paper-with-me

Papers

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

2024-05-31 · Fernando Acebes, José Manuel González-Varona, Adolfo López-Paredes, Javier Pajares

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.

📄 PDF Abstract BibTeX arXiv:2405.20679

Code (0)

등록된 구현이 없습니다.

Tasks

Management

Similar Papers 제목 키워드 기반

Use of Bayesian Network characteristics to link project management maturity and risk of project overcost

2020-09-18 · Felipe Sanchez, Davy Monticolo, Eric Bonjour, Jean-Pierre Micaëlli

The project management field has the imperative to increase the project probability of success. Experts have developed several project management maturity models to assets and improve the project outcome. However, the cu…

Management

Near-Optimal Smoothing of Structured Conditional Probability Matrices

2016-12-01 · NeurIPS 2016 12 · Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky

Utilizing the structure of a probabilistic model can significantly increase its learning speed. Motivated by several recent applications, in particular bigram models in language processing, we consider learning low-rank …

MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction

2024-04-30 · Marlon Steiner, Marvin Klemp, Christoph Stiller

There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap necessitates advancements in prediction be…

motion predictionPrediction

Projecting U.S. coastal storm surge risks and impacts with deep learning

2025-06-16 · Julian R. Rice, Karthik Balaguru, Fadia Ticona Rollano, John Wilson 외

Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial i…

Deep Learning

Stability and Sharper Risk Bounds with Convergence Rate $O(1/n^2)$

2024-10-13 · Bowei Zhu, Shaojie Li, Yong liu

The sharpest known high probability excess risk bounds are up to $O\left( 1/n \right)$ for empirical risk minimization and projected gradient descent via algorithmic stability (Klochkov \& Zhivotovskiy, 2021). In this pa…