Probability Density Function for Predicting Productivity in Masonry Construction Based on the Compatibility of a Crew

Florez, Laura and Cortissoz, Jean (2017) Probability Density Function for Predicting Productivity in Masonry Construction Based on the Compatibility of a Crew. In: 25th Annual Conference of the International Group for Lean Construction, 9-12 July 2017, Heraklion.

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During the different phases of a masonry project, contractors collect detailed information about the labor productivity of its workers and the factors that influence productivity. Information includes quantitative data such as hours, activities, and tasks, and qualitative data such as ratings and personality factors. Personality factors have been found to be a key aspect that influences the compatibility of a crew and the productivity in masonry construction. This paper proposes a mathematical framework to determine how the compatibility between the workers in a crew can be used to predict productivity. A standard method for quantifying personality is used to determine the compatibility of a crew and empirically define a probability density to predict productivity. The probability density determines, for a given compatibility, the average productivity for a crew. The most interesting part of this probability density is that it accounts for variations in the productivity, resulting from the interaction and the relationships between the workers in a crew. The proposed probability distribution can be used to make more realistic predictions, by calculating confidence intervals, of the productivity of masonry crews and to better estimate times of construction, avoid crew conflicts, and find practical ways to increase production.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: masonry construction, productivity, process improvement, crew formation, probability density
Subjects: G100 Mathematics
K200 Building
Department: Faculties > Engineering and Environment > Architecture and Built Environment
Depositing User: Becky Skoyles
Date Deposited: 03 Oct 2017 14:57
Last Modified: 03 Oct 2017 14:57

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