Simulation-Based Methodology for Strategic Risk Assessment in Fuel and Energy Sector Companies

The article proposes a methodology that integrates methods, models, algorithms, and tools for predictive strategic risk assessment in fuel and energy sector (FES) companies to support managerial decision-making under uncertainty in the short, medium, and long term . The methodology rests on scientifically grounded premises, assumptions, and constraints: the financial position of an FES company is treated as an indicator of the cumulative impact of strategic threats, and the indicators describing this position are modeled by a joint lognormal distribution. The study provides a theoretical rationale for using the mode of the multivariate distribution of FES companies’ financial indicators as the industry benchmark vector. To improve the quality and reliability of the methodological basis for quantitative strategic risk assessment, it substantiates an integrated measure based on the Mahalanobis distance. This distance captures the deviation of a multivariate vector describing an FES company’s financial position from the industry benchmark. The article also develops a method for determining strategic risk levels by identifying statistically significant deviations of companies’ financial indicator vectors from this benchmark using the proposed integrated measure. Simulation modeling is used to analyze changes in strategic risk levels under different scenarios of financial indicator dynamics in FES companies. The proposed Monte Carlo algorithm uses revenue as the controllable stress-testing variable and evaluates changes in the integrated strategic risk measure under controlled revenue variations and simulated uncertainty. Simulation results an therefore serve as a basis for developing strategic threat profiles and assessing strategic risks in fuel and energy sector companies. Calculations based on model-generated data demonstrate the viability of the proposed methodology, and its testing confirms the feasibility of practical implementation.

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