Technology Platforms as an Efficient Tool to Modernize Russia's Economy

There is an urgent need to consider the dynamic development of the global economy from the point of view of its positive impact on competitiveness improvement in national manufacturing industries, and the best ways to modernize the country economy. The purpose of the paper is to provide with perspectives for development of instruments related to technology platforms within the framework of innovation management and adapted to the conditions of Russia's economic reality. The major method in studying this issue is mathematical economic modeling which has made it possible to facilitate expediency in determining a technology platform as an effective innovation control instrument.  The paper considers European and Russian experience in deploying technology platforms, and identifies national features characteristic to the performance of the innovation management instrument.  A mathematical economic model is used for justifying the efficiency of introducing technology platforms into Russian institutional innovation system. The practical significance of results and conclusions is in its ability to improve the mechanisms of developing and implementing federal and regional innovation development programs, development of the innovation infrastructure, stimulation of the innovation activity, use of a set of technology platform instruments by public authorities.  Keywords: technology platforms, innovative development, modernization, triple helix. JEL Classifications: C02, C18, O2

Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks

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)

Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

Globally, assessing sustainable development methodology is kept in sustainable society index (SSI) format, but at the level of meso- and microsystems it remains undeveloped. The aim of the study is to typologize innovative mesosystems in Russian industry in the context of sustainable development based on the CART algorithm and to develop an algorithm for identifying priority areas of sustainable development. The research methods applied included formalization, a systematic approach, and the CART algorithm (calculation of the Gini index, training sample segmentation, the use of a recursive function and regression assessment). As a result of the study, the algorithm for the differentiated identification of innovative mesosystems sustainable development priority directions in industry based on the unique author’s methodology (ISDI) is proposed. The predominance of mesosystems with weak level of sustainable development requiring state support in favor of such mesosystems restructure is revealed. The novelty of the research lies in the development of new science-based solutions to ensure an accelerated transition of industry to the path of sustainable development. The difference of the author’s approach from the provisions known in science is the inclusion of environmental innovations in the mechanism for managing the sustainable development of innovative mesosystems and subsequent accounting in the process of mathematical processing of an array of data, which determines the uniqueness of the constructed decision trees

Innovative Mesosystems Algorithm for Sustainable Development Priority Areas Identification in Industry Based on Decision Trees Construction

Globally, assessing sustainable development methodology is kept in sustainable society index (SSI) format, but at the level of meso- and microsystems it remains undeveloped. The aim of the study is to typologize innovative mesosystems in Russian industry in the context of sustainable development based on the CART algorithm and to develop an algorithm for identifying priority areas of sustainable development. The research methods applied included formalization, a systematic approach, and the CART algorithm (calculation of the Gini index, training sample segmentation, the use of a recursive function and regression assessment). As a result of the study, the algorithm for the differentiated identification of innovative mesosystems sustainable development priority directions in industry based on the unique author’s methodology (ISDI) is proposed. The predominance of mesosystems with weak level of sustainable development requiring state support in favor of such mesosystems restructure is revealed. The novelty of the research lies in the development of new science-based solutions to ensure an accelerated transition of industry to the path of sustainable development. The difference of the author’s approach from the provisions known in science is the inclusion of environmental innovations in the mechanism for managing the sustainable development of innovative mesosystems and subsequent accounting in the process of mathematical processing of an array of data, which determines the uniqueness of the constructed decision trees

Directions of Development of Human Capital of Innovative Petrochemical Enterprises

The main aim of the article is to identify the directions of innovative human capital development of industrial enterprises based on the development of an improved assessment methodology. The object of the research is industrial enterprises of the Russian economy. The subject of the research is innovative activity of the enterprises. The key research method is modeling (building a regression model, production function), which makes it possible to systematically study the links between indicators of enterprise innovation and the use of human capital in this process, as well as identify problem areas and directions of human capital development of industrial enterprises