queueing model was proposed.•The proposed framework is validated with data from three real-world projects in which BIM was applied.•The effects of BIM staffing are quantitatively analyzed using time and cost indicators•Optimal BIM staffing can be judged using the waiting costs of project participants. |
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ISSN: | 0926-5805 1872-7891 |
DOI: | 10.1016/j.autcon.2020.103123 |