Optimisation of resource management in construction projects: A big data approach

Kusimo, Habeeb; Oyedele, Lukumon; Akinade, Olugbenga; Oyedele, Ahmed; Abioye, Sofiat; Agboola, Alirat; Mohammed-Yakub, Naimah

Abstract

Purpose – The purpose of this paper is to identify challenges faced in resource management in the UK construction industry and to propose some solutions to these problems. Design/methodology/approach – Based on a qualitative research methodology, 14 experts from the UK construction industry were chosen to be participants in the study. The participants were equally divided into two focus groups to discuss resource management using five projects as case studies. Thematic analysis of the discussion reveals seven key factors that affect resource management. Findings – The results show that most of the problems identified are due to poor data management processes and the practice of having data in silos. Overcoming this challenge requires the adoption of big data approaches for resource management to allow the integration of large and different forms of data. Originality/value – This study seeks to bring to the fore challenges faced in resource management by the UK construction industry and to outline some solutions to address them.

Citation

Kusimo, H., Oyedele, L., Akinade, O., Oyedele, A., Abioye, S., Agboola, A., & Mohammed-Yakub, N. (2019). Optimisation of resource management in construction projects: A big data approach. World Journal of Science, Technology and Sustainable Development, 16(2), 82-93. https://doi.org/10.1108/WJSTSD-05-2018-0044

• Journal Article Type: Article
• Acceptance Date: Dec 6, 2018
• Online Publication Date: Jan 21, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Dec 10, 2018
• Publicly Available Date: Feb 8, 2019
• Journal: World Journal of Science, Technology and Sustainable Development
• Electronic ISSN: 2042-5953
• Publisher: Emerald
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 2
• Pages: 82-93
• DOI: https://doi.org/10.1108/WJSTSD-05-2018-0044
• Public URL: https://uwe-repository.worktribe.com/output/853616
• Publisher URL: https://doi.org/10.1108/WJSTSD-05-2018-0044
• Additional Information: Additional Information : This is the author's accepted manuscript. The final published version is available here: https://doi.org/10.1108/WJSTSD-05-2018-0044