Abstract
Abstract
Completing large-scale projects on time is a daunting challenge, partly due to the intricate network of dependencies between a project’s activities. To support this challenge, existing theory focuses on predicting whether a delay in completing a single activity is likely to spread and impact downstream activities. Using fine-grained information from 68 546 activities and 84 934 pairs, associated with the delivery of a $1.86Bn infrastructure project, we show that the core mechanism that underpins existing theory underestimates delay propagation. To elucidate the mechanisms that drive delay, we generated null models that destroyed the structural and temporal correlations of the original project activity network. By doing so, we argue that this underestimation is the result of neglecting endogenous structural features within the project’s activity network. Formulating a new mechanism that utilizes both temporal and structural features may help improve our capacity to predict how delays spread within projects.
Funder
H2020 Innovation In SMEs
HORIZON EUROPE Marie Sklodowska-Curie Actions
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems