International Construction Specialty Conference of the Canadian Society for Civil Engineering (ICSC) (5th : 2015)
Predictive modeling of prefabrication feasibility for the United States electrical contracting firms Said, Hisham M.
Electrical contractors have promoted offsite prefabrication after experiencing its potential in improving their operations. However, prefabrication is not a one solution that fits all. Accordingly, there is a need to develop better understanding of prefabrication feasibility for electrical contractors by analyzing its operational requirements and surrounding industry factors. The objective of this paper is to identify and model the determinants of electrical contractors’ prefabrication feasibility within the U.S. industry context, which can be used to predict the viability of prefabrication as a production approach for individual electrical contracting firms. The methodology of this study included four main phases. First, a qualitative analysis was performed to initially understand current prefabrication operations and practices of electrical contractors through a set of semi-structured interviews, site visits of prefabrication facilities, and prefabrication case studies. Second, a quantitative data collection task was performed by: 1) acquiring the internal business variables of a sample of electrical contractors using an online questionnaire; 2) complementing the questionnaire data with location-based economic data to represent external industry-related variables. Third, the collected data was used to develop and validate a binary logistic regression model that relates the prefabrication feasibility to its significant determinants. Fourth, a sensitivity analysis was performed for the developed model to provide a larger understanding of electrical construction prefabrication feasibility beyond the collected data. The developed predictive model provides useful insights about prefabrication feasibility dependency on union relations, labor conditions, market competition, supply chain relations, and building information modeling.
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