Paper: arXiv 2406.02589

Authors: Fernando Acebes, M Pereda, David Poza, Javier Pajares, Jose M Galan

Abstract

The aim of this paper is to describe a new an integrated methodology for project control under uncertainty. This proposal is based on Earned Value Methodology and risk analysis and presents several refinements to previous methodologies. More specifically, the approach uses extensive Monte Carlo simulation to obtain information about the expected behavior of the project. This dataset is exploited in several ways using different statistical learning methodologies in a structured fashion. Initially, simulations are used to detect if project deviations are a consequence of the expected variability using Anomaly Detection algorithms. If the project follows this expected variability, probabilities of success in cost and time and expected cost and total duration of the project can be estimated using classification and regression approaches.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents an applied project management framework extending Earned Value Management (EVM) with Monte Carlo simulation and basic statistical learning (anomaly detection, classification, regression). While the mathematical derivations are light and accessible, the application of machine learning techniques to stochastic project control offers practical novelty, supported by a clear step-by-step methodology.

Research Flowchart

  flowchart TD
    A["Research Goal: Integrated Project Control under Uncertainty"] --> B["Data Input: Monte Carlo Simulations of Project EVM"]
    B --> C["Step 1: Anomaly Detection (Statistical Learning)"]
    C --> D{"Is Project Deviation Expected Variability?"}
    D -- Yes --> E["Step 2: Classification & Regression Models"]
    D -- No --> F["Flag Out-of-Control Risk"]
    E --> G["Key Outcomes: Success Probabilities & Cost/Duration Forecasts"]

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0 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.