This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the air transport industry. The present paper focuses on the development of a model for medium-term flight demand estimation by flight destinations. This is based on the analysis of historical airline data on dates, departure times, and passenger demand, as well as the consideration of the influence of macroeconomic indicators, namely gross regional product (GRP), median per capita income, and population of departure and arrival points. This paper reviews international experience in the development of demand forecasting models and their use for resource planning in the industry. The developed model was evaluated using historical data on demand for a single turnaround flight operated by an airline. The developed model allows for the forecasting of the distribution of potential demand for airline flight destinations in the medium term, utilizing comprehensive historical data on departure times and flight demand by destination.

With the transition to a post-industrial economy, knowledge has emerged as a critical driver of economic activity. Consequently, the accumulated intellectual capital of a consulting company is the main factor determining its market position and operational effectiveness. Amid the growing influence of intellectual capital on the profitability of consulting companies and its essential role in creating sustainable competitive advantages within the knowledge economy, developing a methodology for assessing the relationship between intellectual capital efficiency and profitability in Russian consulting firms has become increasingly relevant. The study proposes a methodology based on the application of an econometric modeling apparatus using a multiple regression model. The simulation analysis utilizes a dataset including 730 observations for the period from 2015 to 2021 for Russian consulting companies. The dependent variable is net return on sales, while intellectual capital efficiency is measured by the coefficient of added value of intellectual capital and its components: human capital efficiency, structural capital efficiency and capital employed efficiency. The model also includes control variables that reflect company-specific and region-specific differences. The simulation was carried out using a fixed-effects model. The results reveal that there is a positive relationship between the coefficient of added value of intellectual capital and the coefficient of net return on sales. Additionally, the human capital efficiency indicator exhibits a predominantly positive but gradually decreasing marginal effect of on the net return on sales. It was noted that there is a positive relationship between the efficiency ratio of capital employed and the coefficient of net return on sales. The findings highlight the importance for managers of Russian consulting companies to pay attention to knowledge management practices in order to enhance the effective utilization of intellectual capital and improve financial performance.

Risk modeling and assessment are an integral part of companies' activities. This is especially important in the context of assessing the impact of various risks on the performance of Russian companies that account for a signification portion of state revenue in the country. In this regard, it is relevant to assess the impact of direct tax risks on the short-term financial policy of a vertically integrated oil company using the author's methodology. The proposed methodology is based on the use of the Monte Carlo probabilistic modeling method, the Prophet time series forecasting tool and financial analysis methods. At the first stage, time series forecasting is carried out for the parameters that determine the greatest variability in the amount of the mining tax. Next, the most likely outcomes for each parameter are modeled using the Monte Carlo probabilistic modeling method. At the third stage, considering the calculations obtained earlier, the amount of the mineral extraction tax for each time is calculated as part of the short-term financial policy of the vertically integrated oil company Gazprom Neft PJSC. At the fourth stage, the planned financial indicators of the company are calculated using trend analysis. At the final stage, the impact of tax risks on the short-term financial policy of the oil company is assessed by recalculating financial indicators. As a result, tax risks were modeled for Gazprom Neft in the short term, problems that would arise as a result of the implementation of these tax risks were identified, and recommendations were made on how to neutralize and mitigate them. According to the results of the analysis of the structure of the mining tax, it was observed that the most significant variability for it is set by the change in the price of Urals crude oil, the US dollar exchange rate, and the average price of an export alternative for diesel and gasoline fuels of class 5. The results also show that the integration of direct tax risk assessment into the short-term financial planning process makes it possible to not only minimize the negative impact of potential changes in the tax burden but also optimize internal cash flow management processes.