KSHP. OMD №12-2024 el.dop
T. V. Bratarchuk (Financial University under the Government of the Russian Federation, Moscow)
Study of the complex impact of artificial intelligence and big data on optimization of industrial production processes
This study aims to study the complex impact of artificial intelligence (AI) and big data technologies on optimization of industrial production processes. An interdisciplinary approach is used, combining methods of system analysis, mathematical modeling and machine learning. The empirical base consists of data on production indicators of 120 enterprises in various industries for the period 2018–2022. Key factors determining the effectiveness of AI implementation in production management are identified, including the level of digitalization, data quality, and personnel qualifications. Predictive models are built to assess the potential for increasing labor productivity (by 18–25 %), reducing costs (by 10–15 %) and improving product quality (by 12–20 %). It is shown that the maximum effect is achieved with the integrated use of AI and big data at all stages of the production cycle. The obtained results have high practical significance, opening up opportunities for further increasing the competitiveness of industrial enterprises in the context of digital transformation of the economy.
Keywords: artificial intelligence; big data; production optimization; digitalization of industry; systems analysis; mathematical modeling; machine learning.
P. V. Trifonov (Financial University under the Government of the Russian Federation, Moscow)
Application of machine learning models and big data to improve the efficiency and innovative development of industrial enterprises
The paper examines the potential of using machine learning models and big data technologies to improve the efficiency and innovative development of industrial enterprises. Based on a critical analysis of relevant literature and a synthesis of theoretical approaches, key trends and gaps in research are identified. An original methodology is proposed that integrates deep learning, data mining, and predictive analytics. The empirical base includes arrays of sensory, operational, and business data (N = 5,6 million records) from 12 enterprises in 5 industries for 2019–2023. Statistically significant (p < 0,01) predictors of production efficiency are identified, and highly accurate (F1 = 0.91) classification models are built. A framework for supporting innovative solutions based on dynamic segmentation and targeting is developed. The results are of high theoretical value, expanding the conceptual boundaries of Industry 4.0 and Data-Driven Manufacturing. Practical significance is associated with the possibility of direct implementation of the proposed tools in management processes. Prospects for the development of the «smart» production paradigm based on artificial intelligence methods are outlined.
Keywords: machine learning; big data; digitalization of production; Industry 4.0; innovative development; intelligent data analysis; predictive analytics.
T. N. Sakulyeva (State University of Management, Moscow)
Unmanned ground transport
Almost all the world's leading automakers in the USA, Germany, Japan, Italy, China, Great Britain, France, Korea and Russia are currently engaged in the creation of unmanned vehicles. The study analyzes the directions of development of unmanned vehicles. Several levels of automation of an unmanned vehicle are highlighted. The advantages, limitations and disadvantages of using unmanned ground transport are revealed. The development of unmanned vehicles is promising both for Russia and for the whole world.
Keywords: car, unmanned ground transport; unmanned vehicle; traffic safety; automation; technical parameters.
S. V. Karpova, O. E. Ustinova (Financial University under the Government of the Russian Federation, Moscow)
Technological changes in the industry
Industry, being a key sector of the economy, has long remained resistant to changes and technologies. For many decades, the industrial sector has been criticized for lagging behind in innovation, having safety issues and low productivity. Nowadays, rapid advancements in technology are transforming the industry, making it safer, more efficient and effective. Industrial companies are focusing on how to adapt to technological advancements. This paper conducts a systematic review to present potential options for the application of digital technologies in industry, analyzing the dynamics of advanced technology adoption by Russian industrial companies. The results of the study may be useful for representatives of public authorities, subjects of industrial markets, as well as public organizations. Methods of theoretical analysis of scientific literature, analysis of statistical data and comparative analysis were used. Based on the analysis, the main advantages of digital technologies in industry and the problems associated with their implementation are highlighted. The most significant technological changes in industry have been identified, rethinking traditional production methods.
Keywords: technological changes; innovations; digital technologies.
N. V. Kuchkovskaya (Financial University under the Government of the Russian Federation, Moscow)
Assessing the economic efficiency of using artificial intelligence and big data in industrial process management
This article is devoted to assessing the economic efficiency of using artificial intelligence (AI) and big data technologies in industrial process management. A comprehensive methodology for assessing the impact of AI and big data analytics on key performance indicators of production has been developed. Methods of system analysis, economic and mathematical modeling, and statistical data processing have been used. The empirical basis of the study was data on 50 large Russian industrial companies for 2017–2021. It has been found that the use of AI and big data allows for an average increase in labor productivity by 12,4 %, a decrease in production costs by 9,6 %, and a decrease in equipment downtime by 14,2 %. The developed methodology allows companies to assess the feasibility of investing in AI, taking into account the specifics of their business processes. The results obtained make a significant contribution to the development of tools for assessing the economic efficiency of digital technologies in industry and can be used to make informed management decisions. Prospects for further research are related to testing the proposed methodology on a wider sample of companies and its refinement taking into account industry specifics.
Keywords: artificial intelligence; big data; industry; production processes; economic efficiency; investments; digitalization.
I. V. Rozhkov, I. K. Zakharenko, N. F. Soldatova, V. N. Rusin (Financial University under the Government of the Russian Federation, Moscow)
Mathematical modeling of regional economic growth and development processes: a multi-level approach taking into account spatial effects, human capital and innovative potential of regions
The article presents an innovative multi-level approach to mathematical modeling of the dynamics of economic growth and regional development. The proposed methodology integrates spatial dependencies between regions, the quality of human capital, innovative activity of economic agents and structural features of regional economies. A set of interrelated econometric models has been developed to quantitatively assess the impact of human capital (education, health, labor mobility, etc.) on regional growth rates, taking into account spatial externalities and heterogeneity. Dynamic models of regional innovation systems reflect network interactions between science, education, business and the state, diffusion and transfer of technologies, agglomeration and multiplier effects of innovations. Multiregional models of economic growth take into account interregional trade, migration of production factors, transport and logistics infrastructure and spatial concentration of economic activity. The stability of regional development trajectories is analyzed, the possibilities of convergence/divergence of regions are identified depending on the initial conditions, institutional factors and policies. Dynamic models of optimal distribution of investments in human capital and R&D between regions and sectors based on socio-economic returns and spatial externalities are proposed. Model parameters are identified based on current statistical data using modern econometric methods. Applied tools for scenario forecasting and decision support in the field of regional development management are developed.
Keywords: regional economic growth; spatial effects; human capital; innovation; multilevel modeling.
P. V. Trifonov, R. V. Seryshev (Financial University under the Government of the Russian Federation, Moscow)
Defining the conditions for the development of an intellectualization strategy in the industry of the Russian federation to achieve technological sovereignty
The article formulates the main conditions for the transition of the domestic industry from the traditional model of development to the intellectual one. The main factors influencing the process of intellectualization in the industry of the Russian Federation are determined on the basis of statistics from Rosstat databases. The assessment of the level of application of digital transformation tools is given and the main models of digitalization of industry in the Russian Federation are described. The main conditions for the development of an intellectualization strategy in the domestic industry to achieve technological sovereignty are described, as well as the main risks and opportunities for the development of domestic industry based on the tools of intellectualization of production systems.
Keywords: intellectualization strategy; industry; production systems; digital transformation; digitalization; technological sovereignty.
M. R. Safiullin (Kazan (Volga Region) Federal University; Center for Advanced Economic Research of the Academy of Sciences of the Republic of Tatarstan, Kazan); L. A. Elshin (Kazan (Volga Region) Federal University; Center for Advanced Economic Research of the Academy of Sciences of the Republic of Tatarstan; TISBI University of Management, Kazan); A. M. Mingulov (Center for Advanced Economic Research of the Academy of Sciences of the Republic of Tatarstan, Kazan)
Multi-component assessment of shock resistance as a tool of state planning and management of regional-industry complexes
The study is aimed at developing a concept and tools for empirical assessment of shock resistance (resilience) of regional-industry complexes as a mechanism for state planning and management of the regional economy. The result of the work is the implemented assessments of shock resistance of regional economic systems of the Volga Federal District, which form the basis for developing solutions in the sphere of implementing regional policy in the context of systemic transformations, expressed primarily in the sanctions pressure on the national economy of the Russian Federation.
Keywords: sanctions pressure; import dependence; import substitution; sustainability of economic growth; region; recovery efficiency; sustainability of economic growth.
S. A. Tronin (Financial University under the Government of the Russian Federation, Moscow)
An integrated approach to industrial development using artificial intelligence and big data to create sustainable production systems
The article proposes an integrated approach to industrial development based on the use of artificial intelligence (AI) and big data technologies to create sustainable production systems. A conceptual literature analysis was conducted, which revealed key trends in this area over the past 5 years. The terminology was clarified, gaps in research were identified, and the relevance of the proposed approach was substantiated. The methods of system analysis, mathematical modeling, and machine learning were used. The empirical base consisted of data from 500 industrial enterprises in various industries. It is found that the introduction of AI can increase the energy efficiency of production by 17 %, labor productivity by 21 %, and reduce CO2 emissions by 12 %. A conceptual model of a sustainable production system based on AI is proposed. Prospects for further research on adapting the model to the specifics of individual industries and its practical testing are outlined.
Keywords: artificial intelligence; big data; sustainable production; digitalization of industry; Industry 4.0; energy efficiency; decarbonization.
V. B. Frolova (Financial University under the Government of the Russian Federation, Moscow)
Application of predictive modeling methods to optimize management decisions in industrial enterprise development
The article is devoted to the study of the prospects for applying predictive modeling methods to improve the efficiency of management decisions in the context of industrial enterprise development. Analyzing relevant works of recent years, the author identifies gaps in the conceptual and methodological understanding of this issue and substantiates the relevance of developing an integrative approach that combines the advantages of various predictive models. Based on a critical review of the literature, the terminology is clarified and an original conceptual framework for the study is proposed. The empirical part of the work is based on a representative sample, including data on the activities of 120 industrial enterprises in Russia for the period 2015–2020. Using a set of methods, including regression analysis, neural network modeling and the principal component method, the author identifies key factors that determine the effectiveness of management decisions and develops a predictive model that allows forecasting the dynamics of key performance indicators of enterprises. Testing of the model confirms its high accuracy (R2 = 0,87) and practical value for information support of decision-making. The obtained results make a significant contribution to the development of the methodology of predictive analytics in management and open up prospects for further interdisciplinary research at the intersection of management, economics and informatics.
Keywords: predictive modeling; management decisions; industrial enterprises; big data; regression analysis; neural network modeling.
A. T. Bayakhmetova (AlmaU, Almaty, Republic of Kazakhstan); Sh. U. Niyazbekova (Moscow Witte University, Moscow; Financial University under the Government of the Russian Federation, Moscow)
Technological transformations and the mechanism for providing financial services to the manufacturing sector by Chinese banking and financial institutions
The article is devoted to the issues of technological transformations and mechanisms for the provision of financial services to the manufacturing sector by Chinese banking and financial institutions. The study noted that the implementation of a new type of Chinese industrialization is a key task of modernization aimed at comprehensively contributing to the construction of a powerful country and the great cause of national revival. It is known that the Government of the country allocates huge financial resources for the development of advanced production, transforms the country from a large manufacturing country into a powerful manufacturing country and directs financial institutions to high–quality development of the service sector, deepening financial services and promoting new types of industrialization. The article also conducts research on the key areas of updating advanced equipment, digital transformation, promoting environmentally friendly equipment, improving safety and other actions related to technological transformations and equipment upgrade projects.
Keywords: manufacturing sector; technological transformations; banking and financial institutions; long-term credit investments in the manufacturing industry; high-quality financing and credit enhancement.
M. P. Lazarev (Financial University under the Government of the Russian Federation, Moscow)
Tokens as a tool for digital transformation of industrial production processes
The article examines the use of tokens for the digital transformation of industrial production processes. Based on a critical analysis of literature from highly rated journals over the past 5 years, the differences in the definitions of key concepts are discussed and the main trends in this area are highlighted. Clear terminology is proposed and the relevance of developing new approaches to the use of tokens in production is substantiated. Using a set of methods, including statistical analysis, simulation modeling and expert interviews, the effects of token implementation are studied on an empirical basis of 10 industrial enterprises. It is found that the use of tokens can increase the speed of production processes by 15–20 %, reduce costs by 10–15 % and improve product quality by an average of 12 %. The results are of high theoretical and practical significance, opening up prospects for further digitalization of industry based on token technology.
Keywords: tokens; digital transformation; industrial production; process efficiency; Industry 4.0; simulation modeling.
A. D. Barsukova (Moscow State University of Civil Engineering, Moscow)
Circular economy as a basis for increasing environmental sustainability
The article considers the definitions of the terms «sustainability», «environmental sustainability». The levels of implementation of environmental sustainability are shown. The characteristic features of the concept of circular economy are revealed. It is concluded that the concept of cyclical use of materials and resources allows not only to reduce the negative impact on the environment, but also to obtain economic benefits from cyclicality. A comparison of the linear and cyclical models is presented. The specifics, difficulties and risks in the implementation of measures to achieve environmental sustainability and the transition to a closed-loop economy are outlined. The phenomenon of industrial symbiosis is analyzed in detail, models for implementing symbiotic systems are identified. Steps for implementing the transition to a circular economy in Russia are presented.
Keywords: ecology; sustainability; environmental sustainability; circular economy; linear economy.
As. B. Mottaeva (Financial University under the Government of the Russian Federation, Moscow)
Integration of digital intelligence as a modern trend in the transformation of the Russian energy industry
The study focuses on the possibilities of the influence of digital intelligence on the development of the Russian energy industry. The relevance of this topic lies in the fact that digital integration and the development of digital technologies, driven by dual aspirations for the transformation of energy and the digital revolution, have become inevitable trends in the modern development of the fuel and energy complex (fuel and energy complex). As a result of the conducted research, conclusions are formulated that reveal the key stages of the integration of digital intelligence and their impact on the further sustainable development of the fuel and energy complex of Russia.
Keywords: integration; artificial intelligence; digital intelligence, environmental modernization; fuel and energy complex; low-carbon energy; energy industry; digital technology.
T. A. Golovina, I. L. Avdeeva (Central Russian Institute of Management, Branch of RANEPA, Orel)
Methodological aspects of development of the ecological cluster of the mining industry of the Arctic zone of the Russian Federation on the basis of industrial symbiosis
The article is devoted to the development of a cluster model of circular economy development based on the principles of industrial symbiosis on the territory of the subjects of the Arctic zone of the Russian Federation. Based on the study of the essence of environmental clusters, classical models of their functioning, it is shown that an effective tool for their development is industrial symbiosis. The concept of industrial symbiosis acts as a fundamental platform for the joint inter-sectoral use of resources and waste in the ecological cluster and is based on process management and ecosystem approach. The organizational model of the ecological cluster of mining industry of Krasnoyarsk region on the basis of industrial symbiosis is proposed. This model is based on the refined principles of cluster theory, taking into account the peculiarities of the Arctic zone, as well as the strategic role of public authorities and the involvement of consumers in resource management processes.
Keywords: ecological cluster; industrial symbiosis; circular economy; Arctic zone.
T. B. Goldvarg, E. V. Petrovich, E. V. Sumyanova, S. S. Muchkaeva, J. N. Radachinskaya (Kalmyk State University named after B. B. Gorodovikov, Elista)
Development of a system of continuous corporate training and advanced training for personnel of high-tech enterprises in the context of accelerated digital transformation and transition to Industry 4.0
The article considers the development of a system of continuous corporate training and advanced training of personnel of high-tech enterprises in the context of accelerated digital transformation and transition to Industry 4.0. The purpose of the study is to develop a conceptual framework and practical recommendations for the formation of a flexible and adaptive system of corporate training that ensures the timely development of employees' competencies necessary for effective work in new technological realities. The key challenges of digital transformation for personnel competencies are identified, the factors of corporate training effectiveness are systematized. A conceptual model of the training system is proposed that integrates adaptive educational technologies, project and team learning, knowledge management tools. The model has been tested on the basis of three high-tech enterprises, proving its effectiveness (increase in engagement by 23,5 %, quality of competencies by 19,8 %). The theoretical and practical significance of the study is related to the development of human resource management methodology in the digital economy.
Keywords: corporate training; digital transformation; Industry 4.0; competency model; knowledge management; high-tech enterprises.
D. S. Shupnev, V. V. Krymsky, E. I. Dashkevich, R. A. Yurchenko (Saint Petersburg University of State Fire Service of Emercom of Russia, St. Petersburg); M. A. Mareev (Main Fire Department of the Ministry of Emergency Situations of Russia, Moscow)
On the need to develop a modern modular educational and training complex for rescuer training
The article discusses educational and training complexes (ETC) for performing exercises practiced during the training of gas and smoke protection workers for work in an environment unsuitable for breathing (UFB). Approaches to the architecture of constructing complexes and shortcomings of existing models from various domestic manufacturers are studied. As an alternative to the TEC, it is proposed to use modular technology using separate panels that will allow assembling individual TEC modules (rooms) for specific tasks solved by gas and smoke protection workers during work. The composition and capabilities of the TEC are described.
Keywords: gas and smoke protection workers; gas and smoke protection service; preparation; training; exercises; fire extinguishing; modular educational and training complex.
D. S. Shupnev, V. V. Krymsky, E. I. Dashkevich, R. A. Yurchenko (Saint Petersburg University of State Fire Service of Emercom of Russia, St. Petersburg); M. A. Mareev (Main Fire Department of the Ministry of Emergency Situations of Russia, Moscow)
Improving the professional training of firefighters at the present stage
The need to improve the professional training (PT) of the fire service (FS) personnel is determined. The analysis of the regulatory framework in the field of organizing the PT of the personnel of the Federal Fire Service (FFS) of the State Fire Service (SFS) and the conditions for performing exercises of practical importance is carried out. A model is proposed for improving approaches to the order of performing exercises, speed characteristics (exercise performance time) presented during their implementation, the most optimal algorithms for performing exercises, taking into account the modern technical equipment of fire and rescue units (FRU).
Keywords: fire protection; professional training; exercises; fire equipment; personnel; training manual.
M. G. Ogur, D. E. Nosikov, K. D. Gorlacheva, N. Dibrov (North Caucasus Federal University, Stavropol)
Mathematical method and algorithm for trusted interaction between agents of a decentralized Internet of
Things environment in continuous tasks with an infinite number of alternatives
A new mathematical method and algorithm for trusted interaction between agents in a decentralized Internet of Things (IoT) environment are developed. The main objective of the method is to ensure efficient, secure, and scalable interaction between agents in continuous tasks with an infinite number of alternatives, which is a pressing issue in the context of IoT network development. The developed method combines several key components, such as a dynamic reputation system, adaptive methods for optimizing agent actions, quantum cryptography for data protection, and the PoARE random consensus algorithm. The proposed approach is capable of solving a wide range of problems, including maximizing agent utility, minimizing resource costs, and increasing system security.
Keywords: decentralized environment of the Internet of Things; trusted interaction; trusted agents; dynamic agents; malicious agents; subject agents; object agents.
V. I. Uskov (Voronezh State University of Forestry and Technologies named after G. F. Morozov, Voronezh)
Solving the initial problem for a dynamical system of an unresolved relative to the highest derivative
For a dynamic system describing the processes that occur when a rigid body rotates with a cavity completely filled with an elastic medium (Lame equation), when reading information from a disk when modeling the work processes of a shockless trellis gripper with an energy-saving drive, the initial problem is solved. In the equation, before the second derivative, there is a Fredholm operator with zero index, which has a one-dimensional kernel. The conditions under which the solution exists, is unique or not unique, are determined. The solution is constructed in an analytical form.
Keywords: dynamic system; second-order differential equation; degenerate differential equation; Fredholm operator; cascade decomposition.