Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation

Paper: arXiv 2405.20679

Authors: Fernando Acebes, José Manuel González-Varona, Adolfo López-Paredes, Javier Pajares

Abstract

The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 5.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a practical quantitative alternative to qualitative risk matrices using Monte Carlo Simulation in project risk management. While the mathematical formulations are straightforward standard statistical distributions, the proposed empirical methodology offers a novel and practical approach to disentangling cost and schedule impacts for risk prioritisation.

Research Flowchart

  flowchart TD
    A["Goal: Address risk matrix limitations with quantitative risk prioritization"] --> B["Input: Define project activities, risk probability, and cost/duration impacts"]
    B --> C["Process: Run Monte Carlo Simulation (MCS) via MCSimulRisk"]
    C --> D["Analysis: Assess individual risk impacts on total cost vs. duration"]
    D --> E["Outcome: Differentiated risk ranking providing absolute metrics for cost & duration"]

Cited References (60)

1. Quantitative Analysis of Project Risks (2013) — link
2. Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques — → 2406.02589
3. Risk Matrix Integrating Risk Attitudes Based on Utility Theory (2015) — link
4. Further Thoughts on the Utility of Risk Matrices (2013) — link
5. Some Limitations of Qualitative Risk Rating Systems (2005) — link
6. Epistemic uncertainty quantification techniques including evidence theory for large-scale structures (2004) — link
7. Project risk analysis and management—PRAM the generic process (1997) — link
8. Market risk in commodity markets: a VaR approach (2003) — link

1 of 60 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.