Uncovering the fragility of large-scale engineering projects
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13688_2021_Article_291.pdf
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1.95 MB
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Author(s)
Santolini, Marc
Ellinas, Christos
Nicolaides, Christos
Date Issued
July 8, 2021
Publisher
Springer Berlin Heidelberg
Citation
EPJ Data Science. 2021 Jul 08;10(1):36
Version
Final published version
Abstract
Abstract
Engineering projects are notoriously hard to complete on-time, with project delays often theorised to propagate across interdependent activities. Here, we use a novel dataset consisting of activity networks from 14 diverse, large-scale engineering projects to uncover network properties that impact timely project completion. We provide empirical evidence of perturbation cascades, where perturbations in the delivery of a single activity can impact the delivery of up to 4 activities downstream, leading to large perturbation cascades. We further show that perturbation clustering significantly affects project overall delays. Finally, we find that poorly performing projects have their highest perturbations in high reach nodes, which can lead to largest cascades, while well performing projects have perturbations in low reach nodes, resulting in localised cascades. Altogether, these findings pave the way for a network-science framework that can materially enhance the delivery of large-scale engineering projects.
MIT Department
Sloan School of Management
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjds/s13688-021-00291-w
