Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Predicting Military Construction Project Time Outcomes Using Data Analytics

Predicting Military Construction Project Time Outcomes Using Data Analytics AbstractThrough its Department of Defense (DoD) agencies, and outside contractors, the USA invests billions of dollars each year in military construction (MILCON) projects. Although construction management expertise is gained and significant amount of data are collected from past projects, completing projects on time remains a challenge. This article uses data from 466 MILCON projects to identify key factors that influence project duration and provide a new model to predict project time outcomes. The model generates accurate results and serves as a useful tool in the early phases of a project life cycle. Another key contribution of this study is the employed methodology, which includes the use of available data, targeting of relevant parameters, and development of the predictive model. The contributed methodology is applicable outside of the MILCON domain with the appropriate data set and by targeting the relevant influential factors to create models to predict time outcomes of future projects. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Management Journal Taylor & Francis

Predicting Military Construction Project Time Outcomes Using Data Analytics

Predicting Military Construction Project Time Outcomes Using Data Analytics

Engineering Management Journal , Volume 30 (4): 15 – Oct 2, 2018

Abstract

AbstractThrough its Department of Defense (DoD) agencies, and outside contractors, the USA invests billions of dollars each year in military construction (MILCON) projects. Although construction management expertise is gained and significant amount of data are collected from past projects, completing projects on time remains a challenge. This article uses data from 466 MILCON projects to identify key factors that influence project duration and provide a new model to predict project time outcomes. The model generates accurate results and serves as a useful tool in the early phases of a project life cycle. Another key contribution of this study is the employed methodology, which includes the use of available data, targeting of relevant parameters, and development of the predictive model. The contributed methodology is applicable outside of the MILCON domain with the appropriate data set and by targeting the relevant influential factors to create models to predict time outcomes of future projects.

Loading next page...
 
/lp/taylor-francis/predicting-military-construction-project-time-outcomes-using-data-ZUymujTAnd

References (43)

Publisher
Taylor & Francis
Copyright
© 2018 Taylor & Francis
ISSN
2377-0643
eISSN
1042-9247
DOI
10.1080/10429247.2018.1490995
Publisher site
See Article on Publisher Site

Abstract

AbstractThrough its Department of Defense (DoD) agencies, and outside contractors, the USA invests billions of dollars each year in military construction (MILCON) projects. Although construction management expertise is gained and significant amount of data are collected from past projects, completing projects on time remains a challenge. This article uses data from 466 MILCON projects to identify key factors that influence project duration and provide a new model to predict project time outcomes. The model generates accurate results and serves as a useful tool in the early phases of a project life cycle. Another key contribution of this study is the employed methodology, which includes the use of available data, targeting of relevant parameters, and development of the predictive model. The contributed methodology is applicable outside of the MILCON domain with the appropriate data set and by targeting the relevant influential factors to create models to predict time outcomes of future projects.

Journal

Engineering Management JournalTaylor & Francis

Published: Oct 2, 2018

Keywords: Military Construction; Project Duration; Predictive Model; Data Analytics; Decision Making & Risk Management; Program & Project Management; Systems Engineering

There are no references for this article.