Process Mining, Modeling, and Management in Construction: A Critical Review of Three Decades of Research Coupled with a Current Industry Perspective
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 11
Abstract
The so-called digital transformation of the construction industry is essential to overcoming long-standing global productivity stagnation. This transformation aims to adopt the latest technological developments and methodologies to improve construction productivity while supporting data-informed decision-making. However, the construction sector has fallen short of meeting the fast-growing population’s demands for sustainable quality infrastructure at the required pace as it has not yet taken full advantage of these advancements. Despite broad experience in managing projects, when it comes to modeling, monitoring, and re-engineering processes, the construction industry has fallen behind several other industries. To overcome these challenges, efficient construction processes and operational strategies are essential to keeping organizations competitive and meeting market demands. In this regard, even though several studies on process modeling and management in construction exist, research on construction process improvement and automation through data-driven process mining remains understudied. Moreover, the literature lacks a comprehensive review of process-oriented studies with practical industry insights. To fill these gaps, this paper aims to provide an exhaustive analysis of process mining, modeling, and management as reported by the most current state of the literature in the architecture, engineering, construction/facility management (AEC/FM) domain coupled with a current industry perspective. As a result, the authors: (1) propose a conceptual process classification framework that considers the broad spectrum of process-oriented studies in the existing literature; (2) identify construction processes commonly present across a project’s life cycle; (3) design and conduct structured interviews with subject matter experts to validate identified processes and get industry insights about them; (4) spot major literature gaps describing future research opportunities; and (5) develop a business process model canvas template that supports construction organizations in improving their corporate memory and pursuing construction productivity growth by better managing, monitoring, and automating construction processes.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC). This study was funded by NSERC through RGPIN/6697-2017. Also, we deeply thank our industry partner, the industry experts who responded to our questionnaire, and Daria Khadir for assisting with the interviews.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 150 • Issue 11 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Nov 10, 2023
Accepted: May 13, 2024
Published online: Aug 28, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 28, 2025
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