Statistical Analysis of the Effectiveness of Management Programs in Improving Construction Labor Productivity on Large Industrial ProjectsSource: Journal of Management in Engineering:;2016:;Volume ( 032 ):;issue: 001DOI: 10.1061/(ASCE)ME.1943-5479.0000375Publisher: American Society of Civil Engineers
Abstract: The purpose of this research effort is to identify the effectiveness of a particular set of important management programs in improving construction labor productivity. These programs were previously defined through industry experts and research consensus as having significant impact on project cost, schedule, safety, scope, and quality performance, but their relationship to labor productivity was not known. Through statistical analyses of the database maintained by the Construction Industry Institute’s Benchmarking and Metrics Committee, headquartered in Austin, Texas, United States, the research presented in this article examined whether projects with high levels of implementation of programs related to front-end planning, materials management, automation and integration of information systems, team building, constructability, and safety, experienced better labor productivity among the mechanical, electrical, concrete, and structural steel trades compared to projects with low levels of implementation. The result showed that the majority of the investigated management programs were statistically significantly correlated to better construction labor productivity, and the significance was different among various craft trades. The paper’s major contribution to the overall body of knowledge is that it comprehensively quantifies the potential relationship of implementing project management programs to labor productivity on large industrial projects.
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contributor author | Yongwei Shan | |
contributor author | Dong Zhai | |
contributor author | Paul M. Goodrum | |
contributor author | Carl T. Haas | |
contributor author | Carlos H. Caldas | |
date accessioned | 2017-05-08T22:25:55Z | |
date available | 2017-05-08T22:25:55Z | |
date copyright | January 2016 | |
date issued | 2016 | |
identifier other | 44647954.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/80529 | |
description abstract | The purpose of this research effort is to identify the effectiveness of a particular set of important management programs in improving construction labor productivity. These programs were previously defined through industry experts and research consensus as having significant impact on project cost, schedule, safety, scope, and quality performance, but their relationship to labor productivity was not known. Through statistical analyses of the database maintained by the Construction Industry Institute’s Benchmarking and Metrics Committee, headquartered in Austin, Texas, United States, the research presented in this article examined whether projects with high levels of implementation of programs related to front-end planning, materials management, automation and integration of information systems, team building, constructability, and safety, experienced better labor productivity among the mechanical, electrical, concrete, and structural steel trades compared to projects with low levels of implementation. The result showed that the majority of the investigated management programs were statistically significantly correlated to better construction labor productivity, and the significance was different among various craft trades. The paper’s major contribution to the overall body of knowledge is that it comprehensively quantifies the potential relationship of implementing project management programs to labor productivity on large industrial projects. | |
publisher | American Society of Civil Engineers | |
title | Statistical Analysis of the Effectiveness of Management Programs in Improving Construction Labor Productivity on Large Industrial Projects | |
type | Journal Paper | |
journal volume | 32 | |
journal issue | 1 | |
journal title | Journal of Management in Engineering | |
identifier doi | 10.1061/(ASCE)ME.1943-5479.0000375 | |
tree | Journal of Management in Engineering:;2016:;Volume ( 032 ):;issue: 001 | |
contenttype | Fulltext |