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Investigating profitability performance of construction projects using big data: A project analytics approach

Bilal, Muhammad; Oyedele, Lukumon O.; Kusimo, Habeeb O.; Owolabi, Hakeem A.; Akanbi, Lukman A.; Ajayi, Anuoluwapo O.; Akinade, Olugbenga O.; Davila Delgado, Juan Manuel

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Authors

Muhammad Bilal Muhammad.Bilal@uwe.ac.uk
Associate Professor - Big Data Application

Lukumon Oyedele L.Oyedele@uwe.ac.uk
Professor in Enterprise & Project Management

Habeeb Kusimo Habeeb.Kusimo@uwe.ac.uk
Research Associate - Digital Construction with Big Data

Hakeem Owolabi Hakeem.Owolabi@uwe.ac.uk
Associate Professor - Project Analytics and Digital Enterprise

Dr Lukman Akanbi Lukman.Akanbi@uwe.ac.uk
Associate Professor - Big Data Application Developer

Anuoluwapo Ajayi Anuoluwapo.Ajayi@uwe.ac.uk
Associate Professor - Big Data Application

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Manuel Davila Delgado Manuel.Daviladelgado@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence



Contributors

Abstract

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities of existing project intelligence tools used within the industry. Several tasks in the project lifecycle bear implications for the efficient planning and delivery of construction projects. Setting up right profit margins and its continuous tracking as projects progress are vital management tasks that require data-driven decision support. Existing profit estimation measures use a company or industry wide benchmarks to guide these decisions. These benchmarks are oftentimes unreliable as they do not factor in project-specific variations. As a result, projects are wrongly estimated using uniform rates that eventually end up with entirely unusual margins either due to underspends or overruns. This study proposed a project analytics approach where Big Data is harnessed to understand the profitability distribution of different types of construction projects. To this end, Big Data architecture is recommended, and a prototype implementation is shown to store and analyse large amounts of projects data. Our data analysis revealed that profit margins evolve, and the profitability performance varies across several project attributes. These insights shall be incorporated as knowledge to machine learning algorithms to predict project margins accurately. The proposed approach enabled the fast exploration of data to understand the underlying pattern in the profitability performance for different types of construction projects.

Journal Article Type Article
Acceptance Date Jun 23, 2019
Online Publication Date Jul 3, 2019
Publication Date Nov 1, 2019
Deposit Date Sep 5, 2019
Publicly Available Date Sep 5, 2019
Journal Journal of Building Engineering
Electronic ISSN 2352-7102
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 26
Article Number 100850
DOI https://doi.org/10.1016/j.jobe.2019.100850
Keywords Mechanics of Materials; Civil and Structural Engineering; Safety, Risk, Reliability and Quality; Architecture ; Building and Construction
Public URL https://uwe-repository.worktribe.com/output/2780305
Additional Information This article is maintained by: Elsevier; Article Title: Investigating profitability performance of construction projects using big data: A project analytics approach; Journal Title: Journal of Building Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jobe.2019.100850; Content Type: article; Copyright: © 2019 The Authors. Published by Elsevier Ltd.
Contract Date Sep 5, 2019

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