Abstract
Identifying risks ensures that organizations systematically find out where potential losses may occur. A particular aspect is that risk events should reflect multiple perspectives of losses and be evaluated in different categories of risk at different levels. In this case, sorting risk events improves perception and helps decide how best to choose strategies to mitigate levels of risk events. However, uncertainties arising from the system's external and internal parameters prevent managers from precisely managing such a strategy. For example, in the natural gas pipeline context, parameters of the infrastructure, limits placed on resources, and demand may change over time, thereby limiting the ability of the organization to meet its objectives, which for risk-based purposes is to minimize losses. In other words, the level of risk events may vary due to uncertainties in the assessment model. Therefore, investigating uncertainty in risk-based models is crucial, and an experimental evaluation should be carried out before making decisions. For this reason, it is crucial to establish appropriate risk prioritization procedures that detect uncertainty factors and estimate the variability in risk behavior concerning categories of risk and the impacts caused. A suitable way to study variability is by conducting a thorough Sensitivity Analysis (SA). SA in risk-based models is useful for investigating the influence of parameters on measurements of risk, thus generating information on the uncertainty levels of different parameters, and how measurements of risk change decisions. This study presents a risk categorization model that supports managers in developing suitable mitigation plans according to the levels of risk based on sensitivity information. The multidimensional risk assessment and categorization model (MRAC) integrates Utility Theory and the ELECTRE TRI multicriteria method to sort sections of a gas pipeline by their level of risk. Based on a Monte Carlo Simulation experiment, the input parameters of MRAC are explored, thus generating information on sensitivity levels of the sections with the potentiality of increasing and decreasing their risk category. Finally, the sensitivity information is deployed in an efficient visualization that helps practitioners understand the uncertainties that should be controlled, thereby improving the process of mitigating risks. The contributions of this paper are set out based on how to establish critical information to control the variability of risk categories and prevent losses with regard to people, the environment, and the organization.
Keywords: Risk analysis, Risk categorization, Sensitivity Analysis, Gas pipeline, Multicriteria Deci
