РАЗРАБОТКА ЭКОНОМИКО-МАТЕМАТИЧЕСКОЙ МОДЕЛИ ВЗАИМОСВЯЗИ КАЧЕСТВА ПРОГНОЗИРОВАНИЯ И ПОКАЗАТЕЛЕЙ УСТОЙЧИВОСТИ ПРОМЫШЛЕННОГО ПРЕДПРИЯТИЯ

Abstract:
The article is devoted to the problem of quantifying the impact of forecasting accuracy of key financial indicators on the level of economic stability of an industrial enterprise. Based on a multiplicative time series model with a seasonal component, a revenue forecast was obtained and forecast quality metrics were calculated. The information base of the study was made up of quarterly data on the company’s revenue for 2018-2025 and the annual accounting statements of four food industry enterprises for 2021-2025. A system of sustainability indicators has been formed, including coefficients of liquidity, autonomy and investment coverage. The integral sustainability index is calculated using the principal component method based on six financial coefficients characterizing solvency, availability of own working capital and the capital structure of the enterprise. Using correlation and regression analysis, an economic and mathematical model was constructed that reflects a statistically significant relationship between the average relative forecast error and the integral sustainability index. An inverse correlation has been established between the magnitude of the average absolute percentage forecast error (MAPE) and the stability index (r = -0.61). The constructed regression model showed that an increase in MAPE by 1 percentage point, all other things being equal, is accompanied by a decrease in the stability index by an average of 0.215 units, while the variation in forecast accuracy and the autonomy coefficient explains 89% of the variability of the integral indicator. The model was tested based on the reporting data of food industry enterprises. The results obtained confirm the possibility of quantifying the relationship between the quality of revenue forecasting and the financial stability of an enterprise and allow us to determine the expected effect of improving the accuracy of planned calculations. The results can be used to justify management decisions aimed at improving the reliability of planned calculations and strengthening financial stability.