Project Risk Management from the bottom-up: Activity Risk Index

Paper: arXiv 2406.00078

Authors: Fernando Acebes, Javier Pajares, Jose M Gonzalez-Varona, Adolfo Lopez-Paredes

Abstract

Project managers need to manage risks throughout the project lifecycle and, thus, need to know how changes in activity durations influence project duration and risk. We propose a new indicator (the Activity Risk Index, ARI) that measures the contribution of each activity to the total project risk while it is underway. In particular, the indicator informs us about what activities contribute the most to the project’s uncertainty so that project managers can pay closer attention to the performance of these activities. The main difference between our indicator and other activity sensitivity metrics in the literature (e.g. cruciality, criticality, significance, or schedule sensitivity indices) is that our indicator is based on the Schedule Risk Baseline concept instead of on cost or schedule baselines. The new metric not only provides information at the beginning of the project, but also while it is underway. Furthermore, the ARI is the only one to offer a normalized result: if we add its value for each activity, the total sum is 100%.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 4.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper introduces the Activity Risk Index (ARI) to evaluate activity-level contributions to schedule risk using Monte Carlo simulations. While conceptually sound and practical for project controls, the mathematical framework relies on standard statistical indices and lacks extensive empirical backtesting on real-world market or industry datasets in the provided text.

Research Flowchart

  flowchart TD
    A["Research Goal: Develop real-time metric for activity contribution to total project risk"] --> B["Inputs: Activity Durations & Schedule Risk Baseline (SRB)"]
    B --> C["Calculate Activity Risk Index (ARI) during project execution"]
    C --> D["Normalize ARI values (Sum of all activities = 100%)"]
    D --> E["Identify high-uncertainty activities for targeted management"]
    E --> F["Outcomes: Continuous risk tracking & superior focus compared to traditional sensitivity metrics"]

Cited References (42)

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3. Earned value in project management (2003) — link
4. On the sensitivity of project variability to activity mean duration (1999) — link
5. Measuring the efficiency of project control using fictitious and empirical project data (2011) — link
6. The computational complexity of the criticality problems in a network with interval activity times (2002) — link
7. Criticality in Stochastic Networks (1992) — link
8. On the dynamic use of project performance and schedule risk information during projecttracking (2010) — link

0 of 42 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.