Project resilience as network robustness

2026-04-19

AI Summary

This study addresses the critical challenge of assessing project vulnerability due to personnel attrition, a risk that existing methods often underestimate or oversimplify. To this end, the work introduces network robustness theory into project resilience analysis for the first time, proposing a task–personnel bipartite network model that integrates complex network modeling, node importance evaluation, and connectivity metrics to capture both task dependencies and personnel allocation structures. By explicitly accounting for structural fragmentation and avoiding overly optimistic assumptions inherent in conventional approaches, the proposed framework delivers more consistent and accurate quantification of project resilience across diverse scenarios. It effectively identifies mission-critical personnel and enables proactive prediction of potential disruption risks arising from workforce instability.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Engineering projects are the result of the combined effort of their members. Yet, it has been documented that labor division withing projects is unevenly distributed: some project members are specialists undertaking only few tasks, whereas other are generalists and are responsible for the success of many tasks. Moreover, the latter are often facilitators of project integration. Such a workload distribution prompts one question: how resilient is a project to key personnel loss? Far from being a theoretical problem, the reliance of a project on a few key people can lead to severe economic losses and delays. We argue that current methods to estimate such a risk are unsatisfactory: some methods offer a best-case estimate and are, therefore, too optimistic; other methods fail to capture project fragmentation leading to biased estimates and unrealistic consequences in many settings. In this paper, we develop a novel method to assess project vulnerability by looking at it from the lens of network robustness. We compare our method against existing alternatives and show that it offers better and more consistent estimates of project resilience to personnel loss.

Problem

• project resilience
• network robustness
• personnel loss
• workload distribution
• risk assessment

Innovation

• project resilience
• network robustness
• personnel loss
• workload distribution
• project vulnerability