О МОДЕЛИ ПРОГНОЗИРОВАНИЯ ВЫРУЧКИ ОРГАНИЗАЦИИ С БЫСТРЫМ РОСТОМ

Abstract

We consider a revenue forecasting model for fast-growing organizations. We use the logistic curve for approximation of the growth curve. The purpose of the work is to predict the development of fast-growing organizations using the logistic curve function. Materials and methods. A set of system dynamics simulation models is used to predict the indicators of organizations. A particular case of approximating the revenue forecast is the logistic curve as a variant of the sigmoid. The information basis of the study was the open data of the Federal State Statistics Service (Rosstat), the State Information Resource of accounting (financial) statements of the Federal Tax Service. The least squares method was used to identify the parameters. Results. We identified more than 1200 fast-growing organizations of all industries with revenue of more than 1000 million rubles. The criterion for identifying fast-growing organizations is a compound annual growth rate of revenue of more than 50% per year. According to the actual data, the following parameters of the logistics curve were identified: growth potential, growth rate, and inflection point. The example of a fast-growing food delivery company, “SberMarket” is considered in more detail. According to the model forecast, the marginal value of the logistics revenue curve will be about 700 billion rubles. Conclusion. A study of fast-growing organizations in Russia has been conducted. The logistics curve model is applied to fast-growing companies. The approximation of the company’s growth by the logistics curve allowed us to assess of the growth of a sample of organizations. The growth assessment of “SberMarket” shows that the organization could become one of the dominant participants in the retail market with the current forecasting parameters.