KSHP. OMD №5-2026 el.dop

D. V. Kolesnikova (Financial University under the Government of the Russian Federation, Moscow)

Integration of robotics in mechanical engineering

The presented article is devoted to current issues of robotization in production processes within the mechanical engineering and metallurgical industries. Particular emphasis is placed on analysing the capabilities and practices of using industrial robots and «smart» production lines to enhance the efficiency of production cycle management. Based on an analysis of open sources, industry publications, and materials from professional communities, the key trends in the development of automation are identified, along with existing challenges and technological constraints. The materials of the article have practical value for specialists and production managers who aim to improve the effectiveness of strategic management. The review of best practices may serve as a foundation for developing targeted programs for the implementation of robotics, both at the level of individual companies and across the industry as a whole.
Keywords: robotization; artificial intelligence; process automation; digitalization of production.

 

O. V. Demyanova, E. I. Mullakhmetov (Kazan (Volga Region) Federal University, Kazan)

Decomposition of the conceptual apparatus of technological transformation: essence and economic content of automation, robotization, and digitalization processes

In the context of the Fourth Industrial Revolution, the concepts of «automation», «robotization», and «digitalization» are often used interchangeably, which creates challenges for economic analysis and the development of corporate strategies. The purpose of this article is to systematize and clarify the key concepts of technological transformation from an economic perspective. The author proposes a methodological framework for decomposing these terms, which includes criteria such as the object of transformation, the nature of labor substitution, the type of decisions made, and the source of economic impact. The paper provides economically oriented definitions of automation, robotization, mechatronics, digitalization, and Industry 4.0, and analyzes their synergy and interconnection within a multi-level model. Special attention is paid to a comparative analysis of economic effects (cost reduction, increased flexibility, and creation of added value) and risks of technology implementation. The theoretical findings are supported by empirical data and industry-specific case studies (including those from Russia), demonstrating the practical significance of distinguishing between concepts for assessing investments, labor productivity, and public policy.
Keywords: automation; robotization; digitalization; mechatronics; Industry 4.0; technological transformation; economic effects; labor productivity; terminological decomposition; Fourth Industrial Revolution.

 

G. A. Gareeva (KNRTU-KAI’s Branch in Naberezhnye Chelny, Naberezhnye Chelny); A. G. Fayzullina, S. V. Bulatova (Kazan Federal University – Naberezhnye Chelny Institute, Naberezhnye Chelny); D. A. Grigorieva (Kazan State Power Engineering University, Kazan); A. R. Gazieva (College of the Kazan Innovative University named after V. G. Timiryasov, Naberezhnye Chelny)

Developing a desktop application for automating database operations

This article presents the development of a desktop application on the Windows platform using Windows Forms technology, including creating a PostgreSQL database and developing a user interface using Microsoft Visual Studio in the C# programming language, as well as the Bunifu UI and Kimtoo frameworks. This software product is designed for secure and efficient work with information, servers, and the automation of key database operations without restrictions.
Keywords: desktop application; database; automation; Windows Forms; PostgreSQL; Microsoft Visual Studio; Bunifu UI; Kimtoo.

 

I. V. Mirgaleeva, I. I. Frolova, Z. SH. Aglyamova (Kazan Innovative University named after V. G. Timiryasov (Naberezhnye Chelny branch), Naberezhnye Chelny); G. A. Gareeva (KNRTU-KAI’s Branch in Naberezhnye Chelny, Naberezhnye Chelny); A. G. Fayzullina (Kazan Federal University – Naberezhnye Chelny Institute, Naberezhnye Chelny)

A web-based project management system: monitoring, quality, and team coordination

This article presents the development of a project management website designed to monitor the results and quality of enterprise projects. The website promotes project management transparency, automated reporting, improved coordination between teams, and increased competitiveness. The website is implemented in Visual Studio Code (using MockAPI).
Keywords: website; project management; digitalization of project management; project management quality; monitoring; Visual Studio Code; MockAPI.

 

I. I. Eremina (Yelabuga Institute, branch of Kazan Federal University, Yelabuga)

Digital support for software licensing processes: creating an IT solution using the 1C:Enterprise platform

The paper addresses the development of an IT solution for the automation of licensing and accounting of commercial software products on the 1C:Enterprise platform. The relevance of the work stems from increasing losses caused by the unauthorized use of software and from the fragmentation of existing accounting tools, which do not form a unified information environment. The system architecture is described, including a web interface for a showcase of solutions, an order and payment management module, an authorized access control subsystem embedded in every commercial product, and a personal account for the counterparty. Mathematical models for request processing are presented: an order processing model, a Boolean model for verifying license authenticity, a recurrent model for calculating subscription cost, and a load forecasting model based on a Poisson process. Requirements for the database are formulated together with a description of the ER diagram, the main entities, and the integrity constraints. The design is carried out using the built-in language of the 1C: Enterprise platform and integration with the 1C: Counterparty service. The economic efficiency of implementation is analyzed: reduction of labor costs, fewer manual input errors, higher collection of subscription payments, faster order processing, and instant delivery of modules after payment. The solution makes it possible to minimize losses from the unauthorized use of software and to optimize the company's operational processes.
Keywords: licensing; commercial software product; IT solution; 1C:Enterprise; SaaS; solution showcase; counterparty personal account.

 

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

Economic assessment of robotization efficiency in manufacturing: an investment return model

Robotization of the manufacturing industry is transforming cost structures and enterprise productivity, yet quantitative assessment of the economic impact of industrial robot deployment at the sectoral level remains debatable. Meanwhile, the gap between leading economies, where robot density exceeds 400 units per 10,000 employees, and catch-up countries, including Russia, continues to widen. The study aims to develop a model for economic assessment of investment returns from robotization in manufacturing, accounting for sectoral heterogeneity of productivity factors. The methodological framework builds on a Cobb–Douglas production function extended with robotization capital as a separate factor, supplemented by a modified discounted cash flow method for calculating the payback period of robotic systems. The empirical base comprises Rosstat panel data on manufacturing for 2019–2025, International Federation of Robotics (IFR) statistics for 2018–2024, and data from the Russian Ministry of Industry and Trade on the industrial robot fleet. Modeling results indicate that the contribution of robotization to total factor productivity growth in Russian manufacturing ranges from 0,21 to 0,34 percentage points per year, with payback periods significantly differentiated across sectors: from 9 months in automotive to 4,5 years in food production. Practical significance lies in the model's applicability for substantiating priorities of state robotization support within the national project framework.
Keywords: robotization; manufacturing industry; total factor productivity; investment return; industrial robots; Industry 4.0; robot density.

 

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

Economic assessment of industrial robotization in the manufacturing sector: automation density and labor productivity

Robotization of the manufacturing industry is becoming a central mechanism for increasing labor productivity and transforming the sectoral structures of national economies. Recent empirical studies confirm the contribution of industrial robots to the growth of total factor productivity at the level of 0.36 percentage points annually, while simultaneously recording complex redistributive effects in the labor market – substitution of low-skilled workers with growing demand for highly qualified personnel. Under conditions of structural labor shortage and technological sovereignty objectives, Russia has intensified its robotization policy: in 2024 the industrial robot fleet reached 20,800 units with a density of 29 robots per 10,000 workers, placing the country 42nd in the global ranking. The aim of this study is to quantitatively assess the relationship between industrial robotization density and labor productivity in the manufacturing sector, identifying sectoral priorities and economic barriers to accelerated adoption. Methodologically, the study relies on comparative cross-country analysis of International Federation of Robotics (IFR) data for 2019–2024, Rosstat indicators for the manufacturing industry, and results of enterprise digital maturity surveys. The elasticity of productivity with respect to robotization density has been calculated, and sectoral implementation patterns have been systematized. The results demonstrate that achieving the stated goal – Russia's entry into the top-25 countries by robotization density by 2030 – will require an average annual fleet growth rate of 36 %, which under current investment dynamics and high key interest rate entails significant risks.
Keywords: industrial robotization; labor productivity; manufacturing industry; automation density; Industry 4.0; total factor productivity; structural labor shortage.

 

E. R. Zhdanov, A. V. Volkov, R. A. Yafizova, A. V. Kryukov, A. F. Yazykin (MIREA – Russian Technological University, Moscow)

Evaluating mechanical testing results using a digital twin

The article discusses the simulation of mechanical tests of flat samples made of high-temperature radiotransparent composite material VRCM in the ANSYS Composite PrepPost (ACP) program. The analysis of mathematical modeling of laminate on a fabric basis for the transition to a digital twin is carried out for the possibility of reducing the time for obtaining the finished product (part) due to mechanical tests on the example of the material. The article discusses aspects of modeling the structural and technological appearance of a sample of the plate type made of a laminated fabric composite material. This article provides a detailed analysis of one of the leading solutions in this area, the ANSYS Composite PrepPost (ACP) module integrated into the ANSYS Workbench environment. This article shows the steps involved in creating a mechanical tensile test specimen. A comparative analysis of the results of the obtained full-scale tests with the tests of the digital twin is carried out.
Keywords: product; technology; mechanical engineering; finite deformations; artificial intelligence; digital model; digital twin.

 

A. M. Kireeva-Karimova, G. A. Khaziakhmetova, S. M. Nuriyakhmetova (Kazan (Volga region) Federal University, Kazan); A. V. Zakharov (Gidravlika Trade LLC, Kazan)

Digital transformation and restructuring: drivers of efficiency growth in the machine-building industry

Modernization, as a process of updating and bringing into compliance with new standards, regulations, and technical and technological capabilities of the organization's production and sales activities, is a significant factor in ensuring the company's competitiveness. This is due to the changes in the characteristics of the production process and the adaptation of the offer to the requirements of the market. The comprehensive effect of production modernization can be identified by analyzing the dynamics of the system of indicators that characterize the production, sales, and financial components. In this regard, the article focuses on the impact of operational and strategic measures on the performance of four engineering companies: Gidravlika Trade, Rostselmash, Ruselprom and KAMAZ-Metallurgy. The study demonstrates that operational measures aimed at modernization help stabilize current processes and improve the efficiency of production and sales. Special attention is given to the role of science-intensive technologies in modernization, including Manufacturing Execution Systems (MES), digital twins, and Industrial Internet of Things (IIoT), which enable the transition to a data-driven proactive production management model. Strategic transformations contribute to long-term competitiveness through innovation adoption, product portfolio diversification, and new market development. The results indicate a synergistic effect of operational and strategic measures, leading to sustainable growth and enhanced adaptability of enterprises under conditions of digital transformation.
Keywords: modernization; restructuring; mechanical engineering; competitiveness; efficiency; innovation; losses; digitalization of business processes; stamping processes.

 

A. S. Shinkaryov, M. A. Gridnev (MISIS National University of Science and Technology, Moscow)

The role of shear deformations in polymer processing: a review

The paper considers the role of shear deformations in the processing of polymer materials. The main rheological patterns of polymer melt flow are analysed, including the dependence of viscosity on shear rate and the influence of molecular structure on the nature of flow. Theoretical models describing the viscoelastic behaviour of polymer systems are considered, including the Maxwell, Kelvin–Voigt, Ostwald–de Waale, and Carro–Yasuda models. Particular attention is paid to processes accompanied by intense shear deformations, such as liquid-phase extrusion and solid-phase extrusion. It is shown that shear deformations have a significant effect on the formation of polymer structures, including macromolecule orientation, phase domain refinement, and fibrillar structure formation. The influence of these structural changes on the mechanical properties of materials is considered.
Keywords: liquid-phase extrusion; solid-phase extrusion; shear deformation; polymers; rheology.

 

F. T. Ziganshina, R. R. Sadykova, K. Y. Ustyuzhanin (Ufa State Petroleum Technological University, Ufa)

Requirements analysis for developing a machine vision module for object recognition (Data collection and processing. Data architecture selection)

This article discusses the requirements for developing a machine vision module for object recognition. This project is part of the Strategic Technology Project «New Technologies, Technical, and Intelligent Systems in Mechanical Engineering» of the development program of the Federal State Budgetary Educational Institution of Higher Education «Ufa State Petroleum Technological University» for 2025–2036, in accordance with the «Priority 2030» strategic academic leadership program. Modern monitoring systems require automation of the object identification process. The development of this module is aimed at creating an effective recognition algorithm capable of operating in real time. A key stage of the project is high-quality data collection and preprocessing, as well as the selection of an optimal neural network architecture, which directly impacts the accuracy and speed of the system. Of particular interest is the use of machine vision for monitoring the position of a manipulator tool.
Keywords: machine vision; computer vision; structured objects; data architecture; neural networks; data collection and processing.

 

O. V. Zhuravleva (Financial University under the Government of the Russian Federation, Moscow); O. N. Mitrofanova (Lipetsk State Technical University, Lipetsk); E. Vuchkovich, (Academy of Applied Research in Belgrade, College of Tourism, Belgrade)

Innovative development in the Russian Federation

The article is devoted to the issues of innovative development in the Russian Federation. The relevance of the study is due to the fact that the development of digital infrastructure, an increase in investment in R&D and an increase in the share of innovatively active enterprises are key factors determining the dynamics of the country's innovative development. The aim of the work is to build and verify a multiple regression model describing the influence of factors of innovation activity and the introduction of ICT on the share of innovative products in GRP (yₓ), and to develop a forecast for 2025-2030. The object of the research is the innovative development of the Russian Federation; the subject is the quantitative relationship between Yₓ and factors x₁–x₁₂. Data sources are official publications by the Federal State Statistics Service.  (sections «Science, Innovation and Technology», «Technological development of economic sectors», «Efficiency of the Russian economy»), as well as HSE analytics. Methodological tools include statistical analysis, multiple regression, and graphical analysis. The practical significance of the forecast lies in the possibility of its application for strategic planning and adjustment of national programs: «Science and Universities», «Digital Economy», «Labor Productivity», etc. The results can be used in the development of regional socio-economic development strategies, innovation cluster programs, and digitalization roadmaps.
Keywords: innovative development; innovation; innovative potential; innovative products; technological innovation; multiple regression.

 

N. S. Sergienko (Financial University under the Government of the Russian Federation, Moscow)

A program-oriented approach to support the development of shipbuilding

The article is devoted to the application of a program-oriented approach in government support for the development of shipbuilding. It is proved that for the capital-intensive, technologically complex and strategically significant shipbuilding industry, fragmentary support measures have limited effectiveness, while the program-target format allows linking the goals of industrial, transport, scientific and technological, defense and regional policies. Based on the analysis of the Russian regulatory framework, strategic documents and existing support tools, the strengths and systemic limitations of program-oriented management in shipbuilding have been identified. Special attention is paid to the development strategy of the shipbuilding industry, preferential leasing mechanisms, measures to stimulate the localization of ship components, as well as investment mechanisms for the renewal of a specialized fleet. It is proved that the further development of the industry requires a transition from primarily production and quantitative logic to the logic of programming the full life cycle of a vessel, including design, serial production, component base, service, repair, digital support and staffing. The directions of improvement of the program-targeted approach are proposed: the formation of end-to-end industry programs, the transition to a system of technological contours, strengthening project discipline, the development of software lifecycle budgeting and the creation of mechanisms for responsibility for achieving final results.
Keywords: program-oriented approach; shipbuilding; government support; industrial policy; strategic planning; preferential leasing; import substitution; localization; ship life cycle; industrial development.

 

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

Instruments for assessing the effectiveness of industrial policy based on micro-level data and general equilibrium models for developing an evidence-based strategy

The article is devoted to the development and testing of tools for the quantitative assessment of industrial policy effectiveness based on the combination of micro-level data and general equilibrium models within the framework of an evidence-based strategy. The purpose of the study is to identify the causal effects of various support instruments on firms’ productivity, investment, financial stability, and macroeconomic aggregates using an extensive set of microdata (a panel of more than 2,4 million observations of non-financial enterprises for 2017–2023, integrating financial statements, registers of recipients of federal and regional subsidies, tax benefits, customs statistics, and indicators of foreign economic activity) and a rigorous econometric apparatus. The main methods used include DiD combined with PSM to form comparable Treatment/Control groups, the Levinsohn–Petrin method for total factor productivity estimation, instrumental variable models to mitigate endogeneity of support decisions, as well as a dynamic stochastic general equilibrium model with an intersectoral block, calibrated on the social accounting matrix and accounting for frictions in capital and labor markets. The results show a statistically significant increase in TFP among beneficiaries of support, accompanied by a decrease in ROA, an increase in investment leverage and debt burden, as well as the strengthening of allocative distortions in a number of high-tech industries, manifested in a rise in the dispersion of the marginal product of capital. CGE modeling demonstrates the advantage of infrastructural and horizontal measures that reduce transaction costs for a broad range of agents over selective «champion» support in terms of GDP growth, consumption, and inflation dynamics. The analysis of fiscal multipliers reveals higher cost-effectiveness and a smaller «deadweight loss» for repayable instruments (guarantees, interest rate subsidies) compared to direct grants. It is concluded that there is a need to transition toward evidence-based industrial policy relying on microdata, integrated CGE/DSGE models, online monitoring of effects, and the «smart leverage» of budgetary resources.
Keywords: industrial policy; microdata; general equilibrium models; total factor productivity; fiscal efficiency.

 

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

The potential of additive manufacturing in mechanical engineering (review)

Additive manufacturing has recently seen rapid growth in the mechanical engineering industry. The transition from traditional technologies to digital engineering, based on 3D printing, represents a significant breakthrough in design and manufacturing in mechanical engineering. This article presents a comprehensive literature review of additive technologies, analyzing their advantages and emerging technical challenges. It concludes with an analytical review of the need to reconsider 3D printing orientation strategies and process parameters, as well as to reform engineering education with an emphasis on project-based learning and the integration of modeling, design, and implementation processes.
Keywords: mechanical engineering; additive technology; manufacturing; design; 3D printing.

 

A. N. Semin, A. V. Kurdyumov, A. S. Lylov, V. A. Meshcheryagina (Ural State Economic University, Yekaterinburg)

Strategic analysis of the activities of a metallurgical plant in the production of iron, steel and ferroalloys

In the scientific article, the authors, on the basis of theoretical and methodological approaches, analyze the strategic development of the organization in the production of cast iron, steel and ferroalloys of Evraz NTMK JSC. It is proposed a list of main measures to implement the diversified growth strategy for Evraz NTMK JSC, the implementation of which will allow achieving the main goals in the medium term: to increase revenue by 0,3–0,4 %; increase profit from sales by 0,5 %, increase the market share of sales, increase the competitiveness of the enterprise, as well as improve the quality of products.
Keywords: strategic analysis; competitiveness; steel mill; iron production; steel production; ferroalloy production; market penetration strategies; diversified growth strategy. 

 

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

Econometric assessment of the impact of digital modernization on total factor productivity in Russian manufacturing

The slowdown in total factor productivity (TFP) growth in Russian manufacturing, observed since 2008, raises questions about the role that digital modernization can play in overcoming technological stagnation. This study provides an econometric assessment of how digital capital (investment in ICT, software, and automated equipment) contributes to TFP dynamics in Russian manufacturing industries. Results show a statistically significant positive effect of the digital capital share on TFP levels: a one percentage point increase in this share is associated with a 0,32–0,41 p. p. productivity index gain depending on specification. However, nonlinearity in returns was identified, with a threshold value estimated at 18–22 % of digital assets. The largest TFP gains were found in pharmaceuticals, electrical equipment, and the chemical industry; the smallest in wood processing and food production. Practical significance stems from the applicability of these estimates to justifying sectoral priorities within the national project «Data Economy».
Keywords: total factor productivity; manufacturing industry; digital capital; production function; Ackerberg–Caves–Frazer method; panel data; technological modernization.

 

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

Multilevel management of industrial clusters and technoparks as a driver for increasing productivity and innovation in the manufacturing industry

The article examines the impact of multilevel management of industrial clusters and technoparks on the productivity and innovation activity of manufacturing enterprises under the transition to an ecosystem model of industrial policy. The purpose of the study is to quantitatively assess the extent to which the management architecture and the degree of centralization of process coordination within a cluster determine the differences in operational, financial, and innovation performance of resident enterprises compared to autonomous firms. The information base consists of statistical data from Rosstat, the Ministry of Industry and Trade of the Russian Federation, and the Association of Clusters, Technoparks, and SEZs of Russia for 2018–2024, including over 12,000 observations on 145 industrial clusters, 80 technoparks, and 300 large enterprises in a control group. The methodological framework relies on stochastic frontier analysis (SFA) for assessing X-efficiency, integrated indices of innovation activity, an original scoring model of corporate governance quality, financial analysis based on RAS and IFRS reports, as well as structural equation modeling (SEM) and transaction cost analysis. It is shown that cluster residents demonstrate significantly higher revenue per employee, asset turnover, EBITDA margin, and shorter working capital cycles compared to autonomous companies, confirming the presence of a synergistic effect of multilevel coordination. It is established that strictly centralized management models provide the highest values of R&D share in revenue, ROI of innovation projects, share of innovative products, and patent activity, whereas «soft» service-based models lag behind across all key metrics. At the same time, optimal indicators of investment project launch speed and resident satisfaction are achieved at a medium level of centralization, indicating the necessity of hybrid organizational solutions. The results obtained justify a shift from quantitative expansion of the number of clusters toward focusing industrial policy on multilevel management standards, development of digital platforms, and KPIs that link government support measures to the quality of management practices and innovation performance.
Keywords: multilevel management; industrial clusters; technoparks; labor productivity; innovation activity.

 

S. G. Eremin (Financial University under the Government of the Russian Federation, Moscow); V. F. Babyna (Francisk Skorina Gomel State University, Belarus, Gomel)

The role of public procurement and regulatory experiments in stimulating demand for domestic high-tech solutions and import substitution

The article provides a quantitative assessment of the extent to which public procurement, combined with regulatory experiments, shapes sustainable demand for domestic high-tech solutions and contributes to real import substitution under conditions of sanctions and a policy of technological sovereignty. Based on depersonalized data from the Unified Information System for 2019–2023 (458,920 contracts under Federal Laws 44-FZ and 223-FZ, with a focus on IT, medical technology, energy engineering, and radio electronics), registries of experimental legal regimes, reports from 1200 technology companies, and a survey of 350 top managers, a comprehensive panel is constructed and analyzed using NLP-based semantic analysis of technical specifications, Difference-in-Differences models with Propensity Score Matching, regional cluster analysis, localization level estimation based on input–output tables, as well as Tobin's Q and ROIC indicators. A comparison between standard electronic auctions and specialized procedures (restricted tenders, offset contracts, SPIC, R&D procurement from a sole supplier) reveals a stable inverse correlation between the depth of price competition and the technological complexity of purchased products (r = –0,78): offset contracts provide the highest localization rate (over 85 %) and the greatest tax return per contract unit with minimal nominal price reductions, whereas auctions generate the largest “savings” but are accompanied by low localization and a predominance of rebranded imports. Companies participating in regulatory sandboxes demonstrate an average annual revenue growth 21 percentage points higher than the control group, a 3,7-fold increase in attracted investments, more than a twofold reduction in time-to-market, and nearly a fivefold decrease in compliance costs, confirming the role of deregulation as an indirect fiscal incentive. Sectoral analysis of import substitution dynamics reveals a successful price and quality breakthrough in industrial software (an increase in the share of domestic solutions by 33,6 percentage points with a negative price premium) and persistent price and technological barriers in hardware segments (microelectronics, medical technology). The integrated model shows that the combination of a «regulatory sandbox» with a subsequent offset contract produces a synergy exceeding the sum of individual effects by 45 %, while the currently dominant «price minimization» logic contradicts the objectives of technological sovereignty. The study concludes that it is necessary to transform the contracting system toward performance-based procurement, scale up offset mechanisms, and institutionally embed regulatory experiments into the procurement cycle as a key condition for turning public procurement into a driver of the national high-tech industry.
Keywords: public procurement; high-tech products; regulatory sandboxes; import substitution; technological sovereignty.

 

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

Synergy of industrial and regional policy leveling spatial disparities in productivity and income on the example of single-industry towns

The article is devoted to the analysis of the synergy between industrial and regional policies in leveling spatial disparities in labor productivity and household income, using the example of Russian single-industry towns. The purpose of the study is to quantitatively assess how the combination of instruments for supporting city-forming enterprises and the integrated development of the urban environment affects investment activity, economic structure, budgetary stability, and demographic indicators of single-industry municipalities. The empirical base includes panel data on 319 single-industry towns for 2018–2024, grouped by risk zones, using indicators of gross urban product, fixed capital investment per capita, wages, unemployment, migration balance, economic structure, and budgetary parameters. Methodologically, the study applies deflation of value indicators, Herfindahl–Hirschman and localization concentration indices, panel regressions with fixed effects, the difference-in-differences method to assess the effect of comprehensive programs, cluster analysis, and the construction of an integral «policy synergy index that accounts for the institutional environment. The results demonstrate significant differentiation in investment multipliers: in the «green» zone, each ruble of state support generates a multiple increase in private investment and higher growth rates of productivity and real wages compared to crisis-prone single-industry towns, while fiscal autonomy and the reduction of population dependence on social transfers are closely associated with economic diversification and the growth of the share of SMEs. It is shown that a decline in the economic concentration index and growth in «non-core» revenues is accompanied by improvements in fiscal indicators and quality of life, while a positive migration balance occurs only upon reaching threshold values of the synergy index. It is concluded that the simple summation of sectoral and territorial measures is insufficient to overcome the «single-industry town trap»; a differentiated, project-oriented model of coordination between industrial and regional policies is required, linking support for city-forming enterprises with the objectives of diversification, enhanced fiscal autonomy, and human capital retention.
Keywords: single-industry towns; industrial policy; regional policy; spatial inequality; policy synergy.

 

I. A. Eremina (Peter the Great St. Petersburg Polytechnic University, St. Petersburg)

Evaluation of emergent properties of the marketing environment for mechanical engineering for the Arctic agro-industrial complex based on quantum machine learning

The article substantiates the need to develop a specialized methodological apparatus for assessing the emergent properties of the marketing environment of the mechanical engineering segment for the agro-industrial complex (AIC) of the Arctic, arising in the context of digital transformation. It is shown that existing models of industrial marketing do not take into account the qualitatively new effects generated by end-to-end technologies in the production and service of high-tech equipment in high-latitude territories. Based on the integration of industrial marketing theory and the apparatus of quantum computing, a hybrid QML mechanism (quantum coding of features, a variational quantum classifier, and fuzzy logic) is proposed, validated on panel data from 23 mechanical engineering and service enterprises supplying equipment for the AIC of the Murmansk region and the Yamalo-Nenets Autonomous Okrug (N = 276). Three emergent properties are identified and conceptualized for the first time: network synergy of remote service and spare parts logistics platforms, digital Arctic brand capital of equipment reliability, and adaptive price homeostasis of service contracts. The developed mechanism ensures their identification on a lead horizon of 2,5–3 months, which is 5–6 times greater than the capabilities of traditional methods with an F1-score increase of 30–39 %.
Keywords: Аrctic zone of the Russian Federation; mechanical engineering for the agro-industrial complex; digital transformation; emergent properties; industrial marketing; quantum machine learning; fuzzy logic; blockchain verification.

 

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

Mission-oriented industrial policy goal-setting, priority selection and program architecture in high-tech industries

The article is devoted to substantiating the parameters of mission-oriented industrial policy in high-tech industries, which involves a transition from universal horizontal support measures to targeted missions with clearly defined quantitative indicators. The aim of the study is to develop an approach to goal-setting, priority selection, and the architecture of government programs, taking into account the industry-specific risk profile, structure of investment flows, and organizational forms of project implementation. The empirical basis includes more than 15000 observations of 450 large industrial and technological companies and 120 federal and regional programs for the period 2018–2024; data are drawn from Rosstat, sectoral ministries, international organizations, and technological audits. The methodological framework includes DEA efficiency analysis, input–output analysis for assessing fiscal multipliers, clustering of enterprises and regions by technological readiness level, network analysis of cooperative linkages, Monte Carlo simulation for risk assessment, and panel analysis of lag effects from R&D investment. Quantitative estimates of investment leverage have been obtained for four priority missions (AI and data analytics, biotechnology, advanced manufacturing technologies, new energy), revealing a threefold difference in their capacity to attract private co-investment and a significant spread in revenue growth rates. It is shown that the key bottleneck is the «valley of death» between R&D and production scaling, characterized by extremely high capital costs and high project mortality, which necessitates specialized mezzanine financing instruments and interest rate subsidies. A comparison of federal innovation centers, regional clusters, and private technology parks demonstrates higher labor productivity, patent activity, and return on public investment in centralized structures, while also indicating a significant potential of private initiatives. The study concludes that a differentiated program architecture is needed for different types of missions, along with the introduction of a portfolio approach to risk management and a shift in the state’s role from passive subsidization to that of a professional investor and architect of innovation ecosystems.
Keywords: mission-oriented industrial policy; high-tech industries; efficiency of government programs; investment leverage; «valley of death».

 

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

Digital platforms, interoperability standards, and industrial policy: accelerating the adoption of the industrial internet of things and artificial intelligence in manufacturing

The article is devoted to identifying the economic role of interoperability of digital platforms and data exchange standards in accelerating the adoption of the industrial internet of things (IIoT) and artificial intelligence (AI) systems, overcoming the «productivity paradox», and increasing the return on digital investments in the manufacturing sector. The purpose of the study is to quantitatively assess the impact of the level of standardization and openness of platform architecture on the structure of CAPEX and OPEX, timelines and risks of digital projects, operational efficiency (OEE), and financial performance of industrial enterprises, as well as to conduct a comparative analysis of industrial policy models that encourage interoperability. The empirical basis consists of data from 1,240 enterprises across 18 industrialized countries for the years 2018–2024, supplemented by regulatory and sectoral statistics. The methodological framework includes the development of a composite interoperability index based on 34 indicators and PCA, panel regression models with fixed effects and instrumental variables, DCF analysis taking into account technology risk premiums, Monte Carlo scenario modeling, as well as in-depth interviews with CTOs and CDOs. It is shown that a decrease in interoperability levels leads to more than a twofold increase in integration cost share, significant extension of implementation timelines, and a sharp rise in budget overruns, effectively transforming integration expenditures into an «innovation tax», particularly in capital-intensive sectors. A comparison of proprietary, open, and hybrid platforms demonstrates that despite higher initial CAPEX, open architectures provide a significantly lower TCO, higher free cash flow, and nearly double project IRR growth due to reduced OPEX and expanded integration opportunities. The cross-country analysis of industrial policy instruments reveals differences between regulatory, market-based, and directive models of standard support and shows that the maximum multiplier of private investment is achieved when public support is focused on the development of open standards and consortia. The conclusion is drawn that industrial policy should be reoriented from subsidizing «digitalization as such» toward promoting interoperability and accounting for the «digital compatibility coefficient» as a key parameter of industrial asset investment attractiveness in the context of ecosystem competition.
Keywords: digital platforms; interoperability; industrial internet of things; artificial intelligence; industrial policy.

 

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

Digitalization of production processes and the industrial Internet of Things as factors of increasing competitiveness and reducing transaction costs

The article is devoted to a comprehensive assessment of the impact of digitalization of production processes and the implementation of the industrial Internet of Things (IIoT) on the competitiveness of industrial enterprises through the lens of reducing transaction costs and improving operational efficiency. The aim of the study is to quantitatively measure the effect of digital investments on the cost structure, working capital management, product quality, and financial performance of companies across different industries and levels of digital maturity. The empirical base consists of depersonalized financial and operational data from 458 enterprises in 12 sectors of the economy for 2018–2023, as well as survey results from top management. The study employs panel data regression analysis with fixed effects, DEA analysis, factor analysis, simulation modeling of IIoT implementation scenarios, and multifactor methods for detecting outliers and adjusting shocks. It is shown that the implementation of integrated IIoT platforms reduces unit transaction costs by 30–36 % and significantly increases OEE, while the effect varies across industries: the greatest gains are observed in capital-intensive and logistically complex production. Digital leaders demonstrate nearly a twofold acceleration of inventory turnover, a reduction of the financial cycle by more than a month, a multiple decrease in the share of illiquid inventory, and a significant increase in ROCE compared to lagging companies. A sharp decrease in internal defects and warranty claims is noted, accompanied by an increase in the customer loyalty index, while the overall impact on the business model is expressed in an EBITDA margin growth of 4–7 percentage points and an increase in market capitalization over a 3–5-year horizon; each ruble invested in IIoT yields 3,4 rubles of additional marginal profit in the long term. The discussion of the results emphasizes the exponential nature of the relationship between efficiency and the level of digital maturity, the risk of deepening digital inequality, and the need to interpret digitalization expenditures as strategic investments, accompanied by the development of industry standards for data exchange and ecosystem interaction in value chains.
Keywords: digitalization of production processes; industrial Internet of Things; transaction costs; operational efficiency; competitiveness of enterprises.

 

O. V. Demyanova, S. M. Nuriyakhmetova, A. M. Sangaliev (Kazan (Volga Region) Federal University, Kazan)

Economic efficiency of using unmanned flying appliances in the oil and gas industry

The purpose of this article is to analyze the economic efficiency of using UAVs in the oil and gas industry. The relevance of this topic lies in the rapid development of unmanned aerial vehicle technology in the Russian Federation and the unlimited possibilities for using these devices in various sectors of the economy. The object of this study is the field of unmanned aerial vehicles, and the subject of the study is the system of economic relations that arise when using UAVs in the oil and gas industry. The article explores the potential for implementing UAVs in oil and gas companies. This work used empirical and experimental-theoretical research methods (experiment, analysis, synthesis, induction, deduction). The development of the BAS market will continue at an increasing pace and contribute to the overall economic efficiency of organizations that implement this innovative technology.
Keywords: UAV; BAS; global market; economic efficiency; innovative technologies; drone; economic efficiency; oil and gas industry.

 

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

Institutional conditions for the success of industrial policy: the role of independent assessment of transparency and feedback in project management

The article is devoted to the analysis of institutional conditions determining the success of industrial policy, with a focus on the role of transparency, independent evaluation, and feedback mechanisms in the project management of capital-intensive public and public–private initiatives. The aim of the study is to quantitatively assess how the quality of institutional architecture – the level of data openness, audit independence, and speed of managerial response – affects budget overruns, implementation timelines, profitability, and sustainability of industrial projects. The empirical base consists of data on 480 investment projects in 24 countries for 2012–2024, divided by infrastructure, energy, high-tech manufacturing, and biotechnology sectors; the study uses reports of audit chambers, open contract registries, IMF and World Bank data, as well as corporate reporting. The methodology includes the construction of a composite transparency and accountability index (TAI), a management responsiveness coefficient, panel data econometric analysis with fixed effects, Difference-in-Differences to assess the impact of independent audit introduction, a modified CAPM model for risk-adjusted return calculation, and NLP-based sentiment analysis of independent reports. The results show that the transition from a low to a high level of transparency in the energy and infrastructure sectors reduces budget overruns by 3–4 times and significantly increases the ROI and SROI of projects, while in high-tech industries the effect on profitability is maximal. A nonlinear relationship between performance and audit frequency has been established: the optimum is achieved with two audits per year, after which additional control cycles lead to an increase in administrative costs and a decrease in capital allocation efficiency. The analysis of feedback mechanisms demonstrates that the transition to digital and agile management models reduces response time from months to days, significantly lowers cash flow volatility and default probability, and ensures a high value retention ratio under external shocks. The study concludes the existence of «institutional alpha»: investments in transparency, independent audit, and rapid feedback loops are equivalent to a substantial reduction in the cost of capital and should be regarded as a key direction for improving industrial policy.
Keywords: industrial policy; institutional environment; transparency; independent audit; project management.

 

Yu. A. Krupnov (Financial University under the Government of the Russian Federation, Moscow)

Production efficiency management of machine-building enterprises based on digital technologies and KPI

In the context of industrial digital transformation, improving production efficiency has become a key factor in the competitiveness of mechanical engineering enterprises. The increasing complexity of technological processes, the need to reduce equipment downtime, improve product quality, and optimize resource utilization create a growing demand for the integration of digital technologies and key performance indicators. The purpose of this study is to develop an organizational and economic approach to managing production efficiency at mechanical engineering enterprises based on digital technologies and key performance indicators. The methodological framework is based on systemic, process, and institutional approaches, as well as comparative, economic-statistical, and KPI analysis methods. The scientific novelty of the research lies in the development of a model integrating digital technologies – Manufacturing Execution Systems (MES), Enterprise Resource Planning systems (ERP), the Industrial Internet of Things (IIoT), and predictive analytics – with a system of key performance indicators, enabling quantitative assessment of the impact of digitalization on productivity, product quality, and equipment utilization. The practical significance of the results lies in their applicability to the development of digital transformation programs and operational management systems for mechanical engineering enterprises.
Keywords: production efficiency; digital transformation; digital technologies; key performance indicators (KPI); overall equipment effectiveness (OEE); industrial production; Industry 4.0; organizational and economic mechanism; Manufacturing Execution Systems (MES); Enterprise Resource Planning (ERP); Industrial Internet of Things (IIoT).

 

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

Economic assessment of digital marketing effectiveness in manufacturing enterprises

The Russian manufacturing industry demonstrates sustained positive dynamics; however, marketing practices in the sector remain predominantly traditional, reducing the efficiency of commercial strategies in the context of economic digitalization. Digital marketing, which has proven effective in consumer industries, is still weakly integrated into the B2B segment of the industrial market, where long procurement cycles, multi-level decision-making procedures, and the specifics of technical communication significantly limit the direct adoption of consumer models. The purpose of this study is to provide an economic assessment of digital marketing effectiveness in Russian manufacturing enterprises based on the analysis of engagement, conversion, and customer acquisition cost metrics. The methodology combines statistical analysis of Rosstat data for 2020–2025, content analysis of marketing strategies of 84 enterprises in mechanical engineering, chemical, and food industries, and ROI calculation across digital marketing channels. Significant differences in the effectiveness of digital channels between sub-sectors were identified. The results allow for justifying investment priorities in digital marketing for industrial enterprises of various scales and industry affiliations.
Keywords: digital marketing; manufacturing industry; B2B marketing; economic effectiveness; customer acquisition cost; conversion; marketing analytics.

 

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

Assessing the economic efficiency of digital marketing transformation in industrial enterprises

Digital marketing transformation in industrial enterprises is becoming a prerequisite for maintaining competitive positions amid accelerated technological modernization of the manufacturing sector. However, assessing the economic returns from implementing digital marketing tools in the industrial B2B segment remains a problematic area: traditional approaches developed for consumer markets poorly account for the specifics of long sales cycles, limited customer bases, and high contract values. The study aims to develop a methodological approach for assessing the economic efficiency of digital marketing in Russian manufacturing enterprises based on integrating customer acquisition cost (CAC), customer lifetime value (CLV), and return on marketing investment (ROMI) metrics. The methodology relies on comparative analysis of financial and marketing indicators from 86 manufacturing enterprises over 2021–2025, segmented by the digital maturity level of marketing functions. The results indicate significant differentiation: enterprises with high marketing digitalization demonstrate a 28–35% reduction in customer acquisition costs, a 1.6–2.1-fold increase in conversion rates, and a 40–55% ROMI growth compared to enterprises with predominantly traditional marketing instruments. Threshold investment values in MarTech stacks were identified at which nonlinear returns growth is observed. A modified formula for calculating a comprehensive digital marketing efficiency index for industrial enterprises is proposed.
Keywords: digital marketing; industrial enterprise; manufacturing industry; return on marketing investment; customer acquisition cost; digital transformation; B2B marketing.

 

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

Project-based integration of lean manufacturing and digital technologies in the processing industry: labour productivity under structural workforce shortage

Russia's manufacturing sector faces a structural labour deficit of approximately 1,9 million workers while unemployment stands at a record-low 2,3 %. This paper investigates a project management approach to the simultaneous deployment of lean manufacturing and Industry 4.0 technologies as a compound productivity driver. A comparative analysis of 42 enterprises participating in the national Productivity programme (2022–2025) reveals that sites employing project-based synchronisation of lean and digital initiatives achieved a median 18,7 % gain in output per worker over a 12–18-month project cycle, compared with 9,2 % at sites pursuing these agendas separately. Workforce turnover fell by 11 percentage points in the project-managed group. Key barriers include a shortage of middle-management competencies and misaligned planning horizons for digital and lean projects.
Keywords: labour productivity; lean manufacturing; Industry 4.0; project management; manufacturing sector; workforce deficit; digital transformation.

 

P. I. Chuvakhin (Financial University under the Government of the Russian Federation, Moscow)

Legal regulation of industrial safety at hazardous production facilities in the context of digital transformation of supervisory activities

Ensuring industrial safety at hazardous production facilities (HPFs) remains a priority of the Russian Federation's state policy, affecting over 195,000 facilities supervised by Rostechnadzor. The transition to a risk-based model of supervisory activities, legislatively established by Federal Law No. 248-FZ of 31.07.2020, transforms the mechanisms of legal regulation by shifting the emphasis from scheduled inspections to preventive monitoring and remote control. The study aims at a comprehensive legal analysis of the transformation of regulatory mechanisms for ensuring industrial safety at HPFs driven by the introduction of digital supervisory tools, as well as an assessment of the effectiveness of this transition in terms of accident rate reduction. The methodology relies on formal-legal, comparative-legal, and statistical methods. The regulatory framework includes federal laws, governmental acts, and Rostechnadzor orders effective as of late 2025. The experience of the European Union (Seveso III Directive) and the United States (OSHA standards) in the context of industrial oversight digitalization is analyzed. It is shown that the legislative modernization of 2023–2025, including the introduction of mandatory safety management system audits for Class I HPFs and the implementation of remote monitoring, creates legal prerequisites for reducing administrative burden while improving the quality of control. Legal gaps impeding the full digitalization of supervisory procedures are identified, and directions for improving the regulatory framework are proposed.
Keywords: industrial safety; hazardous production facilities; risk-based approach; digital transformation; supervisory activities; Rostechnadzor; Seveso III Directive.

 

P. I. Chuvakhin (Financial University under the Government of the Russian Federation, Moscow)

Legal mechanism of the risk-oriented approach to ensuring industrial safety at hazardous production facilities: assessment of regulatory effectiveness

Ensuring industrial safety at hazardous production facilities (HPFs) remains a priority of state policy amid large-scale reindustrialization and digital transformation of supervisory activities. The reform of the control and supervisory functions of Rostechnadzor, initiated by Federal Law No. 22-FZ of 04.03.2013 and developed in Federal Law No. 248-FZ of 31.07.2020, led to the implementation of a risk-oriented regulatory model that differentiates the intensity of control depending on the hazard class of the facility. The study aims to comprehensively assess the legal framework and regulatory effectiveness of the risk-oriented approach in the industrial safety system of the Russian Federation using data from 2019–2025. The methodological basis includes formal legal and comparative legal analysis of regulatory acts, statistical analysis of Rostechnadzor data on accidents, incidents, and the structure of the state register of HPFs, as well as legal modeling to evaluate the effectiveness of regulatory instruments. Results demonstrate heterogeneous effects: a decrease in accidents at Class I–II facilities is accompanied by an increase in violations at Class III–IV facilities, where scheduled inspections are conducted less frequently. It was established that the moratorium on control and supervisory activities in 2022–2023 reduced inspections by 50 %, but led to a 15 % increase in detected violations in 2024. A mathematical model for assessing regulatory pressure based on the supervisory coverage ratio is proposed. Recommendations for improving regulatory framework are developed, including integration of remote monitoring into the legal framework of production control.
Keywords: industrial safety; hazardous production facilities; risk-oriented approach; Rostechnadzor; control and supervisory activities; regulatory effectiveness; remote monitoring.

 

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

Economic security of Russian manufacturing industry under technological transformation: import dependence, structural shifts, and resilience factors

Manufacturing holds a central position in the national economic security system, determining technological sovereignty and the sustainability of reproduction processes. The large-scale restructuring of foreign economic relations that began in 2022 led to an accelerated revision of established industrial development models. The study aims to comprehensively assess the transformation of import dependence in Russian manufacturing and to identify structural factors determining industrial sector resilience to external shocks in the context of economic security. The methodology is based on a combination of structural-dynamic analysis of Rosstat data for 2020–2025, coefficient-based assessment of import dependence by industry groups, and an index approach to measuring the adaptive potential of industrial subsystems. An integral indicator of technological sustainability of the manufacturing sector was introduced, aggregating indicators of production localization, processing depth, innovation activity, and workforce capacity. Results demonstrate heterogeneity in adaptation processes: while overall production dependence on imports decreased from 12,1 to 7,6 % of material costs in 2021–2023, several high-tech sectors retain critical dependence on foreign technologies and equipment. The mechanical engineering complex, which accounted for almost 40 % of the manufacturing sector's growth in 2024, remains vulnerable in terms of machine tool and electronic component resources. The practical significance lies in the potential application of the proposed tools in developing industry-specific strategies to ensure the economic security of the industrial complex.
Keywords: economic security; manufacturing industry; import dependence; technological sovereignty; structural transformation; industrial policy; adaptive potential.

 

S. A. Petrova (Russian Presidential Academy of National Economy and Public Administration North-Western Institute of Management, RANEPA, Saint Petersburg)

Development of media literacy in future industrial specialists using innovative educational technologies

The article presents the results of a mixed-methods quasi-experimental study aimed at identifying effective educational technologies for developing media literacy among future industrial specialists. The empirical base included 1298 students of technical specialties from six universities and eight industrial partners, observed over two academic cycles with delayed measurement after eight weeks. The author's media literacy index for an industrial specialist, aggregating five substantive blocks (source reliability verification, interpretation of industrial media messages, reading of data visualizations, understanding the limitations of AI recommendations, and quality of communication in industrial briefings), was applied to assess four educational formats: a traditional LMS environment, a project-case track with fact-checking, VR/AR simulators, and a hybrid loop with a digital twin and an adaptive AI mentor. The results demonstrate a pronounced technological gradient: the hybrid model produced an index growth 3,43 times higher than the control value and the highest quality of skill transfer to educational-industrial scenarios. The financial-analytical block recorded a weighted average payback period of 7,11 months and a net effect of 15,81 thousand rubles per student per year, which allows media literacy to be considered as part of the human capital of an industrial organization. A cascade model for the implementation of media literacy educational technologies in engineering training is formulated.
Keywords: media literacy; engineering education; innovative educational technologies; virtual reality; digital twin; artificial intelligence; industrial training; human capital.

 

N. A. Zavyalova (Ural State Pedagogical University, Yekaterinburg)

Training personnel for Russian industrial enterprises using the pedagogical potential of domestic cinema in the vocational education system

The article examines the pedagogical potential of domestic cinema as a tool for training personnel for Russian industrial enterprises within the system of secondary vocational education. The relevance of the study is determined by the systemic personnel shortage in manufacturing industries and the simultaneous institutional expansion of the Professionalitet project, which by mid-2025 had united 493 clusters, 1,470 educational organisations and 2,430 employer partners. The information base includes official statistics from Rosstat, the Bank of Russia, the Ministry of Education and the Ministry of Culture, along with a body of scientific and pedagogical works on cinema pedagogy and the educational film. Empirical approbation was carried out on the basis of eight colleges and twelve industrial enterprises in six regions: the sample included 624 students in welding production, computer numerical control machine tools, mechatronics and industrial safety, divided into pilot and control groups of 312 students each. The cinema-pedagogical module was built according to the scheme «fragment discussion – professional case – practical consolidation». The results show a 9,18 % increase in the average practical examination score, a 26,04 % reduction in occupational safety errors and a 12,86 percentage-point increase in readiness to work in the chosen specialty within the pilot group. The aggregate budgetary return ratio reached 2,56, and 2,87 under regional scaling. It is concluded that domestic cinema, when methodologically integrated, becomes an economically justified instrument for training industrial personnel.
Keywords: cinema pedagogy; domestic cinema; secondary vocational education; industrial personnel training; professional identity; economic efficiency of education; Professionalitet.

 

J. S. Buzykova (Financial University under the Government of the Russian Federation, Moscow) 

Generative artificial intelligence in higher education pedagogy: impact on learning outcomes and readiness of the teaching environment

This article examines the pedagogical potential of generative artificial intelligence (GenAI) in higher education. Interest in this topic stems from the fact that tools like ChatGPT, Gemini, and Copilot are rapidly penetrating everyday teaching practices, yet systematic assessments of their actual impact on academic outcomes remain scarce, and models for preparing faculty to work with these technologies remain rudimentary. The goal of this study is to establish the nature and scale of GenAI's impact on student academic achievement and simultaneously analyze the structural components of faculty readiness for the meaningful implementation of generative tools. Methodologically, the study relies on a mixed-methods design: the quantitative component is a secondary analysis of data from five large meta-analyses conducted in 2024–2025, comprising over 300 experiments; the qualitative component is a content analysis of 40 institutional policies from universities in six global regions and a review of studies on AI-TPACK. A statistically significant positive effect of GenAI on academic performance (weighted mean g ranging from 0.79 to 1.14), cognitive engagement, and higher-order thinking is found. However, the effects varied significantly depending on the course's discipline, the duration of the intervention, and the chosen teaching model. Regarding the pedagogical aspect, a characteristic gap is observed: teachers are confident in using digital tools in general, but their integrated AI-TPACK competence falls short of the threshold. A typology of barriers to GenAI integration is proposed, and the conditions under which the inclusion of generative technologies in the educational process can be truly productive are identified.
Keywords: generative artificial intelligence; higher education; adaptive learning; AI-TPACK; learning outcomes; AI literacy; pedagogical integration of technologies.

 

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

Project approach to labor productivity management in manufacturing enterprises under structural workforce shortage

Russian manufacturing is undergoing a period of simultaneous production growth and deepening structural labor shortage: by the end of 2024, the labor productivity index in the sector reached 104,5 %, while unmet staffing needs exceeded 400,000 workers. This combination necessitates a shift from extensive staffing models to project-oriented mechanisms for improving labor efficiency. The study aims to substantiate a project-based approach to labor productivity management in manufacturing enterprises, accounting for labor market constraints and digital transformation potential. The methodology is based on comparative analysis of Rosstat industry statistics, HSE business survey data, and results of the national project «Labor Productivity» for 2020–2025. Regional grouping by workforce vulnerability level, correlation analysis of output dependence on technological equipment and personnel qualification factors, and management practice benchmarking elements were employed. Results reveal significant productivity differentiation across manufacturing technology levels: medium-tech industries demonstrate an index of 107,9 %, high-tech – 105,3 %, low-tech – 102,5 %. A project-based productivity management model integrating lean manufacturing tools, workplace digitalization, and workforce strategy restructuring is proposed. Practical significance lies in the model's applicability for developing corporate productivity programs under staffing constraints.
Keywords: labor productivity; manufacturing industry; structural workforce shortage; project approach; lean manufacturing; digital transformation; human resource management.

 

A. A. Makarevich-Konstantinova (Petrozavodsk State University, Petrozavodsk)

The relationship between accelerating information flows, decreasing attention spans, and changing productivity patterns in a highly competitive economy

This paper examines the interdependence between the increasing intensity of information flows, the persistent decline in attention spans, and the restructuring of productivity patterns in knowledge workers. The problem is acute due to the growing discrepancy between the amount of digital stimuli generated by modern communications infrastructure and how much the human brain can process without loss of quality. The global economic damage from loss of focus is measured in trillions of dollars, and this is no longer a matter of personal discipline—it has become a macroeconomic problem. The aim of this paper is to uncover the mechanisms through which information overload affects attention parameters and to trace how these changes are projected onto productivity in a competitive economy. Methodologically, the study relies on a systematic review of peer-reviewed publications from 2004–25 (n = 87, English- and Russian-language sources), secondary quantitative analysis of data from major industry reports (Gallup, Microsoft, Economist Impact), and content analysis of corporate productivity surveys. The results show that the average time spent focusing on a single screen has shrunk from 150 seconds in 2004 to 47 seconds in 2021, with the number of task switches per workday exceeding 560. Multitasking wastes up to 40% of effective time, and it takes an average of 23 minutes to restore concentration after a single interruption. This suggests that linear productivity models have exhausted their usefulness and need to be replaced by adaptive models that can account for workers' cognitive limitations in an information-rich environment.
Keywords: information overload; attention span; productivity; cognitive load; multitasking; attention economy; knowledge workers.

 

M. V. Polevaya (Financial University under the Government of the Russian Federation, Moscow)

Labor shortage and labor productivity in manufacturing: HR analytics as a tool for workforce optimization

Russia's manufacturing sector is experiencing an unprecedented structural labor shortage, reaching 1,9 million unfilled positions by 2024, while employment in the sector grew by 7 % during 2021–2024. This study provides a quantitative analysis of the interdependence between workforce adequacy and labor productivity in Russian manufacturing enterprises, identifying the role of HR analytics as a mechanism for compensating deficit constraints. The aim is to construct a factor model of labor productivity that accounts for staffing levels, training expenditures, employee turnover, and the degree of HR process automation. The methodology includes correlation-regression analysis of panel data across 12 manufacturing subsectors for 2019–2025, using data from Rosstat, Bank of Russia monitoring, and surveys by the Institute of Economics of the Russian Academy of Sciences. Results indicate that a 10 % decrease in staffing adequacy leads to a 6,2–8,4 % reduction in output, with a nonlinear relationship mediated by sectoral capital intensity. Enterprises that have implemented predictive HR analytics tools demonstrate 14–18 % higher productivity at comparable deficit levels. The practical significance lies in the applicability of the developed model for industrial workforce strategies and regional labor development programs.
Keywords: manufacturing industry; labor shortage; labor productivity; HR analytics; human capital; factor model; personnel management.

 

M. V. Polevaya (Financial University under the Government of the Russian Federation, Moscow)

Structural labor shortage in Russian manufacturing: quantitative assessment of productivity losses and enterprise adaptation strategies

The Russian manufacturing industry operates under conditions of an unprecedented labor shortage, which reached a historical maximum by 2024. The lack of qualified workers and engineering and technical specialists creates systemic constraints on output growth, despite steady increases in capacity utilization and expanding order portfolios. This study aims to quantitatively assess the impact of structural labor shortages on labor productivity in manufacturing industries and to systematize the adaptation strategies implemented by industrial enterprises. The methodological framework combines correlation-regression analysis of panel data from Rosstat and HSE for 2019–2025 with content analysis of corporate reports and business climate survey results. Productivity elasticity estimates by staffing levels were obtained for three technological strata of manufacturing. A nonlinear relationship was identified: productivity losses from understaffing are significantly more pronounced in high-technology industries than in low-technology ones, which is determined by specific competency requirements. Five clusters of adaptation strategies were systematized, and an integral model for assessing their comparative effectiveness was proposed.
Keywords: manufacturing industry; labor shortage; labor productivity; adaptation strategies; labor market; staffing levels; technological strata.

 

E. L. Cherkashina (Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, Moscow); E. V. Pinevich (Bauman Moscow State Technical University, Moscow); O. V. Ponomareva (Moscow State University of Civil Engineering, Moscow)

Development of teaching and methodological materials on Russian as a foreign language for mastering industrial enterprise terminology and documentation

This article is devoted to the development and testing of a teaching and methodological package on Russian as a foreign language for students majoring in engineering and technical fields and aimed at developing skills in working with industrial enterprise terminology and documentation. The relevance of this topic is determined by the growing number of international students at Russian technical universities: according to the Ministry of Education and Science, by 2025 their enrollment will exceed 400,000, while the language barrier remains a serious obstacle to academic and professional integration. The aim of this study is to develop a methodological model for teaching professionally oriented Russian, based on an analysis of student needs and a genre-discursive principle for selecting language material. Methodologically, the study combines: a needs analysis through a survey of 186 students and 24 faculty members from three universities; Content analysis of an industrial documentation corpus (412 text units); a pedagogical experiment with control and experimental groups. The result is a teaching and methodological complex consisting of five thematic modules—from basic technical terminology to design and estimate documentation. An experimental test, conducted on a sample of 94 students at levels B1–B2 (TORFL certification levels I–II) over two semesters of the 2024/25 academic year, records a 31 % increase in terminological competence and a 26 % improvement in the quality of business text production relative to the control groups. The obtained results demonstrate the effectiveness of integrating corpus data from industrial enterprises into the educational process and can be used when designing professionally oriented Russian as a foreign language courses at technical universities.
Keywords: Russian as a foreign language; language of specialization; professional terminology; industrial documentation; teaching and methodological complex; needs analysis; genre-discursive approach.

 

E. N. Yalunina, O. A. Kolotkina, I. D. Yagofarova (Ural State University of Economics, Yekaterinburg)

Russia's competitive policy in the new reality: from market protection to economic sovereignty

The article discusses current issues of Russia's competitive policy in the modern period. It analyzes the government's measures to influence the competitive environment in various sectors of the economy over the past few years, draws reasonable conclusions, and proposes a set of recommendations aimed at improving the effectiveness of the government's competitive policy.
Keywords: competition; digital platforms; risk-based approach; government regulation; antitrust regulation эффективности конкурентной политики государства.

 

A. B. Mottaeva (Financial University under the Government of the Russian Federation, Moscow); Y. A. Gordeyeva (K. Kulazhanov Kazakh University of Technology and Business, Astana, Republic of Kazakhstan)

The role of nuclear energy in the ESG transformation of Russia's export potential

With the strengthening of the ESG agenda and the global transition to sustainable development, approaches to building the export potential of national economies are being transformed. The relevance of the research is due to the growing importance of low-carbon technologies and the need to integrate environmental, social and management principles into Russia's export strategy. Nuclear energy is of particular importance in this context as a factor in ensuring the sustainability and competitiveness of export supplies. The purpose of the study is to scientifically substantiate the role of nuclear energy in the ESG transformation of Russia's export potential and to develop conceptual approaches to its integration into a sustainable model of foreign economic activity. The paper uses systemic and institutional approaches that have made it possible to analyze the impact of ESG factors on the structure of global energy markets, identify the environmental and social effects of the development of the nuclear industry, and identify institutional mechanisms for its regulation. As a result, a conceptual model of the ESG transformation of export potential has been developed, taking into account the environmental, social and managerial parameters of the development of nuclear energy. It is proved that the nuclear industry is a driver of the formation of sustainable export products (NPP construction, fuel cycles, service contracts) and contributes to strengthening Russia's position in the global low-carbon energy markets. The practical significance of the results lies in the possibility of their use in the development of state export policies, strategies for the development of the nuclear industry and mechanisms for promoting Russian energy technologies in international markets.
Keywords: export potential; nuclear energy; ESG transformation; sustainable development; low-carbon economy; foreign economic activity; energy exports; institutional development. 

 

E. V. Shirinkina, A. V. Prokopev (Surgut State University, Surgut); Ya. Yu. Stupak (Financial University under the Government of the Russian Federation, Moscow); Sh. U. Niyazbekova (Research and Education Center «Sustainable Development»; Moscow Witte University, Moscow)

Assessment of the impact of state support on tariff policy in the electric power industry

The article examines recent changes in tariff policy in the electric power sector driven by state support and structural shifts. It shows that the traditional cost-recovery model is being replaced by more flexible approaches that generate economically meaningful price signals. The paper reviews international practices, particularly in the EU, and performance-based regulation models focused on outcomes. It highlights that volumetric tariffs no longer reflect actual network usage. Therefore, the study emphasizes the need to apply cost causation principles and develop capacity- and time-based tariff components. It also assesses the prospects of implementing these approaches in Russia, considering its institutional specifics.
Keywords: tariff policy; electric power industry; state regulation; incentive regulation; price signals; network tariffs; cost causation; capacity tariffs; distributed generation; energy system.

 

I. V. Skvortsova, O. T. Ergunova, A. G. Somov, S. V. Chayuk (Peter the Great St. Petersburg Polytechnic University, St. Petersburg)

A strategic model for the implementation of AI-enabled digital twins in energy infrastructure: management challenges and organizational transformation

The implementation of digital twins (DTs) with artificial intelligence (AI) elements in energy infrastructure promises tangible operational benefits, but in practice, organizations repeatedly encounter barriers that go far beyond the technology. This paper examines the management challenges and organizational restructuring requirements that arise during the deployment of DTs through an exploratory case study of a transmission system operator in Central Europe. Semi-structured interviews with 36 stakeholders are subjected to reflexive thematic analysis, yielding 847 coded segments grouped into five themes. It turned out that non-technical challenges–a lack of strategic leadership (72 % of executives lack basic AI literacy), departmental silos, a lack of human capital (89 % lack a basic understanding of AI), and regulatory uncertainty–account for 69,7 % of the total weighted impact on adoption (1306 out of 1875 points), significantly outweighing technical challenges. Moreover, the LSTM anomaly detection model achieved an AUC of 0,91 and F1 of 0,87 in shadow mode, while operator adoption stalled at 48 % a clear indication of the primacy of sociotechnical factors. Drawing on empirical evidence and a synthesis of sociotechnical systems theory, dynamic capabilities, and continuous change, we develop a three-phase management model: assessing organizational readiness through a weighted Organizational Readiness Index (ORI), phased deployment with AI-specific management (model drift detection, retraining protocols), and continuous improvement with feedback loops. As a practical solution are propose the ORI, a RACI-based management architecture, and co-design strategies aimed at overcoming resistance related to professional identity.
Keywords: digital twin; artificial intelligence; energy infrastructure; organizational transformation; change management; AI management; socio-technical systems; implementation model.

 

N. N. Samokhina (Nizhnevartov State University, Nizhnevartovsk); I. A. Volkova (Omsk State University of Communications, Omsk)

Strategies for managing social systems in the context of the dialectics of stability and variability

The current stage of society's development is characterized by a fundamental contradiction: on the one hand, social systems objectively strive for stability and self-preservation, on the other hand, they are forced to continuously transform under the influence of a multitude of interconnected crisis phenomena. In these conditions, the problem of the relationship between stability and variability ceases to be purely theoretical and acquires a sharp practical significance: the ability of a management system to survive and develop depends on how well it understands the dialectics of these two states.
Keywords: dialectics of stability and variability; social systems; global uncertainty; management strategies; adaptability; institutional transformation.