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

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