Risk assessment model using conditional probability and simulation: case study in a piped gas supply chain in Brazil

Abstract

The objective of this article is to present a proposed application for systematic risk assessment considering the dependence between risks. The proposal relies on a systematic literature review (SLR) as the initial phase, in which the risk classes, management phases and the tools that can be applied to the risk assessment are identified, considering the dependence between them. For this, the system adopted includes the identification and later evaluation of the risks. The evaluation involves the analytic network process (ANP), Monte Carlo Simulation and conditional probability by means of Bayes’ theorem. The identification and evaluation of the risks were applied to two links of a piped gas supply chain in Brazil, identified as company X and Y, where six specialists were interviewed in each company in the managerial areas. The ANP indicted that the most critical risk in the links is the demand risk. From this, it was possible through Monte Carlo Simulation to identify the probability of occurrence of events with connection to demand risk: demand (X) / demand risk (Y), with probability of 10%; price risk (X) / demand risk (Y), with probability of 0.64%; and risk of supply (Y) / demand risk (X), with a probability of 0%. This indicates that the highest risk is the risk of demand of firm Y, and therefore mitigation strategies should focus on this risk, as it represents the true cause of supply chain vulnerability, generating risk with the highest probability.

Keywords:

• risk management
• risk assessment
• supply chain risk management (SCRM)
• Monte Carlo simulation
• analytical network process (ANP)

Acknowledgements

This study was partially supported by the National Council for Scientific and Technological Development (Process No. 303350/2018-0), Sao Paulo Research Foundation (Process No. 2018/14433-0), and the Office for the Improvement of Higher Education Personnel – CAPES (Case No. 9183/2012).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.