ИНДИКАТОРЫ ПРОГНОЗИРОВАНИЯ БИЗНЕС-ПРОЦЕССОВ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ ЭНЕРГЕТИЧЕСКОГО СЕКТОРА

Abstract:
The purpose of the article is to study the indicators of forecasting business processes of industrial companies in the energy industry. The paper considers the problems of forecasting business processes at industrial enterprises, in particular in the energy sector. Special emphasis is placed on the fact that now, in conditions of high volatility of commodity markets, as well as taking into account stricter environmental requirements, deterioration of infrastructure and changes in the regulatory environment, traditional indicators for estimating and calculating forecasts have become ineffective. The theoretical review contains a description of the authors’ approaches to the essence of business processes of industrial companies. Based on this, a system of indicators is proposed that makes it possible to assess the dynamics of the main business processes of energy companies ahead of time. The hypothesis is that an integrated approach to the system of indicators based on a single model will give a more accurate forecast than individual metrics. The scientific novelty of the work lies in the selection of key indicators that will help Russian energy companies to take into account the indicators of specific energy efficiency, capacity utilization, sensitivity to tariff regulation and business activity indices in related industries. The methodology of this research includes a review and analysis of sources, a method of forecasting, statistics, comparison, as well as a method of deduction and generalization. The results of the work are that, as a result, three blocks of indicators were identified, namely, operational, market and institutional. Their combined dynamics allows us to predict a slowdown or acceleration of the main business processes with a period of up to two quarters. The practical significance of the research lies in the fact that the new indicator system can be applied in companies in the energy sector. The prospects for further research are related to clarifying and updating the model with data on the technological state of equipment and tools using machine learning algorithms to adaptively adjust indicators depending on the phase of the economic cycle.