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Our team's published research papers forming the foundation of our technology.
Dr Christos ellinas

Neglecting complex network structures underestimates delays in a large-capital project

The study looks at completing big projects on time, specifically focusing on how delays in one activity affect others. Contrary to current theories, the research suggests that these delays are underestimated because they ignore certain structural features in the project's network. The study proposes a new approach that considers both timing and structure to better predict and understand how delays spread in projects.

Alexei Vazquez, Iacopo Pozzana, Georgios Kalogridis & Christos Ellinas

Activity networks determine project performance

Projects have a critical path, a sequence of activities crucial for on-time project completion. Project managers often focus intensely on this path, overlooking the broader network structure's impact on it. Using a generative model and data from 77 projects (totalling over $10 billion), the study reveals that the network structure extends beyond the critical path.

The proposed duplication-split model predicts consistent patterns in real projects, emphasising that delay propagation in project schedules is a network property, not limited to the critical path.

Christos Ellinas, Christos Nicolaides, Naoki Masuda

Mitigation strategies against cascading failures within a project activity network

Ensuring timely project delivery is crucial for addressing societal challenges, yet projects are challenging due to their complex and interconnected nature, making them prone to cascading failures. The study develops a cascading failure model and tests it on a temporal activity network from a large engineering project.

Evaluating six mitigation strategies, the research surprisingly finds that, in most cases, the temporal properties of activities are more crucial than their structural properties in preventing large-scale cascading failures. These findings suggest new approaches for designing and scheduling projects to naturally limit the impact of cascading failures.

Christos Ellinas, Marc Santolini, Christos Nicolaides

Uncovering the fragility of large-scale engineering projects

Engineering projects often face challenges in timely completion, with delays spreading across interconnected activities. Using data from 14 diverse large-scale projects, the study uncovers perturbation cascades, where delays in one activity impact up to 4 downstream activities, causing significant overall project delays.

Perturbation clustering is identified as a crucial factor, with poorly performing projects experiencing major disruptions in high-reach nodes, leading to large cascades. In contrast, well-performing projects have perturbations in low-reach nodes, resulting in localized cascades. The findings suggest a network-science framework for improving the delivery of large-scale engineering projects.

Iacopo Pozzana, Christos Ellinas, Georgios Kalogridis & Konstantinos Sakellariou

Spreading of performance fluctuations on real-world project networks

Understanding the impact of individual nodes is crucial in studying spreading processes on networks. The study introduces a novel metric, reachability-heterogeneity (RH), to quantify each node's contribution to network robustness against spreading processes. Using data from four large engineering projects, the study validates the RH metric, finding that nodes with low RH consistently perform better.

The comparison with seven other node metrics reveals the interdependence of RH with activity performance. The context-agnostic nature of RH, demonstrated with real-world data, emphasises the role of network structure in overall project performance.

Christos Ellinas

The Domino Effect: An Empirical Exposition of Systemic Risk Across Project Networks

Activity network analysis is commonly used for project risk management, assuming linear cause-and-effect relationships. This study challenges this assumption, using a computational framework on real-world project data to assess project systemic risk.

It finds that local failures can trigger cascading effects, with even small disruptions causing large systemic failures more frequently than anticipated.

The study attributes these failures to the topological and temporal features of activity networks, highlighting the inadequacy of local mitigation efforts. The findings contribute to a better understanding of the causal mechanisms behind large-scale project failures.

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