Aug 2026· Strategic decisions and risk management· 0 citations· 3 references
Abstract
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.
The current economic environment is characterized by profound volatility and unpredictability, posing significant threats to business survival. For the manufacturing sector, particularly animal feed producers, operations have been severely disrupted by geopolitical and economic shocks. Under these circumstances, traditional forecasting methods lose their reliability, creating a critical need for advanced analytical tools to support managerial decision-making. The purpose of this research is to explore and implement scenario planning as a strategic management instrument tailored for manufacturing activities under extreme uncertainty, focusing on the operational framework of LLC NVP Ukrzoovetprompostach. The relevance of the study stems from the urgent necessity of transitioning from reactive survival tactics to proactive resource management and technological modernization. To achieve this objective, the study employs a comprehensive set of general scientific and specific economic methods. Financial and ratio analyses are utilized to diagnose the liquidity, profitability, and efficiency of fixed asset utilization over a multi-year period. A comparative analytical method allows benchmarking the enterprise’s financial performance against direct industry competitors. Furthermore, a PEST analysis is conducted to map the macro-environmental forces affecting the national animal feed market. Finally, the scenario planning method is applied to synthesize diagnostic findings into actionable pathways. The results of the diagnostic phase revealed critical vulnerabilities within the target enterprise, including a substantial physical deterioration of fixed assets exceeding normative thresholds, insufficient absolute liquidity, and a lagging net profit margin. Based on these empirical findings, three divergent development scenarios were constructed for a one-year horizon. The optimistic scenario delineates a strategy of intensive technological growth, premised on favorable market conditions and external funding, allowing for the comprehensive replacement of outdated equipment. The baseline scenario models a gradual renewal strategy, relying on internal financing to target specific production bottlenecks. Conversely, the pessimistic scenario outlines a survival strategy in a deteriorating macroeconomic climate, necessitating strict cost-cutting measures, staff optimization, and the substitution of new equipment purchases with capital repairs. The practical value of the obtained scientific results lies in providing corporate management with a structured, adaptable matrix for decision-making. By integrating these specific scenarios into daily operations, managers can anticipate market shifts, allocate resources efficiently, and mitigate financial risks, ensuring the continuous optimization of production processes in a highly volatile environment.
The article examines the fuel and energy balance (FEB) as a tool for systemic management of all economic subsystems that influence the formation of the energy balance structure. The goal of the study is to develop a conceptual model for generating a forecast FEB, ensuring full alignment of fuel and energy sectors with macroeconomic targets by selecting the most effective management decisions at the national, regional, and district levels. A distinctive feature is the rationale for a multi-criteria assessment of the impact of management decisions in the fuel and energy sector and related sectors on the structural parameters of the forecast FEB. This will enable a quantitative analysis of the impact of alternative management decisions (program measures) on the key elements of the forecast FEB. This article presents a mathematical model for calculating the integral value of a structural gap in individual FEB subsystems caused by a specific decision option or set of program measures. It also presents a developed mathematical model for calculating the integral indicator of structural changes in FEB subsystems caused by the implementation of specific management decisions or development programs.
I. Osinovskaya· International journal of mat...· 0 citations
The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a quantitative, evaluative, and longitudinal three-phase design, the retrospective evaluation of nine preventive variables was employed using Six Sigma metrics (DPMO, Z, Y), along with the evaluation of overall performance through the Geometric Capability Indicator (GCI) and the Arithmetic Capability Indicator (ACI), and the stochastic modeling of the process using Markov chains. It was demonstrated that evaluating processes in isolation hides structural inefficiencies, as four variables showed an Excellent individual performance (Z≈6.0), but the comprehensive multivariate evaluation revealed a Deficient systemic state (GCI of 0.471 and ACI of 0.493). Furthermore, Markov modeling on the compliance of the process management index predicted a 100% probability of long-term stagnation in a deficient absorbing state (x1=1). It is concluded that the proposed method functions as a rational anticipation system that provides potential managerial benefits by offering early warning indicators of operational degradation, supporting corrective decision-making on unstable preventive indicators.
Tomás José Fontalvo Herrera, Enrique J. Delahoz-Domínguez, Neiser Rodelo Barrios· Engineer· 0 citations
This research aims to present the Framework for Enhancing Network Integrity eXcellence (FENIX) model, integrating technological, organizational, human, and economic dimensions to enhance risk assessment in complex socio-technical systems.
The FENIX model employs fuzzy logic, multi-criteria prioritization, and non-additive aggregation methods to enhance risk assessment in complex socio-technical systems. Its applicability is demonstrated through a case study involving the introduction of a new aircraft.
The findings indicate that technological risks can be reduced through diagnostic and monitoring systems, whereas operational and human factors continue to play a significant role. Training, communication, and fatigue management are essential for minimizing systemic vulnerability. The economic dimension highlights the feasibility of reconciling mitigation effectiveness with financial sustainability.
The methodology assists decision-makers by optimizing the RPN, improving RTO reliability, and incorporating ERI and EPNmetrics, thereby allowing organizations to align risk management strategies with operational resilience and financial constraints.
In contrast to conventional FMEA, the FENIX framework incorporates uncertainty, nonlinear interdependencies, and cost–benefit considerations, offering a more comprehensive and flexible approach to risk prioritization and resource allocation.
Giuseppe Caristi, Daniela Barba, Maria Frasca et al.· Management Decision· 0 citations
Introduction. Against the backdrop of ongoing changes in the financial and economic environment, growing uncertainty, digital transformation, and globalization, risk management is emerging as a key factor for business resilience and competitiveness. Traditional risk management approaches often prove insufficiently flexible in current turbulent conditions, underscoring the need for a transition towards comprehensive and adaptive systems capable of rapid response to new challenges.
Goal. The article aims to develop and substantiate the advantages of using the Dynamic Risk Integration Model (DRIM) in organization risk management system. The banking sector was identified as the main application area for the model, though it demonstrates broad adaptation potential across other economic sectors and management levels. The key objective of the model is to establish an integrated risk management system for financial institutions and real-sector enterprises, improving the precision of strategic risk assessment.
Materials and methods. The research was based on a comparative analysis of various risk management techniques and standards, including COSO ERM, ISO 31000, FAIR, System Dynamics, PMBOK, and SCRUM. By integrating the core principles and practices of these methodologies, the DRIM model was developed. It combines quantitative and qualitative analytical approaches, strategic planning, and operational flexibility.
Results and discussion. The effectiveness of comprehensive approaches is confirmed by the practices of leading Russian companies. DRIM represents a holistic model incorporating advanced practices from modern risk management standards. The model enables not only the identification and assessment of risks but also the integration of analysis results into the strategic goals of the company. It demonstrates versatility and potential for adaptation to various management levels and industry sectors, including financial services and information technology.
Conclusion. DRIM combines modern risk management methodologies, including adaptability, strategic planning, and quantitative analysis. It optimizes the overall system for managing banking risks, enhancing the resilience and competitiveness of banks in an unstable environment. DRIM encompasses methods for identifying, assessing, and managing banking risks, considers qualitative and quantitative aspects, and can be based on the use of machine learning, AI, and big data processing, making the system flexible to changes.
E. Grinko, D. V. Ivatin· Вестник Северо-Кавказского ф...· 0 citations
This study develops a scenario-based analytical framework for assessing smart microgrids in industrial zones as infrastructure investments and for analyzing multi-actor risk allocation and governance. No empirically validated return is reported for a specific Bulgarian industrial zone. Public institutional, market, financial, technology and environmental sources are instead used to benchmark the scenario assumptions. Three scenarios are evaluated for a reference zone with annual consumption of 12,000 MWh. The model incorporates photovoltaic (PV) degradation, battery round-trip efficiency of 85–90%, annual usable-capacity degradation, one modeled battery replacement within a 12–15-year service interval, component-based capital and operating expenditures, and an author-defined semi-quantitative likelihood–impact risk matrix. Net present value (NPV) is EUR −1,114,726 in the conservative scenario, EUR 697,836 in the baseline scenario and EUR 3,424,835 in the favorable scenario. Discounted payback is not achieved within 20 years in the conservative scenario and is approximately 14.1 and 7.0 years in the baseline and favorable scenarios, respectively. Deterministic one-at-a-time sensitivity analysis identifies electricity price, capital expenditures, and the direct PV self-consumption ratio as the dominant financial drivers.The indicative reduction in location-based emissions associated with grid electricity purchases is 802.9–1385.5 tonnes of carbon dioxide equivalent (tCO2e) per year under the selected grid-average electricity emission factor. The contribution lies in integrating public-data availability assessment, external parameter benchmarking, battery service life, degradation and replacement economics, investment appraisal, threshold analysis, semi-quantitative risk prioritization and contractual risk allocation within one reproducible framework. The outputs remain illustrative and require project-level validation using measured hourly loads, binding prices, financing terms and enforceable contracts.
Kiril Luchkov, Mihail Chipriyanov, Galina Chipriyanova et al.· Journal of Risk and Financia...· 0 citations
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