When does it end? Monte Carlo Simulation Applied to Risk Management in Defense Logistics’ Procurement Processes

  • Abel de Castro Laudares University of the Air Force – UNIFA
  • Maria Filomena Fontes Ricco University of the Air Force – UNIFA
  • Rodrigo Antônio Silveira dos Santos University of the Air Force – UNIFA
Keywords: Logistics, Risk Management, Monte Carlo Simulation

Abstract

Goal: The main objective of this research was to evaluate the application of the Monte Carlo simulation, in comparison with other estimation methodology, in order to verify if it would be possible to apply this tool to improve the risk perception of deadlines during a procurement process for logistical support of defense projects.

Methodology: By means of collected data from 2015 to 2017, a comparison was conducted among deadlines of real procurement processes versus simulated processes with the use of the Monte Carlo simulation.

Results: The performance analysis of the Monte Carlo simulation suggests that it could be possible to apply it to the first phase of risk management in order to improve the risk perception of deadlines during a procurement process for logistical support of defense projects.

Limitations of the investigation: The main limitation of the research relates to the database analyzed, which involved three different modalities of Bidding Processes.

Practical implications: The data from this research reveals that the Monte Carlo simulation can be used as an effective tool for advising the decision maker and, mainly, the managers of internal control about aspects of corporate risk management in public organizations.

Originality/Value: This research presents an original contribution to corporate governance related to risk management, which is not commonly seen in the Brazilian public sector.

Published
2019-03-07
How to Cite
Laudares, A., Ricco, M. F., & Silveira dos Santos, R. A. (2019). When does it end? Monte Carlo Simulation Applied to Risk Management in Defense Logistics’ Procurement Processes. Brazilian Journal of Operations & Production Management, 16(1), 149-156. https://doi.org/https://doi.org/10.14488/BJOPM.2019.v16.n1.a14
Section
Articles