T. V. Bratarchuk (Financial University under the Government of the Russian Federation, Moscow)

The impact of data center infrastructure and distributed data warehousing on the efficiency of strategic planning in the industrial sector

The study analyzes the impact of data center infrastructure and distributed data warehouses on the efficiency of strategic planning in the industrial sector. The key factors and mechanisms of this impact are identified, as well as the possibilities for optimizing business processes through the use of modern data storage and processing technologies are determined. The methodological base consists of a systems approach, statistical and correlation-regression analysis. The empirical data include performance indicators of 120 industrial companies from various industries for 2019–2023. It has been established that the level of data center infrastructure development is a significant predictor of the accuracy of strategic planning. The effect is mediated by an increase in the speed and quality of analytical data processing. The introduction of distributed storage reduces the time for making strategic decisions by 20–35 %. A conceptual model of the impact of IT infrastructure on the efficiency of strategic management is proposed. The findings open up prospects for further study of the transformational potential of data storage and analysis technologies in the context of industrial enterprise management.
Keywords: data center infrastructure; distributed data storage; strategic planning; industrial sector; data management; business process efficiency.

 

S. A. Tronin (Financial University under the Government of the Russian Federation, Moscow)

Study of predictive analytics and artificial intelligence methods for improving quality and productivity in industrial production

The article examines methods of predictive analytics and artificial intelligence for improving the quality and productivity of industrial production. A conceptual analysis of the literature reveals key trends in the application of machine learning, computer vision, and big data analysis to optimize production processes. A critical analysis of terminology is conducted and gaps in existing research are identified. An original approach based on the integration of deep learning, statistical modeling, and simulation is proposed. The empirical base consists of data from production lines of five mechanical engineering enterprises (a total of about 2 million observations). The application of the developed methodology allows increasing the yield of good products by 4,7 %, reducing equipment downtime by 12,5 %, and reducing costs by 7,2 %. The results are of high practical importance for the digitalization of industry. Further research should be aimed at adapting the proposed methods to other industries and expanding the range of tasks to be solved.
Keywords: predictive analytics; artificial intelligence; industrial production; machine learning; big data; computer vision; digitalization.

 

V. B. Frolova (Financial University under the Government of the Russian Federation, Moscow)

A comprehensive study of the implementation of digital models in industrial enterprise production processes to optimize resources and reduce costs

The article presents the results of a comprehensive study of the implementation of digital models in industrial enterprises production processes to optimize resources and reduce costs. Based on a systematic literature review and analysis of empirical data obtained from a sample of 120 enterprises in various industries, key trends, barriers and success factors for digital transformation of production were identified. Using methods of economic and mathematical modeling and statistical analysis, it was found that the implementation of digital twins, simulation modeling and predictive analytics can reduce resource costs by 12–18 %, increase productivity by 15–22 % and accelerate the introduction of new products to the market by an average of 20 %. Original methodological approaches to assessing economic effects and building optimal strategies for digitalization of production are substantiated, taking into account industry specifics. The results obtained develop scientific ideas about the potential of digital technologies in increasing the efficiency and competitiveness of the industrial sector and are of high practical importance for managing innovative projects at enterprises.
Keywords: digital models; digital twins; simulation modeling; predictive analytics; production optimization; resource efficiency; Industry 4.0.

 

P. V. Trifonov (Financial University under the Government of the Russian Federation, Moscow)

Developing of systematic approaches to the use of digital models for innovative development and improving the efficiency of industrial enterprises in the digital economy

The article is devoted to the development of systemic approaches to using digital models for innovative development and improving the efficiency of industrial enterprises in the context of digital transformation of the economy. Based on a critical analysis of relevant research, the authors identify gaps in existing concepts and propose original terminology to describe key concepts. A comprehensive methodology for implementing digital models has been created, ensuring an increase in the innovative potential and economic efficiency of the industrial sector. The empirical base is formed by a survey of 120 Russian enterprises of various profiles and sizes. Using the methods of factor and cluster analysis, structural equation modeling, key drivers and barriers to digitalization have been identified, a typology of transformation strategies has been developed. It has been established that the effect of implementing digital models (an increase in profitability by 12–17 %) is achieved through optimization of business processes, rationalization of decision support systems, and development of end-to-end technologies. A conceptual scheme for a phased transition to digital business models, adapted to the conditions of industrial enterprises, is proposed. The obtained results develop the theoretical and methodological basis for managing digital transformation and serve as the basis for developing practical tools for increasing competitiveness in Industry 4.0.
Keywords: digital economy; innovative development; industrial enterprises; digital models; systems approach.

 

T. V. Bratarchuk (Financial University under the Government of the Russian Federation, Moscow)

Development of methodological approaches to the integration of data centers and distributed data storage systems into strategic management processes of industrial enterprises

The article is devoted to the development of methodological approaches to the integration of data centers and distributed data storage systems (DSS) into strategic management processes of industrial enterprises. The relevance of the topic is due to the need to improve the efficiency of using information resources for making management decisions in the context of digital transformation. A conceptual model and tools for integrating data centers and DSS into strategic processes have been created. The objectives include analyzing best practices, developing the architecture of an integrated system, and testing the methodology using case studies. The empirical base consists of data on the IT infrastructure and performance indicators of 50 industrial companies. As a result, a multi-level integration model has been developed that allows for a 15–20 % increase in data processing speed (p < 0,01) and a 10–15 % reduction in IT costs (p < 0,05). Methods for assessing the maturity level of integration processes are proposed. Promising directions for the development of approaches based on predictive analytics and artificial intelligence are identified. The results obtained are important for improving the quality of strategic decisions and can be used as a methodological basis for the digital transformation of industry.
Keywords: strategic management; data centers; distributed data storage systems; integration; digital transformation; industrial enterprises; industry 4.0.

 

P. V. Trifonov, S. V. Kashirin (Financial University under the Government of the Russian Federation, Moscow)

Developing approaches to integrating predictive analytics into industrial enterprise growth modeling processes

Approaches to integrating predictive analytics methods into industrial enterprise growth modeling processes are being developed. The relevance of the study is due to the need to improve the accuracy of forecasting the dynamics of key performance indicators of companies in conditions of high uncertainty. A comprehensive methodology for using predictive models to support decision-making on enterprise development management has been developed. An algorithm for integrating predictive models into an enterprise growth management system has been developed, ensuring an increase in revenue forecast accuracy by 12–17 % (MAE = 0,14; R2 = 0,89). A methodology for selecting the optimal configuration of a predictive model based on boosting is proposed (MAPE decreases by 5–9 % relative to base models). A set of enterprise development scenarios has been formed and management decision options for each scenario have been developed. The results obtained have high theoretical and practical significance, opening up opportunities for further development of the predictive analytics methodology in the context of strategic business growth management.
Keywords: predictive analytics; machine learning; growth modeling; industrial enterprises; scenario analysis; decision support.

 

T. A. Matseevich, E. V. Kondrashova, J. R. Batkaeva (National Research Moscow State University of Civil Engineering, Moscow)

Factors influencing preparation for various types of certification at the Faculty of Physics and Mathematics based on correlation and regression analysis: students' opinions

The purpose of the study is to build correlation and regression models of the dependence of factors influencing the successful preparation of students of the Faculty of Physics and Mathematics for various types of certification, and the level of effectiveness of passing various types of certification, analysis and interpretation of the data obtained. Materials and methods. Based on a survey of students of the Faculty of Physics and Mathematics conducted using the «snowball» method, a guide was compiled that takes into account the influence of factors on the preparation of students for final certification (session), for intermediate certification, for extracurricular work. The respondents also assessed their performance in each of the certifications over the past period. The following variables were taken as dependent variables: the effectiveness of the session over the past period; the effectiveness of the interim control work over the past period; the effectiveness of self-study of the material during extracurricular work over the past period. After a correlation and regression analysis of the results of the initial survey was conducted, which did not show a strong influence of factors on the effectiveness of the educational process, the initial guide was revised and a second survey was compiled and conducted with an increase in the number of respondents. As a result of the analysis, influential variables were identified and interpreted linear regression models were constructed. Results. As a result of this study, we were able to identify a number of factors that can affect the educational process of students of the Faculty of Physics and Mathematics. Based on the results of this study, recommendations can be submitted to the Faculty of Physics and Mathematics for the competent improvement of the educational process on their part. Several models have been developed that reveal the dependence of various types of certification on factors that take into account both the logistical role the campus of the Faculty of Physics and Mathematics, as well as various psychological factors of successful learning, factors of leisure activity, etc.
Keywords: factors affecting the quality of education; statistical study; certification; correlation-regression analysis; linear regression models.

 

A. I. Galkin (Financial University under the Government of the Russian Federation, Moscow)

The role of industrial policy in the development of high-tech industries and the transition to a knowledge economy

This article examines the role of industrial policy in the development of high-tech industries and the transition to a knowledge economy. Based on the analysis of a wide range of modern studies, the key factors and mechanisms of industrial policy influence on the pace and trajectories of technological development are identified. An original conceptual model is proposed that explains the relationship between industrial policy instruments, innovative activity of firms and structural shifts in the economy. Empirical testing of the model on data for 28 OECD countries for 2000–2023 confirmed its high explanatory power (R2 = 0,81). It has been established that targeted industrial policy combining instruments for stimulating R&D, developing human capital and supporting high-tech exports can accelerate the transition to a knowledge economy by 5–7 years. At the same time, ensuring the complementarity of different instruments and their compliance with country specifics plays a key role. The obtained results contribute to the understanding of strategic mechanisms of industrial transformation and open up opportunities for developing more effective public policy measures.
Keywords: industrial policy; high-tech industries; knowledge economy; innovation; R&D; structural shifts.

 

S. G. Eremin (Financial University under the Government of the Russian Federation, Moscow)

Analysis of the economic efficiency of industrial policy measures in the context of digital transformation

The article is devoted to the analysis of the economic efficiency of industrial policy measures in the context of digital transformation. The relevance of the study is due to the need to find optimal instruments of state support for industry in the new technological realities. The purpose of the work is to identify the key factors and mechanisms that determine the effectiveness of industrial policy in the context of digitalization. The objectives include systematization of theoretical approaches, analysis of foreign and Russian experience, econometric assessment of the effects of government measures. The research methodology is based on the synthesis of neoclassical, institutional and evolutionary paradigms. The empirical base covers data on 35 countries for the period 2010–2023. The results indicate a significant positive impact of investments in R & D and human capital on the dynamics of industrial production (elasticity of 0,37 and 0,28). The complementarity of selective and horizontal support measures is revealed. The non-monotonic nature of the dependence of policy effectiveness on the level of digital infrastructure development is established. The practical significance lies in substantiating the priorities of the industrial policy of the Russian Federation in the context of digital transformation. The findings advance the scientific debate on mechanisms for stimulating innovative activity in industrial companies.
Keywords: digital transformation; industrial policy; innovation; R&D; human capital; econometric analysis.

 

A. L. Kudryashov (Financial University under the Government of the Russian Federation, Moscow)

Application of data center technologies and distributed data storage systems to optimize industrial enterprise strategy development

This article is devoted to the study of the possibilities of using data center technologies and distributed data storage systems to improve the processes of industrial enterprise strategy development. A comprehensive analysis of modern research in this area was conducted, which allows us to identify key trends and gaps in the study of the problem. The author's terminology is proposed to eliminate conceptual discrepancies. The empirical basis of the study is data on the strategies of 120 large industrial companies from various industries. Using statistical and network analysis methods, as well as simulation modeling, patterns of influence of enterprise IT infrastructure characteristics on the effectiveness of their strategic planning were identified. A set of practical recommendations for optimizing industrial companies' IT systems has been developed to improve the validity, consistency and adaptability of their strategies in a dynamic environment. The findings open up new prospects for integrating strategic management methods and big data analysis.
Keywords: industrial enterprises; strategic planning; data centers; distributed data storage systems; big data; optimization.

 

N. V. Kuchkovskaya (Financial University under the Government of the Russian Federation, Moscow)

Analysis of the efficiency of using predictive analytics in modeling the life cycle of industrial enterprises

Predictive analytics is increasingly used in the management of industrial enterprises, allowing for early identification of potential risks and opportunities at various stages of the life cycle. A comprehensive analysis of the efficiency of using predictive analytics methods to model the dynamics of key performance indicators of manufacturing enterprises is conducted. The empirical base consists of data on 112 large and medium-sized enterprises in various industries for the period 2015–2022. It was found that the use of predictive models allows for a 10–15% increase in the accuracy of forecasting production volumes (MAPE = 7.6%) and a 20–25% increase in profitability (MAPE = 9.2%) over a 1–2-year horizon compared to classical regression models. It is shown that the greatest effect is achieved by combining methods based on neural networks and decision trees (R2 = 0.88). Practical recommendations for the implementation of predictive analytics in the management circuit of an industrial enterprise are formulated. The results of the study are important both for the development of the DataScience methodology in management and for improving the validity of decisions made in real business.
Keywords: predictive analytics; enterprise life cycle; machine learning; big data; business process modeling.

 

V. B. Frolova (Financial University under the Government of the Russian Federation, Moscow)

Analysis of the impact of digital modeling on strategic management and competitiveness of industrial enterprises in the era of Industry 4.0

The article presents an analysis of the impact of digital modeling on strategic management and competitiveness of industrial enterprises in the era of Industry 4.0. The relevance of the topic is due to the growing role of digitalization in improving the efficiency of business processes and the need to develop new approaches to management in the context of technological transformation. The key factors and mechanisms of the impact of digital modeling on the strategic development of industrial companies are identified. The methods of system, comparative and economic-statistical analysis, as well as a case study on the example of 30 large mechanical engineering enterprises of the Russian Federation are used. The results show that the introduction of digital modeling technologies can increase the accuracy of demand forecasting by 15-20%, reduce the time for developing new products by 30-40% and increase overall profitability by 5-7%. At the same time, the key factors of success are the integration of modeling into the decision-making system, the development of personnel competencies and partnership with technology leaders. The findings contribute to the theory of strategic management and can be used to improve the competitiveness of the industrial sector in the digital economy. Further research should be directed at developing industry reference models and assessing the long-term effects of digital transformation.
Keywords: digital modeling; Industry 4.0; strategic management; competitiveness; industrial enterprises; digitalization; technological transformation.

 

M. P. Lazarev (Financial University under the Government of the Russian Federation, Moscow)

A study of the use of tokens in the digital economy to improve the efficiency of industrial enterprises

The article presents the results of a study of the use of tokens in the digital economy to improve the efficiency of industrial enterprises. The relevance of the topic is due to the growing interest in tokenization as a tool for transforming business models and optimizing production processes in the context of Industry 4.0. The empirical base consists of data on 50 industrial enterprises implementing tokens. It was found that the use of tokens contributes to an increase in labor productivity by 12–15 %, a decrease in transaction costs by 20–25 %, and an increase in the transparency of supply chains. The factors and barriers affecting the efficiency of tokenization in industry are determined. The obtained results have theoretical significance for the development of the tokenization concept, as well as practical value for optimizing business processes of enterprises. Research prospects are related to the development of a methodology for assessing the economic effects of token implementation and the analysis of the best practices of tokenization in industry.
Keywords: tokenization; digital economy; Industry 4.0; industrial enterprises; business processes; efficiency.

 

T. N. Sakulyeva (State University of Management, Moscow)

Technology and organization of cargo transportation by road

Road transport is the only type of transport that delivers cargo «door to door». The organization of road freight transportation is an important element of the logistics chain and affects the timely delivery of goods to the right place at the right time. The article examines the technological process of cargo delivery by road. Its organization and features are analyzed.
Keywords: road transport; freight transportation; cargo transportation; motor vehicles; freight transportation.

 

K. V. Kharchenko (Financial University under the Government of the Russian Federation, Moscow); A. S. Kolesnikov (M. Auezov South Kazakhstan University, Shymkent, Kazakhstan)

Strategy for the development of the scientific and production cluster: from content development to digital format

The purpose of the paper is to substantiate the need for strategic planning of the development of regional clusters as research and industrial associations interacting with the territory of their presence and designed to make a significant contribution to the regional and national socio–economic system. Despite the fact that the legal norm on programs and, moreover, the strategy for the development of industrial clusters is currently not in force, the existence of a strategic document is seen as an important attribute of any organization that cares about its future. It is shown that the intra-cluster strategy reveals the heuristic potential of cluster cooperation, creates a clear and structured image of the future, allows rational allocation of limited resources, strengthens inter-project coordination, raises the interaction of participants from operational, technical and legal to a spiritual level. The strategy also serves as a arbitrary power tool within the cluster and a message to external actors. The participation of a public partner in the cluster makes it possible to link the purely economic tasks of enterprises and organizations with the social and environmental context. The paper formulates the principles of strategic planning for cluster development, which are then projected onto the features of the structure and content of the intra-cluster strategy. An important element of the strategy is the analysis of various types of cluster potential, which makes it possible to strengthen the integration of its participants and generate joint projects. In terms of the format, the modern cluster strategy is considered as an interactive digital platform coupled with an integrator platform for current activities.
Keywords: industrial cluster; strategic planning; intra-cluster strategy; digital platform; government support for industry.

 

N. L. Krasyukova (Financial University under the Government of the Russian Federation, Moscow)

Analysis of instruments for the development of domestic industry

The article is devoted to the analysis of the use of government tools for the development of domestic industry, which contribute to ensuring the technological sovereignty of the Russian Federation. Among these tools are the national projects «Labor Productivity», «Digital Economy», the Register of Projects of Technological Sovereignty and Structural Adaptation of the Russian Economy, Industrial Development Fund, Consolidated strategy for the development of the manufacturing industry of the Russian Federation until 2024 and for the period until 2035.
Keywords: instruments of public administration; technological sovereignty; industrial policy.

 

A. V. Semenov, Sh. U. Niyazbekova (Moscow Witte University, Moscow); S. I. Adinyaev, B. Bissenbayev (Patrice Lumumba Peoples' Friendship University of Russia, Moscow); V. V. Varzin (A. N. Kosygin Russian State University (Technologies. Design. Art), Moscow); G. I. Nurzhanova (Astana International University, Astana, Republic of Kazakhstan); A. B. Mottayeva, N. N. Gubskaya, E. A. Isaeva (Financial University under the Government of the Russian Federation, Moscow); S. Sh. Mambetova, S. K. Yerzhanova, A. K. Kurmanalina, Zh. M. Zeynullina, D. T. Nursultan, A. R. Arystanbek (KARAGANDA BUKETOV UNIVERSITY, Karaganda, Republic of Kazakhstan)

The steel industry in the face of increasing competition, digitalization and improved environmental sustainability

This study notes that the steel industry is considered one of the largest and most dynamically developing industries in the world. Therefore, new developments in this industry have a significant impact on the global economy, but it is also strongly influenced by international events. The article presents the dynamics of global stainless steel production in smelters from 2005 to 2022. It is generally accepted in the steel industry that manufacturers must implement digitalization in order to prepare their businesses for increased competition and stricter environmental regulations. Terms such as data analysis, artificial intelligence (AI) or interconnected systems are not just buzzwords; they are the most important concepts and cornerstones of the digitalization roadmap of any steel mill. Consequently, data or, more precisely, the availability and use of data has become a top priority for technology directors and digitalization directors.
Keywords: steel industry; steel industry; increased competition; technological innovation; financial support; quality improvement; national economy.

 

T. A. Golovina, I. L. Avdeeva (Central Russian Institute of Management, Branch of RANEPA, Orel)

Strategic approach to management of circular economy technology adoption processes

The article is devoted to the peculiarities of realization of the strategic approach to the management of the processes of implementation of circular economy technologies in modern conditions. It is the circular economy that in the future should prevent environmental crises and disasters, reduce the rate of depletion of natural resources and increase the efficiency of their use. It is concluded that the realization of a strategic approach in the management of the processes of implementation of circular economy technologies not only contributes to the conservation of natural resources and reduction of the ecological footprint, but also contributes to improving the level of socio-economic development of the subjects of the Arctic zone of the Russian Federation, increasing their resilience to risks and ensuring sustainable long-term development. In the process of the study such research methods as: method of analysis and synthesis, dialectical and systematic approach, historical, logical, comparative, decomposition, deductive and others are used.
Keywords: sustainable development; environmental sustainability; circular economy; circular economy; circular economy; strategic management.

 

A. V. Semenov, Sh. U. Niyazbekova (Moscow Witte University, Moscow); Zh. S. Khussainova, L. Zh. Bekbosynova, G. R. Mombekova, D. H. Shuptybayeva (S. Seifullin Kazakh Agro Technical Research University, Astana, Republic of Kazakhstan); E. V. Shirinkina, A. S. Klishin (Surgut State University, Surgut); A. M. Nurgaliyeva, A. N. Lambekova (Narxoz University, Almaty, Republic of Kazakhstan); B. Bissenbayev (Peoples' Friendship University of Russia, Moscow); S. K. Yerzhanova, Zh. M. Zeynullina, D. T. Nursultan, A. R. Arystambek (KARAGANDA BUKETOV UNIVERSITY, Karaganda, Republic of Kazakhstan); V. V. Varzin (A. N. Kosygin Russian State University (Technologies. Design. Art), Moscow)

Development of carbon dioxide emission reduction technologies in industrial

This article examines the low-carbon industry. Today, clean energy is based on low-carbon technologies, in order to ensure the cost-effectiveness of low–carbon technologies, it is necessary to jointly promote the creation of renewable energy infrastructure to expand the production and use of electricity. The article notes that huge funds are needed to transition to low-carbon production. Experts note that together with government support and private capital, it is possible to create a system of financing the transition period, which is necessary to strengthen support. The paper presents data on carbon dioxide (CO2) emissions in the world from 1940 to 2024 and the distribution of greenhouse gas emissions worldwide in 2023 by sector.
Keywords: chemical industry; carbon dioxide emissions; renewable energy source; minimal carbonation; greenhouse gas; innovative technologies; low-carbon industry.

 

I. V. Deryabin (Togliatti State University, Togliatti)

Low noise production room

The original design of a sound-absorbing plate for an industrial facility with resonator chambers integrated into its porous structure is considered. The walls of these chambers are made of thin, air-tight, sound-transparent material, and the porous structure is represented by separate segments made of secondary processed raw materials. The results of experimental studies of a fragment of the sound-absorbing plate in a test reverberation chamber are presented. A conclusion is made about the acoustic efficiency of the sound-absorbing plate in a wide range of the sound spectrum.
Keywords: production facility; sound-absorbing plate; sound spectrum; noise; resonator chamber.

 

V. S. Batomunkuev, T. Sh. Rygzynov (IMBT SB RAS; BINM SB RAS, Ulan-Ude)

Historical and geographical analysis of Manchuria's natural resources and their role in the development of metallurgy

The article presents a historical and geographical analysis of Manchuria's natural resources and their influence on the development of metallurgy. The largest deposits of coal, iron ores, magnesite and other minerals, their extraction and utilisation, especially during the period of Japanese occupation, are examined. The analysis emphasises the geopolitical and industrial importance of the region, and the limited development of Manchuria due to external control.
Keywords: Manchuria; minerals; metallurgy; coal; iron ores; natural resources; industrialisation.

 

O. V. Panina (Financial University under the Government of the Russian Federation, Moscow)

The first years of soviet power: industrial management in new conditions

The approaches to reforming the management of the national economy and industry in the early years of Soviet power are analyzed. The author substantiates the thesis that at that time there was an active search for new forms, methods and tools of industrial management in the context of the nationalization of industrial enterprises. The introduction of the principles of self-government, democratic centralism, on the one hand, and the objective strengthening of state control, regulation, and management of industry led to the creation of a management model that fully met the needs of the state at that time.
Keywords: Ministry of Trade and Industry; industry; public administration; industrial complex; industrial policy.

 

I. V. Shatskaya, A. V. Shpak, E. R. Zhdanov, O. S. Kharina, K. D. Skobelev (MIREA – Russian Technological University, Moscow)

Strategic socio-ecological assessment of the Arctic and management of the development of its natural-spatial potential (based on the methodology of V. L. Quint)

The article describes the methodology of strategic socio-ecological assessment of the Arctic, as well as the formulation of recommendations in the field of development of the natural and spatial potential of the Arctic territories. The concept of «natural and spatial potential of the territory», its structure and content are defined; the methodology of strategic socio-ecological assessment is shown using the Arctic region as an example; specific recommendations are formulated aimed at developing the natural and spatial potential of the territory in order to improve the quality and standard of living of its residents. The theoretical and methodological basis of the article are the scientific achievements of the scientific school of strategizing at Lomonosov Moscow State University named under the guidance of Academician of the Russian Academy of Sciences, Doctor of Economics, Professor V. L. Quint, as well as domestic and foreign scientists in the field of development of the natural and spatial (natural resource) potential of the territory.
Keywords: Arctic region; strategy; natural and spatial (natural resource) potential; strategic socio-ecological assessment.