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.
Highlights
Delivery of construction projects is notoriously challenging, with cost and duration escalations being typical across the entire industry
We showed that task perturbations occur irrespective of project size or task duration
We used a unique dataset of 14 large-scale engineering projects with activity networks and delay data to study how activity network properties relate to project performance
Summary
Delivery of construction projects is notoriously challenging, with cost and duration escalations being typical across the entire industry. Poor project performance is unlikely to be the result of bad practice, since the relationship between widely recognised variables that impact performance has long been researched and acted upon (e.g., how uncertainty in the duration of project’s activities impacts the overall project delivery time) [4]. To explain this disparity between theory and practice, recent work in both academia [5,6,7,8,9,10] and industry [11, 12] has proposed a new, independent variable that impacts project performance: project complexity
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