The review of modern methodological approaches to assessing the sustainable development of an industrial enterprise revealed the absence of generally accepted integral tools and the connection sustainable development trends with financial flows. To fill this gap this, taking into account the principle of balanced development economic, environmental and social components aimed. The purpose of the study is the development of a financial flow management model for the sustainable development of an industrial enterprise (using the example of a large Russian petrochemical enterprise). To achieve the goal of the purpose, the following methods implemented systematic approach, analysis and synthesis, comparative analysis, analysis of dynamics series, correlation analysis, regression analysis, solving the linear programming problem. As a result of the study, we came to conclusion about the shift of the enterprise’s focus on environmental issues; the growth of the integral indicator of sustainable development of an industrial enterprise; the negative impact of credit resources on the aggregate indicator. The novelty of the study lies in the development of a new methodological solution, which is the basis of the financial management model for the sustainable development of the enterprise: it is adequate to the level of microeconomic system; covers three ways of measuring sustainable development and the possibility of choosing the best quality; allows to implement a proactive approach to managing financial flows with the principles of sustainable development of the enterprise (existing approaches either represent only a set of indicators or addressed the diagnosis of a specific subsystem, either do not consider the relationship between financial flows and the aggregated indicator of sustainable development of the enterprise).

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on the training of artificial neural networks to predict the direction of industrial development, taking into account a set of factors—innovation, environmental friendliness, modernization and production growth. The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research methods were correlation analysis, training of neural networks (species—regression), extrapolation, and exponential smoothing. As a result of the research, the estimation efficiency technique of an innovative industrial system in a complex considering the criteria of technical modernization, development, innovative activity, and ecologization is developed; the prognostic neural network models allow to optimize the contribution of signs to the formation of target (set) values of indicators of efficiency for macro and micro-industrial systems that will allow to level a growth trajectory of industrial systems; the priority directions of their development are offered. The following conclusions: the efficiency of industrial systems is determined by the volume of sales of goods, innovative products and waste recycling, which allows to save resources; the results of forecasting depend significantly on the DataSet formulated. Although multilayer neural networks independently select important features, it is advisable to conduct a correlation analysis beforehand, which will provide a higher probability of building a high-quality predictive model. The novelty of the research lies in the development and testing of a unique methodology to assess the effectiveness of industrial systems: it is based on a multidimensional system approach (takes into account factors of innovation, environmental friendliness, modernization and production growth); it combines a number of methodological tools (correlation, ranking and weighting); it expands the method of effectiveness assessment in terms of the composition of variables (previously presented approaches are limited to the aspects considered).