Application of Joint Confidence Level analysis and Decision Trees method in quantitative risk management for public investment projects in Peru

Cuiro E.A.E. · 2021 7th Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2021 - Conference Proceedings (2021) · DOI: 10.1109/coniiti53815.2021.9619657

Resumen

The current global scenario that has caused the pandemic forces us to take into account the uncertainty of the various external factors that affect our decisions. Although several measures are being taken for the economic recovery of each country, there are still preexisting management problems that must be solved in order to give continuity to the flow of public investment that Peru is promoting. Given that the construction sector has a considerable impact on the economy, this sector being the most affected by the uncertainty inherent to the life cycle of each project cannot be ignored. Up to 366 public infrastructure construction projects are paralyzed, and despite an increase in resources for public spending, a change in traditional project management is needed to reduce the number of hindersome events and lack of financing in the state's project portfolio. The application of risk management methodologies and quantitative modeling such as JCL analysis provides the ability to anticipate foreseeable and unforeseeable events under a preventive approach that allows taking early and necessary action to ensure the country's economic development. This paper summarizes the author's research on the cost and schedule variability of a portfolio of construction projects in the public sector, and recommends the use of quantitative risk management, through JCL analysis and decision tree method as the best alternative to reduce the cost and schedule impacts of any project. The author presents a case study that demonstrates the benefit of the stochastic approach to ensure success and reduce project uncertainty.

Instituciones: Universidad Católica de Santa María