KSHP. OMD №1-2026 el.dop
D. A. Lapinsky (Irkutsk National Research Technical University, Irkutsk)
Application of computer vision in industry
This article examines modern computer vision methods and technologies used in the industrial sector to automate processes, control quality, and improve production efficiency. Particular attention is paid to problems solved using machine learning algorithms and neural networks, including product defect detection, production line monitoring, robotic system control, and the analysis of large volumes of visual data. Examples of successful computer vision implementation in various industries are provided: automotive, energy, construction, agriculture, and logistics. Key benefits, such as cost reduction, minimization of human error, and increased measurement accuracy, are analyzed, and limitations associated with the need for high-performance computing and the adaptation of algorithms to specific production conditions are discussed.
Keywords: computer vision; manufacturing; algorithms; industry; machine learning; artificial intelligence.
S. V. Mikhailova, I. A. Pogrebnaya (Industrial University of Tyumen (branch), Nizhnevartovsk)
Dynamic model of hydraulic drive of high-speed press hammers taking into account equivalent elastic elements
In the context of the modernization of the Russian machine-building complex and the ongoing import substitution policy, the task of simplifying methods for engineering assessment of the energy-strength characteristics of high-speed hydraulic press hammers is becoming particularly relevant. The article discusses approaches to determining the maximum speed of movement of moving parts and impact energy realized during the operation of hydraulic drives of impact machines, based on an equivalent representation of the hydraulic system in the form of an elastic element and the use of simple measuring equipment. A mathematical model of the motion of moving masses is proposed that takes into account the rigidity of a hydraulic «spring», the rigidity of an additional mechanical spring that simulates the resistance of a deformable workpiece, as well as energy losses due to friction, throttling of the working fluid and elastic deformations of structural elements. Based on the solution of the equations of motion, dependences are obtained for calculating the maximum speed, the boundary stroke of the moving parts and the kinetic energy of the impact through the drive parameters and the set pressure in the hydraulic system. Special attention is paid to a simple experimental method for estimating the impact energy by the height of the bounce of a spherical indenter mounted on the end face of a hydraulic cylinder rod. This method makes it possible to correlate the measured potential lifting energy with the actual impact energy using the mass ratio of the moving parts of the installation and the indenter. Recommendations are given on the application of the considered methods for setting and controlling the operating mode of high-speed press hammers in production environments where the use of complex control and measuring systems is difficult. It is shown that the described techniques can serve as a basis for an operational assessment of the energy characteristics of equipment, the selection of rational process parameters and the subsequent modernization of existing drive systems.
Keywords: hydraulic drive; high-speed press hammer; impact energy; maximum striker speed; spring stiffness; kinetic energy; ball bounce; energy-strength characteristics.
I. P. Nikiforov, V. B. Fyodorov (Pskov State University, Pskov)
Increasing the tribological characteristics of the parts of sliding friction units by gas-thermal spraying of polymer coatings
Innovations in materials and manufacturing technologies play a crucial role in improving the reliability and durability of sliding friction pairs, especially under critical load conditions. This article discusses one of the most advanced and promising methods for modifying friction surfaces, involving the creation of combined structures with polymer coatings on the work areas of future tribopair. It is shown that the main disadvantage of using polymer coatings in friction pairs is the limitation of heating of the contact surfaces. With a significant increase in loads, the working layer is deformed and destroyed, which leads to a breakdown of the sliding friction unit. A thin-layer application of plastic onto a metal structured substrate can serve as a solution. An increase in the tribological characteristics of the tribopair in this case is achieved by a stable heat sink, as well as an increase in the stiffness of the friction bodies, which prevents unwanted deformation. At the same time, the applied polymer layer does not need to be machined, since this operation does not significantly affect the further operability and durability of the unit.
Keywords: sliding friction pairs; sliding friction units; tribological characteristics; combined structures; composite materials; laser microstructuring; gas thermal spraying.
A. V. Krasnov (Togliatti State University, Togliatti)
Evaluation of sound quality indicators in the passenger compartment of a car during the internal combustion engine is idling
The acoustic parameters of sound in the passenger compartment of a car have been studied during the internal combustion engine is idling during the initial period of its warming up. The car is alternately equipped with four samples of catalytic converters of the exhaust system, manufactured by various manufacturers. Sound levels and objective sound quality parameters (loudness, sharpness, roughness, fluctuation, and tonality) are evaluated. The measurement results show that the sound levels in the passenger compartment of a car equipped with various samples of catalytic converters differ by 1–7 dBA in the frequency ranges 125–630 Hz, 1600–12500 Hz. The complete set of the car, in which the lowest values of total sound levels, volume and tonality are recorded, receives the worst subjective assessment by experts. The refinement of the design of the catalytic converter and the mounting of its thermal insulation shield made it possible to reduce sound levels in the 125 Hz frequency band and the 1600–2500 Hz range to 7,5 dBA, reduce the sharpness and roughness of the sound, and improve the subjective expert assessment by 2,3 times.
Keywords: sound quality; sound level; car; passenger compartment; subjective assessment; catalytic converter.
Yu. A. Abuzin, I. E. Komissarov, D. A. Filippov (Nanokom LLC, Moscow); G. A. Yudin, S. A. Tyurina, G. Yu. Dalskaya (MIREA – Russian Technological University, Moscow)
Study of the interaction of Al-Cu-Fe quasicrystal powder with water
The effect of temperature and pH of an aqueous suspension of Al-Cu-Fe quasicrystals on the onset time of a spontaneous reaction with water, resulting in hydrogen evolution, was studied. The reaction occurs on the particle surface and is fading. The spontaneous reaction time, which takes 18 hours at room temperature, is reduced to 15 minutes when heated to 80 °C. A significant effect of aqueous pH on the onset time of the reaction was found: an alkaline environment accelerates the process, while an acidic environment slows it down.
Keywords: quasicrystals; Al-Cu-Fe quasicrystalline powder; hydrogen production.
S. V. Kurovsky, D. A. Mishin (Higher School of Education LLC, Odintsovo)
Synthesis and properties of nanostructured steels for trunk pipelines
Modern challenges in pipeline development, including the transition to fields with highly aggressive transported media (high carbon dioxide and chloride content), operations in Arctic conditions and deep-water offshore areas, and the need to increase operating pressure to increase pipe throughput, require the development of innovative materials, including nanomaterials. The synthesis and properties of nanostructured steels for trunk pipelines in the oil and gas sector are being studied. A method for producing high-strength, low-alloy steels with a nanoscale grain structure and dispersed nanophases (nitrides, carbides) is being developed. The influence of nanostructuring on a range of mechanical properties (strength, impact toughness, cold resistance) and resistance to brittle fracture is being studied.
Keywords: pipeline transport; nanostructured steels; dispersed nanophases; transported media; influence of nanostructuring; mechanical properties; brittle fracture.
S. V. Kurovsky, D. A. Mishin (Higher School of Education LLC, Odintsovo)
Nanoscale corrosion inhibitors for pipeline fluids
The relevance of this issue stems from the fact that traditional corrosion inhibitors have certain drawbacks that necessitate the development of new conceptual approaches. These include extremely low stability, the limited shelf life of multicomponent composites, the occurrence of antagonistic effects due to the lack of a scientifically sound corrosion inhibitor design concept, which fails to consider the protective mechanism of each component in the composite, environmental restrictions on the use of corrosion inhibitors, and the challenges of implementing control measures due to the lack of clear programs for treating chemical deposits in transported fluids. The article presents the results of a study exploring nanoscale corrosion inhibitors for pipeline fluids as a new conceptual approach. The principles of action and effectiveness of nanoscale corrosion inhibitors in oil, gas, and refined products are examined, and the mechanisms of nanoparticle adsorption on metal surfaces, the formation of protective layers, and their self-regeneration are investigated. A comparative analysis of nanoscale corrosion inhibitors in transported media with traditional inhibitors was conducted. The practical significance of the study lies in the fact that the results obtained can be used by Russian oil and gas companies to improve the production and technological foundations of pipeline network management and to enhance the implementation of fuel and energy resource transportation processes.
Keywords: pipeline transport; nanoscale corrosion inhibitors; traditional inhibitors; transported media; principles of action and effectiveness; nanoparticle adsorption mechanisms; metal surface.
E. V. Savelyeva (Primorsky State Agricultural Technology University, Ussuriysk)
Device with pneumatic transportation systems for loading and unloading grain
The relevance of the study is due to the fact that the existing loading and unloading equipment with traction and mechanical organs for processing grain on grain mills has a number of disadvantages: high cost; grain damage (up to 5 %); intensive wear and limited functionality. At the same time, a comparative analysis shows that pneumatic transportation equipment is more efficient than mechanical equipment for transporting various materials and surpasses competitors by 2–2,5 times in terms of overall performance. A device has been developed for loading and unloading operations at grain silos, which is a technological line that combines the suction and discharge systems of pneumatic conveying. The article presents the general design of the device in statics and dynamics. It analyzes the processes of loading and unloading grain material, indicating the parameters of the device to achieve high productivity, and presents the results.
Keywords: device; loading and unloading operations; valve; material pipeline; conveyor; pneumatic system; rollers; air pipeline.
L. A. Ezhova (Financial University under the Government of the Russian Federation, Moscow)
Intelligent warehouse planning systems based on reinforcement learning
The objective of this study is to develop a conceptual and functional model of an intelligent warehouse operations planning system based on reinforcement learning (RL) methods. A theoretical model for the application of RL algorithms (Q-learning, Deep Q-Network, Policy Gradient, PPO) to warehouse process optimization tasks, including picker routing, task distribution, queue management, and dynamic product placement, has been substantiated. A multi-level architecture of an intelligent planning system is proposed, including telemetry data collection modules, a simulation environment for agent training, a RL strategy optimization module, and a WMS integration unit. The architecture is designed to self-adapt the algorithms to changes in order volumes, warehouse configuration, or staff workload. A criterion for the integrated effectiveness of strategies was developed based on a weighted reward function that takes into account time, cost, and operational metrics. This formula enables quantitative comparison of strategies generated by the RL agent and the selection of the optimal warehouse operations planning policy. The presented model forms the basis for the development of adaptive, self-learning logistics management systems and demonstrates the potential of RL as a tool for improving the resilience and accuracy of warehouse processes.
Keywords: reinforcement learning; intelligent logistics systems; warehouse operations; digital twin; process optimization; Deep Q-Network; strategic planning in logistics.
L. A. Ezhova (Financial University under the Government of the Russian Federation, Moscow)
Automating public procurement monitoring using machine learning algorithms
The purpose of this study is to develop a conceptual and algorithmic model for automated public procurement monitoring based on machine learning and data mining methods. A methodological model for integrating classification, clustering, and predictive analytics methods into a single monitoring framework is substantiated. Particular attention is paid to automating the processes of extracting key characteristics of procurement documentation, normalizing data, identifying potential risks, and searching for inconsistencies between procurement parameters, market price dynamics, and historical patterns of customer and supplier behavior. A system architecture is proposed, including modules for intelligent parsing of data sources; Semantic analysis of documentation; machine detection of anomalous contracts; and the generation of risk indices based on trainable models. The theoretical basis of the research is based on methods of computational linguistics, statistical learning, probabilistic modeling, and time series analysis. The practical significance of the work lies in the potential application of the developed model to automate routine control operations, improve the accuracy of procurement compliance assessments, and identify hidden relationships between procurement parameters. The use of machine learning algorithms significantly reduces procurement analysis time, reduces the likelihood of human error, and develops robust monitoring tools capable of adapting to changing regulatory conditions and the market environment.
Keywords: public procurement; machine learning; intelligent data analysis; predictive analytics; procurement documentation parsing; digital audit.
O. V. Panina (Financial University under the Government of the Russian Federation, Moscow)
Spatial aspects of industrial policy and reduction of interregional imbalances through smart specialization instruments
The article is devoted to the analysis of spatial aspects of industrial policy in Russia and the search for mechanisms to reduce interregional imbalances based on smart specialization tools. The purpose of the study is to identify the relationship between the structure of the regional economy, the nature of industrial policy, and the dynamics of socio-economic convergence of regions. The results show a high concentration of industrial output and investment in a limited number of regions, a weak correlation between the volume of budgetary transfers and the real dynamics of industrial growth, as well as higher capital productivity, fiscal multipliers, the share of high-tech products, and resilience to shocks in regions implementing smart specialization strategies. Additionally, it is established that the growth of economic complexity in regional economies is accompanied by a decrease in the volatility of budget revenues and unemployment, while high interregional trade connectivity accelerates GRP convergence and reduces structural inequality. The conclusion is drawn about the need to shift from a unified redistributive model to a differentiated spatial industrial policy based on the identification and support of unique regional specializations, strengthening interregional cooperation, and integration into national value chains.
Keywords: spatial industrial policy; interregional imbalances; smart specialization; economic complexity; interregional integration.
M. E. Kosov (Financial University under the Government of the Russian Federation; Plekhanov Russian University of Economics; HSE University, Moscow); O. A. Polyakova, V. V. Eremin (Financial University under the Government of the Russian Federation, Moscow); T. K. Chernysheva (Financial University under the Government of the Russian Federation; Plekhanov Russian University of Economics, Moscow)
Modernization of state financial incentives for industrial development (using the example of the machine tool industry)
The article examines the instruments of state financial incentives for industrial development and the possibility of their modernization using the example of the machine tool industry. The relevance of the research is due to the strategic importance of the machine tool industry for technological sovereignty and import substitution. The purpose of the work is to study the current measures of state financial support for industry and develop proposals for their improvement. The study analyzes the regulatory framework and identified a wide range of incentive tools, from subsidies and preferential loans to special investment contracts. An analysis of the current state of the machine tool industry was carried out, which showes significant government investments (in 2024 – 130 billion rubles) and the simultaneous persistence of problems – dependence on imports, insufficient production volumes, and a decline in exports under sanctions pressure. Measures are proposed to increase the effectiveness of government support for the industry, namely: improving project selection criteria, expanding preferential financing for SMEs, stimulating long-term investments and developing innovative infrastructure. The implementation of these measures will ensure the sustainable development of the machine tool industry and increase the competitiveness of the Russian economy.
Keywords: government financial incentives; industrial policy; machine tool industry; government support; import substitution; technological sovereignty; innovative development.
A. I. Galkin (Financial University under the Government of the Russian Federation, Moscow)
Reindustrialization strategies and the development of high-tech industries under conditions of limited access to foreign technologies and markets
The article provides a quantitative assessment of reindustrialization strategies and the development of high-tech industries under conditions of limited access to foreign technologies, capital, and sales markets. The purpose of the study is to measure the «sovereignty cost2 through the impact of technological isolation and import substitution programs on capital efficiency, localization dynamics, financial stability of enterprises, and the formation of a dual-circuit economic model. It is shown that the highly profitable resource sector remains the main rent donor, while the reindustrialization of high-tech engineering and critical industries is cost-intensive: there is a negative correlation between CAPEX growth and ROIC, high price elasticity with respect to localization level in microelectronics and machine tool building, and the formation of an «inflationary fee for sovereignty». It is found that digital and pharmaceutical segments demonstrate a more favorable relationship between localization and price dynamics, whereas hardware industries lack economies of scale. A comparison of state-supported and market-financed enterprise groups reveals the formation of a quasi-state, high-leverage circuit, whose stability critically depends on subsidized capital costs and the stability of fiscal support. The conclusion emphasizes the need to shift from frontal import substitution to a strategy of smart technological specialization, targeting support at sectors with the highest multiplier effect, and restructuring investment models in view of the structural decline in total factor productivity and labor shortages.
Keywords: reindustrialization; high-tech industries; import substitution; technological sovereignty; financial stability.
S. G. Eremin (Financial University under the Government of the Russian Federation, Moscow)
The role of state development banks and support institutions in financing capital-intensive projects and selecting technologies in priority industrial sectors
The article analyzes the role of state development banks and specialized support institutions in financing capital-intensive projects and selecting technologies in priority industrial sectors under conditions of high market capital costs and technological turbulence. The aim of the study is to quantitatively assess how concessional financial instruments and the technological expertise of development institutions influence project feasibility, operational outcomes, localization level, and fiscal return. It is shown that the participation of development institutions radically changes the financial model: the effective cost of credit decreases by more than 2,5 times, debt duration almost triples, the share of capitalized interest in CAPEX decreases approximately threefold, and the DSCR in the first year of operation rises to safe levels. Deep technological expertise is associated with substantially lower deviations of production costs from planned parameters, a higher rate of achieving design capacity, reduced defect rates, and almost a twofold increase in equipment localization, accompanied by an increase in export revenue share. The calculation of investment and fiscal multipliers demonstrates a significant superiority of repayable and guarantee instruments over direct subsidies: concessional loans and guarantees provide a crowding-in effect for private investment and a multiple increase in fiscal return. The conclusion emphasizes the need to refocus industrial policy toward expanding the role of development institutions as centers of technological expertise and professional investors, prioritizing guarantee-based and concessional project support, and strictly linking financing to the requirements of technological sovereignty, localization, and labor productivity.
Keywords: state development banks; support institutions; capital-intensive projects; technology selection; concessional financing; investment and fiscal multipliers; guarantee support.
N. L. Krasyukova (Financial University under the Government of the Russian Federation, Moscow)
State venture instruments and development funds as a driver for scaling industrial startups and deep technology companies
The article aims to assess the role of state venture instruments and development funds in scaling industrial startups and companies in the field of deep technologies under conditions of high capital intensity, long R&D cycles, and market failures of traditional venture financing. It is shown that companies with state participation demonstrate a lower short-term ROI but significantly higher survival rates, lower revenue volatility, and an increased share of R&D expenditures compared to firms financed exclusively by private capital. It is established that early grant instruments generate a strong leverage effect: one budget ruble at the seed stage, on average, produces a multiple volume of private investment in subsequent rounds; at growth stages, co-financing models prevail, where higher state participation enhances the inflow of market capital. Comparative analysis shows that equity participation and mixed schemes ensure a more sustainable debt load profile, higher EBITDA profitability, faster renewal of fixed assets, and greater export activity, whereas the dominance of concessional loans leads to excessive leverage, and purely grant-based financing poses risks of «grant addiction» and delays in market scaling. The conclusion emphasizes the need to shift from passive allocation of public funds toward active portfolio management and an ecosystem approach that includes end-to-end alignment of support instruments across the life cycle stages of industrial startups, the development of public-private venture mechanisms, and the focus of performance evaluation on the long-term multiplicative effect for technological sovereignty and structural modernization of industry.
Keywords: state venture funds; industrial startups; deep technologies; leverage effect; technological sovereignty.
O. V. Panina (Financial University under the Government of the Russian Federation, Moscow)
Assessment of the effectiveness of measures to stimulate import substitution and technological sovereignty and their impact on the structure of national industry
The study is devoted to a comprehensive assessment of the effectiveness of government support measures aimed at promoting import substitution and achieving technological sovereignty, as well as their impact on the sectoral structure of the national industry. Based on data from Rosstat, the Bank of Russia, sectoral ministries, the financial statements of more than 500 enterprises for 2018–2024, and management surveys, a combination of structural-dynamic analysis, correlation-regression and econometric methods, index and cluster analysis is applied. This approach makes it possible to compare the dynamics of investment, revenue, localization levels, and budgetary efficiency of support measures in mechanical engineering, the chemical industry, radio electronics, and other strategic sectors. It is shown that the growth of capital investment, especially in radio electronics, significantly outpaces revenue dynamics, reflecting a stage of fixed capital accumulation and a delayed return effect, whereas in the chemical industry, the elasticity of output with respect to investment approaches unity. The introduced coefficient of technological independence demonstrates varying rates of localization deepening: steady growth in the oil and gas equipment segment is combined with a more problematic trajectory in the automotive and machine-tool industries, which are affected by external shocks and the pseudo-import substitution effect. The analysis of budget multipliers shows that medium-sized businesses generate the highest fiscal returns, while state corporations absorb most of the resources with minimal subsidy profitability and long payback periods. The comparison of capital-labor ratios and labor productivity indices reveals a persistent gap in favor of capital, indicating insufficient investment in human capital and managerial innovation. It is concluded that it is necessary to reorient industrial policy from an extensive to a selective and cluster-based approach, focusing on critical technological nodes, supporting R&D, and developing engineering competences, as well as shifting priorities from simple CAPEX expansion to improving the efficiency of already established production capacities.
Keywords: import substitution; technological sovereignty; industrial policy
N. L. Krasyukova (Financial University under the Government of the Russian Federation, Moscow)
The impact of sanction restrictions and trade barriers on reindustrialization and production localization in open economies
The article examines how sanction restrictions and the growth of trade barriers transform the trajectories of reindustrialization and production localization in open economies, shifting the priorities of industrial enterprises from maximizing efficiency to ensuring resilience and technological sovereignty. It is demonstrated that sanction shocks initiate a J-shaped investment dynamic and a nonlinear recovery of profitability: after an initial decline in EBITDA and CAPEX, there is an intensification of import-substituting investments and an increase in localization level, accompanied by rising logistics costs, longer inventory turnover periods, and higher shares of borrowed and subsidized capital. A change in the cost structure in favor of logistics and intermediary expenses has been revealed, along with a leading growth of the wage fund amid engineering personnel shortages and a decline in total factor productivity in several high-tech industries. The assessment of various government support instruments shows the higher effectiveness of preferential and mixed financing compared to direct subsidies, which create weak incentives for efficiency improvement. The introduced technological sovereignty index demonstrates that the growth of localization in mechanical engineering and IT is still accompanied by a decrease in labor productivity, whereas in industries with an initially high share of domestic technologies, a positive scale effect is achieved. It is concluded that the sustainable success of reindustrialization requires a transition from simple import substitution to an innovation-based model grounded in the development of technological clusters, institutional improvement of the investment climate, and the reorientation of industrial policy measures from quantitative localization indicators to technological and competitive performance parameters.
Keywords: sanctions; trade barriers; reindustrialization; production localization; technological sovereignty.
A. I. Galkin (Financial University under the Government of the Russian Federation, Moscow)
Industrial clusters as a driver of innovation and exports and a comparative analysis of coordination mechanisms between firms and regional authorities
The article explores the role of industrial clusters as drivers of innovation and exports, and provides a comparative assessment of the effectiveness of coordination mechanisms between firms and regional authorities. The aim of the study is to quantitatively evaluate the impact of cluster management quality, the structure of state support instruments, and industry specificity on the profitability, export activity, and innovation performance of enterprises. It is shown that mature initiative (bottom-up) clusters ensure significantly higher return on assets and export shares, as well as minimal transaction costs; the reduction of intra-cluster bureaucratic barriers statistically significantly increases ROA. A pronounced industry heterogeneity is revealed: IT and electronics demonstrate the highest «innovation leverage» and the shortest R&D payback period, whereas mechanical engineering and chemistry remain less sensitive to increases in R&D spending. The comparison of support instruments shows that tax incentives and infrastructure projects generate the greatest investment multiplier and tax base growth compared to direct subsidies, which tend to create dependent incentives. The existence of an optimal cluster scale and a time lag of approximately 3–3,5 years between changes in coordination models and sustainable export growth is identified, along with reduced revenue volatility among cluster residents compared to stand-alone firms. The study concludes that cluster policy should shift from quantitative indicators to qualitative metrics of coordination and innovation performance, and prioritize indirect support instruments associated with management quality and cooperation intensity.
Keywords: industrial clusters; coordination mechanisms; innovation activity; export potential; cluster policy.
T. N. Sakulyeva, А. А. Minaev (State University of Management, Moscow)
Theoretical framework and international context of creative industry development
The article analyzes the initial stage of the formation of the state governance and support system for the creative industries (CI) in the Russian Federation. Based on the analysis of Federal Law No. 330-FZ, methodological recommendations of the Russian Ministry of Economic Development, and statistical data, the key elements of the emerging institutional architecture are identified: normative-legal regulation, a registry-based model, a multi-level governance system, and a comprehensive support framework. Particular attention is paid to the two main objectives of the new policy: achieving a macroeconomic target (6 % of GDP by 2030) and realizing the value-forming function of CI in accordance with the Foundations of State Policy for the Preservation of Traditional Values.
Keywords: creative industries; value component; public policy; economic potential; creative sector.
N. V. Kuchkovskaya (Financial University under the Government of the Russian Federation, Moscow)
Digital twins and big data analytics as tools for ensuring economic security and accelerating R&D in industrial enterprises
In the current context of global economic uncertainty and market volatility, industrial enterprises are forced to switch to innovative approaches to asset management and product lifecycle in the framework of Industry 4.0, where traditional methods are giving way to predictive models based on real-time data) The key technologies here are digital twins (virtual replicas of physical processes and big data analytics, which provide high-precision modeling of system behavior, reduction of the R&D cycle, and optimization of management decisions, as evidenced by the exponential growth of the markets for these technologies with forecasts to reach hundreds of billions of dollars by the end of the decadeThe results demonstrate the explosive growth of the global digital twins market (CAGR 34,2 %, coefficient 6,24 times by 2030) and big data analytics (up to 621,94 billion dollars by 2032), radical acceleration of R&D reduction of development time by 49 %, physical prototypes by 81 %, defect costs by 67 % with increased detection errors by 152 %; Financial effects include an increase in EBITDA margin by 39,5 %, OEE by 20,5 %, ROI by 402 % over 3 years, reduced downtime by 26,6 %, and multibillion dollar investment effects in Russia (for example, SIBUR has 11 billion rubles with an effect-to-cost ratio of 1,93). The discussion highlights the qualitative transformation of the paradigm from reactive to proactive management, risk minimization, extraction of reserves from assets, and the prospects for an industrial metaverse with a CAGR of 51,5 %; Despite challenges such as staff shortages and the integration of legacy systems, digitalization is becoming a condition for survival, ensuring competitiveness through data-driven solutions, import substitution and environmental optimization, with recommendations for the development of digital culture and infrastructure for self-optimizing systems.
Keywords: digital twins; big data analytics; R&D cycle; industrial enterprises; Industry 4.0.
M. P. Bondarenko (Financial University under the Government of the Russian Federation, Moscow)
Integration of innovation marketing and digital transformation strategy in an industrial enterprise to accelerate the commercialization of high‑tech products
In the context of Industry 4.0, industrial enterprises must not only expand their digital capabilities but also ensure their direct conversion into accelerated commercialization of high‑technology products, which requires integrating innovation marketing with a digital transformation strategy. The purpose of the study is to provide a comprehensive economic analysis of the effectiveness of such integration in an industrial enterprise in terms of reducing time‑to‑market and improving financial performance. The methodological framework is based on system analysis and synthesis of secondary data from international consulting agencies for 2020–2025, the concepts of dynamic capabilities and open innovation, content analysis of top‑management surveys, as well as the assessment of key performance indicators (ROI, CAC, LTV, TTM) in traditional and digitalized business models. It is shown that the global growth of the digital transformation market is accompanied by regional and sectoral asymmetry and a structural shift in investment toward industrial IoT solutions, digital twins, AI, and process‑automation platforms, which collectively reshape the economics of the innovation cycle. At the micro level, it is demonstrated that integrating R&D and marketing on a unified digital platform using digital twins and AI‑driven analytics reduces TTM by up to 50 %, decreases unplanned downtime by 20–50 %, improves the LTV:CAC ratio to target levels, and lowers CAC by up to 50 % through optimization of the marketing mix and the transition to channels with high ROI (email marketing, SEO, proprietary content, and first‑party data). It is established that product launch quality acts as a mediator between market orientation and innovation performance, while organizational barriers and functional fragmentation are key reasons for the failure of digital transformation initiatives. The study concludes that it is necessary to create cross‑functional teams, implement unified data platforms, develop digital competencies among leadership (including the role of the CDO), and transition to Product‑as‑a‑Service models in which marketing and R&D operate as an integrated system for continuous enhancement of customer experience.
Keywords: digital transformation; innovation marketing; industrial enterprise; R&D-marketing integration; time‑to‑market.
M. P. Bondarenko (Financial University under the Government of the Russian Federation, Moscow)
The role of marketing data analytics and artificial intelligence in forecasting the market potential of technological innovations in capital‑intensive industrial product markets
The article analyzes how marketing data analytics and artificial intelligence tools transform the approach to assessing the market potential of technological innovations in capital‑intensive industrial product markets in the context of Industry 4.0. The aim of the study is to develop an integrated financial‑analytical approach that links demand forecasting accuracy indicators (MAPE, standard error deviation) with key performance parameters of investment projects (NPV, IRR, WACC, working capital requirements) and the value of strategic flexibility. The methodological framework includes an interdisciplinary synthesis of quantitative marketing analysis, corporate finance theory, DCF cash‑flow simulation modeling, inventory management models, a modified CAPM, and real options valuation based on a binomial model. Modeling results based on the example of launching a new type of industrial equipment show that the use of AI‑based approaches reduces MAPE by approximately 2–3 times, decreases safety stock and capital tied up in inventory by several million dollars, and increases free cash flow through optimized working capital and reduced shortage losses. At the same time, it is demonstrated that lowering the risk premium in the discount rate by 4–5 percentage points due to reduced information asymmetry increases the project’s NPV and IRR to levels at which previously marginal initiatives become highly profitable, while the value of real options for production expansion grows by more than one‑third. The discussion concludes that marketing data analytics and AI should be viewed by industrial firms as a high‑yield financial asset that is critically important for risk management, capital structure optimization, and long‑term competitiveness in global B2B markets.
Keywords: marketing data analytics; artificial intelligence; capital‑intensive industrial product markets; market potential forecasting; investment decisions
T. V. Vashchenko (Financial University under the Government of the Russian Federation, Moscow)
Strategies for the commercialization of research and development results in the industrial sector with a focus on patent policy, licensing agreements, and joint ventures
The article examines the transformation of value‑creation mechanisms in the industrial sector amid a shift from tangible assets to intellectual property and the rapid growth of global patent activity. The purpose of the study is to identify the most effective strategies for commercializing R&D results, with an emphasis on patent policy, licensing agreements, and joint ventures as key institutional forms of technology monetization. The methodological framework includes an analysis of global R&D and patent‑filing statistics, a detailed examination of segment‑level financial reporting of leading technology corporations (Qualcomm, Ericsson, Nokia), a structural analysis of industry‑specific royalty‑rate ranges, and a model of the total cost of ownership for a portfolio of 1,000 patents in the United States, Europe, and China, taking into account fees, attorney services, and annual maintenance payments. The findings demonstrate a structural divergence between low‑margin material production and high‑profit licensing divisions that generate operating margins of 60–80 % while accounting for a relatively small share of revenue, as well as the high capital intensity of maintaining global patent portfolios, whose total lifecycle costs reach tens of millions of dollars. The analysis shows that royalty‑rate ranges vary significantly across industries, reflecting technology specifics and competitive dynamics, and that patent disputes and compulsory licensing are becoming essential instruments for influencing market structures (as illustrated by the battery, metallurgy, and automotive industries). The discussion substantiates the need for a transition to multi‑level hybrid commercialization strategies that combine high‑yield licensing with selective joint‑venture formation and active portfolio pruning, the use of patent analytics and AI to evaluate the commercial value of applications, and the global coordination of litigation and patent policy as a key component of corporate financial planning in the industrial economy.
Keywords: R&D commercialization; patent policy; licensing agreements; joint ventures; industrial sector.
S. I. Kravchenko (Financial University under the Government of the Russian Federation, Moscow)
A cluster-oriented policy for the digital transformation of industrial enterprises
This article presents the results of a comprehensive study on digital transformation priorities in industrial enterprises, based on cluster analysis. The empirical data consist of statistical information from 78 entities within the Russian Federation, grouped into five thematic categories: the level of digital technology adoption, information and communication infrastructure development, technological advancement and innovation potential, and sociodemographic and structural-economic contexts. Based on the analysis, five typical clusters were identified, which differ significantly in their level of digital development, innovation activity, and structural features: «digital economy leaders», «industrial innovators», «resource-oriented regions», «lagging peripheral regions», and «moderately developed traditional regions». Regional policy recommendations were developed for each cluster to strengthen their competitive advantages and overcome bottlenecks. A set of inter-cluster initiatives designed to reduce the digital divide and promote cooperation and knowledge transfer between regions was proposed as a systemic measure. Implementing this approach should contribute to the balanced, sustainable development of the industry in the context of digital transformation.
Keywords: digital transformation; industrial enterprises; cluster analysis; regional development; innovation activity; digital maturity; differentiated regional policy.
S. A. Polevoy (Financial University under the Government of the Russian Federation, Moscow)
Integration of project management and production management methodologies to improve the operational efficiency of an industrial enterprise in the context of digital transformation and labor market instability
The research is devoted to the development of an integrated methodological approach synthesizing project management and production management tools to improve the operational efficiency of industrial enterprises in the context of unprecedented digital transformation and a structural shortage of qualified personnel. In a situation where the projected gap between labor supply and demand may reach 1,9-3,8 million unfilled vacancies by 2030, the classic dichotomy between operational management and project management is becoming a factor hindering the development of production systems. The methodological base of the study includes a comprehensive econometric analysis using stochastic boundary analysis and analysis of the operating environment based on panel data from 610 industrial enterprises for the period 2020-2024, with the development of an author's composite index of integration of methodologies and an assessment of the parameters of the transcendental logarithmic production function. The results demonstrate that the integration index is a statistically significant predictor of efficiency with a coefficient of 0,4128, comparable in terms of influence with traditional factors of production, while enterprises in the upper quartile of integration show a level of cumulative factor productivity 18,4 % higher than average. Economic modeling confirms that the integrated approach provides a phenomenal increase in economic value added by more than 10 times, a reduction in the payback period for industrial Internet of Things projects from 4,1 to 2,4 years, and significantly greater resistance to fluctuations in labor costs.
Keywords: project management; production management; digital transformation; operational efficiency; cumulative factor productivity.
M. N. Zabaeva, O. B. Skripnik (Financial University under the Government of the Russian Federation, Moscow)
Formation and development of ambidexterity in industrial innovation management systems for simultaneous exploration and exploitation of new technological opportunities
The research is devoted to the development of conceptual and methodological foundations for the formation of organizational ambidextrity in industrial innovation management systems as a critical ability of industrial enterprises to simultaneously exploit current competencies to maximize profits from existing assets and explore new technological horizons to ensure long-term viability in the face of market turbulence. In a situation of a global slowdown in the growth of research costs to 2,9 % and structural imbalances in the distribution of innovative capital, the ability to ambidextrate is transformed from a factor of competitive advantage into a condition for the survival of business entities. The results confirm a statistically significant correlation between the composite ambidextrous index and innovative productivity, while financial modeling revealed that research projects generate a net present value 2,08 times higher than operational projects, despite a lower internal rate of return, and the introduction of an innovation management system increases the conversion of ideas into projects by 128,69 % and reduces product withdrawal time. to the market by 25 %.
Keywords: organizational ambidexterity; industrial innovation; research; operation; innovation management.
K. A. Barmuta, I. Yu. Popkhadze (Don State Technical University, Rostov-on-Don)
Innovations in local government as a factor in sustainable regional development: assessing the effectiveness of digital technology implementation in municipal management
This paper examines the effectiveness of digital technology implementation at the municipal level. A correlation and regression analysis of the impact of digitalization on the achievement of regional sustainable development goals is conducted. The degree of digitalization's influence on various components of sustainable development is determined.
Keywords: digital technology; regression; correlation; sustainable development; municipality; region.
N. L. Udaltsova (Financial University under the Government of the Russian Federation, Moscow)
Models for managing the digital transformation of industrial enterprises in the context of increasing the competitiveness of the regional economy
The article is devoted to identifying how management models of digital transformation of industrial enterprises affect the competitiveness of the regional economy in the context of Industry 4.0, import substitution and increasing geo-economic turbulence. The purpose of the study is to comprehensively assess the relationship between the level of digital maturity of enterprises and the macroeconomic indicators of regions, as well as to compare the effectiveness of various classes of digital technologies and management models of their implementation. The results show the completion of the primary stage of digitalization (ERP, EDMS, VCS) and the transition to a deep industrial transformation based on digital twins, industrial robotics and predictive analytics; AI-based solutions demonstrate the greatest ROI, while digital twins provide maximum reduction in downtime, and robotics compensates for the shortage of personnel. A stable positive relationship has been revealed between the level of digital maturity of the region and the growth rate of GRP, the share of high-tech products and the volume of investments. The leading regions of digitalization are significantly ahead of the lagging territories, which confirms the role of the digital ecosystem as a factor of investment attractiveness. The discussion of the results allows us to conclude that the crucial condition for success is the quality of management models: the transition from patchwork automation to ecosystem architectures, including scenario planning, consideration of cybersecurity risks, the development of digital competencies and scarce personnel, as well as coordinated interaction between government, business and the educational sector. It is concluded that it is strategically verified models of managing the digital transformation of industrial enterprises that form the basis for sustainable growth and increasing the competitiveness of the regional economy.
Keywords: digital transformation; industrial enterprises; digital maturity; regional economy; competitiveness.
O. B. Skripnik (Financial University under the Government of the Russian Federation, Moscow)
Managing the portfolio of innovative projects of an industrial company based on real options and risk-based prioritization, taking into account resource and time constraints
In the context of increasing macroeconomic turbulence, technological corruption, and tightening resource constraints, traditional approaches to evaluating and selecting innovative projects based on DCF and static NPV demonstrate a systematic underestimation of high-risk but strategically significant initiatives. The aim of the research is to develop a comprehensive methodology for managing the portfolio of innovative projects of an industrial company based on the integration of real options and risk-based prioritization, taking into account budget, time and key competencies constraints. The results show that using the real options approach radically changes the assessment of the attractiveness of highly volatile R&D projects: initiatives with negative static NPV move into a zone of significant positive ENPV due to the value of opt-out and phased investment options, and the total expected portfolio value increases by 63 % when optimized using ENPV compared to traditional NPV optimization. subject to all resource constraints. Taking into account the synergy between projects further increases the ENPV of the portfolio and demonstrates the superadditivity of value. The discussion reveals key management implications: rethinking the role of uncertainty as a source of optional value, the need to move from evaluating individual applications to managing "investment clusters", as well as barriers to ROA implementation related to a lack of competencies and cultural stigmatization of early abandonment of projects. It is concluded that the proposed methodology makes it possible to form a more stable and strategically oriented architecture of an industrial company's innovation portfolio in conditions of high uncertainty.
Keywords: real options; project portfolio; resource constraints; risk-based prioritization; industrial company.
S. A. Tronin (Financial University under the Government of the Russian Federation, Moscow)
Predictive analytics as a tool for ensuring economic security and sustainable development of industrial enterprise
This study examines the integrated application of predictive analysis methods in modeling the sustainable development of industrial enterprises. The relevance of this topic stems from the need to develop reliable tools for forecasting the dynamics of key sustainability indicators in an unstable external environment. The objective of this study is to substantiate a methodology for the systematic use of predictive analytics to improve the adaptability and resilience of the industrial sector. Optimal resource conservation scenarios are determined, enabling a 10–15 % increase in production efficiency. It is found that considering ESG factors increases the accuracy of forecast models by an average of 12 %. An algorithm for dynamically adjusting development strategies based on Big Data analysis is proposed. The obtained results provide a reliable basis for developing preventive measures to ensure sustainable industrial growth in a turbulent socioeconomic environment. Further research should be directed toward validating the developed models on larger samples, taking into account industry specifics.
Keywords: sustainable development; industrial enterprises; predictive analytics; machine learning; ESG factors; strategic planning.
M. M. Makhmudova (Industrial University of Tyumen, Tyumen)
Assessment of the availability of small business in the regions of the Ural Federal District and its importance for the region's economy
Based on the analysis of statistical information, the article identifies regional and sectoral features of the development of the small business sector in the economy of the regions of the Ural Federal District. The assessment of the importance of small business for the economy of each region of the district is given. The dynamics of indicators of entrepreneurship provision in the Ural regions is presented and characterized. A matrix for the development of the regional economy has been built, taking into account the contribution of small business
Keywords: small business; entrepreneurship; Ural Federal District; coefficient of security; coefficient of significance.
S. G. Eremin (Financial University under the Government of the Russian Federation, Moscow)
The role of state development banks and financing institutions in the modernization of production capital and the increase of labor productivity
The article examines the role of state development banks and specialized financing institutions in the modernization of production capital and the improvement of labor productivity of Russian industrial enterprises under conditions of high cost of market capital and aging fixed assets. The research aims to provide a quantitative assessment of the impact of concessional financing on the quality and structure of fixed assets, labor productivity, and operational efficiency of enterprises compared to companies relying exclusively on market-based sources. It is shown that concessional financing provides a radically different cost of capital (an effective rate of 6,85 compared to 21,45 % under market lending), extends the maturity of borrowed funds, and reduces collateral requirements, thereby making capital-intensive modernization projects economically feasible. At the same time, increased debt burden and a shortage of highly skilled personnel are identified, indicating the need for the development of equity and mezzanine financing instruments, comprehensive project support, and the linkage of concessional conditions to the achievement of target labor productivity and technological renewal indicators.
Keywords: state development banks; financing institutions; modernization of fixed capital; labor productivity; concessional financing.
M. V. Polevaya (Financial University under the Government of the Russian Federation, Moscow)
Predictive analytics of personnel risks in industry based on big data to reduce the turnover of scarce specialists and prevent disruptions to the production cycle
The research is devoted to the development and implementation of a predictive analytics system for personnel risks in the Russian industrial sector based on big data and machine learning technologies to reduce the turnover of scarce specialists and prevent disruptions to the production cycle in conditions of a critical shortage of qualified personnel. In a situation where the total shortage of labor resources has reached 2.2 million people, and the turnover of qualified personnel has transformed into a key financial risk factor, traditional human capital management models are showing inconsistency. The results demonstrate that the gradient boosting model identifies 82,6 % of employees at risk with an area under the ROC curve of 0,9540, while the critical predictors of turnover are not monetary factors, but systematic overwork, career stagnation and management instability. The economic calculation confirms the investment attractiveness of implementing a system with a return on investment of 20 to 160 %, depending on the category of personnel, providing a net economic effect of about 5 million rubles annually, even with conservative assumptions.
Keywords: predictive analytics; personnel risks; staff turnover; machine learning; industry.
T. I. Kuchina, N. G. Urasova (Financial University under the Government of the Russian Federation, Moscow); D. Chaikovskii (Shenzhen MSU-BIT University, Shenzhen, People's Republic of China)
Integrated personnel management model for an industrial enterprise under digital transformation of production and the implementation of Industry 4.0 and artificial intelligence technologies
The article is devoted to the development of an integrated personnel management model for an industrial enterprise in the context of digital transformation of production, the introduction of Industry 4.0 and artificial intelligence technologies, as well as the intensification of sanctions pressure and the need to replace imported digital HR platforms. The purpose of the study is to provide a theoretical and applied rationale for the architecture of an HR model that ensures productivity growth and a reduction in transaction costs while simultaneously mitigating the risks of skilled labor shortages and technological dependence. It is shown that the implementation of AI tools in recruitment reduces hiring costs and early turnover, significantly lowers labor inputs for screening, and increases recruitment funnel conversion, generating substantial direct economic effects. Predictive retention analytics models demonstrate that the greatest financial risks are associated with the departure of highly qualified engineers, IT specialists, and managers, and reducing their turnover yields hundreds of millions of rubles in savings by preventing losses of human capital. Scenario analysis of TCO for various HR platform development options confirms the economic feasibility of migrating to domestic solutions, provided deep customization and readiness to pass through an initial «efficiency dip». It is concluded that an effective personnel management model in the era of Industry 4.0 should be built around the concept of a «digital twin of the organization», a unified data lake, and a transition from reactive HR service to proactive human capital management, where the key success factor is not only technological but also cultural transformation of the enterprise.
Keywords: digital transformation, personnel management, Industry 4.0, artificial intelligence, industrial enterprise
V. B. Frolova (Financial University under the Government of the Russian Federation, Moscow)
The influence of human capital, engineering competencies and corporate culture on the innovation activity of industrial enterprises in the regional context
The article is devoted to the analysis of how the human capital of engineering competencies and the peculiarities of corporate culture determine the level of innovation activity of industrial enterprises in the context of the structural transformation of the Russian economy, record profitability of the corporate sector and the continuing technological lag. The purpose of the study is to identify and quantify the relationships between the characteristics of engineering personnel, types of organizational culture and the results of innovation activities in the regional context, as well as to build a typology of regional innovation systems. The results show a pronounced focal concentration of innovations in several agglomerations (Moscow, St. Petersburg, Tatarstan, Tomsk), while traditional industrial regions lag behind, as well as a discrepancy between the record profitability of industry and a low proportion of reinvestment in R&D. It is established that the key limitation is not the amount of financial resources, but the shortage of specific engineering human capital and the predominance of hierarchical, bureaucratic cultures that suppress initiative and create the phenomenon of «innovation pause». It is shown that the combination of a high level of engineering competencies with an adaptive, learning culture multiplicatively increases innovation activity, while the presence of strong personnel with a rigid culture gives a low innovation result. A typology of regions has been formed (innovative ecosystems, industrial bastions, catching-up regions, territories with low potential) and the need to include human capital quality metrics and corporate culture parameters in monitoring systems and industrial policy tools to ensure technological sovereignty and balanced regional development has been substantiated.
Keywords: human capital; engineering competencies; corporate culture; innovation activity; industrial enterprises.
I. B. Eliseev, V. S. Yakovleva, M. D. Nazarova, I. I. Kalanin (Saint-Petersburg University of the State Fire Service of the EMERCOM of Russia, St. Petersburg)
Current state of occupational safety and health at industrial facilities
This article analyzes the current state of occupational safety and health at industrial facilities, identifying key trends related to digitalization, automation, and changing qualification requirements for workers. The challenges and opportunities arising from technological transformation are considered, and avenues for improving the efficiency, safety, and attractiveness of industrial labor in the new economic reality are proposed.
Keywords: occupational safety and health; industrial facilities; working conditions; safety; motivation.
M. E. Kozlov, A. S. Popov, D. M. Makhmudov, A. E. Bazhenov, I. S. Makarov (Povolzhskiy State University of Telecommunications and Informatics, Samara)
A comparative analysis of blocking and non-blocking http request processing models in modern web frameworks
This article presents a comparative analysis of blocking and non-blocking HTTP request processing models in modern web frameworks using the example of implementation using Spring MVC with Tomcat and Spring WebFlux with Netty. The paper examines the architectural features and principles of operation of both processing models, their impact on performance and scalability in terms of processing CPU-bound and I/O-bound operations. The purpose of this study is to identify the advantages and limitations of each query processing model at different load levels. To achieve this goal, application prototypes were developed that include two types of endpoints: computationally intensive (CPU-bound) operations and I/O-bound operations implemented through interaction with a relational database. Comprehensive load testing was performed using the wrk tool, during which key performance metrics were measured: RPS (requests per second), average latency, p95/p99 percentile metrics, as well as CPU usage and memory usage. The results obtained demonstrate that the non-blocking processing model provides significantly higher performance and better scalability under high load conditions, especially for I/O-bound operations. At the same time, the blocking model retains advantages in terms of ease of implementation and is preferred for applications with low load and a predominance of CPU-bound operations.
Keywords: web framework; blocking processing; non-blocking processing; performance; parallelism; request processing; scalability.
M. P. Lazarev (Financial University under the Government of the Russian Federation, Moscow)
Environmental and circular innovations in industrial production as a basis for achieving the goals of sustainable development and decarbonization of supply chains
The study analyzes the role of environmental and circular innovations in industrial production as a key mechanism for achieving the goals of sustainable development and decarbonization of global supply chains. In conditions of critical anthropogenic pressure on planetary boundaries, the industrial sector faces the need for a radical transformation of production models, moving from linear consumption of resources to closed material cycles. The results of the study reveal a structural differentiation in the growth rates of various segments of the circular economy, where heavy industry is showing outstripping dynamics with a projected average annual growth rate of 13,2 %, significantly exceeding the general market indicators. Quantitative analysis proves that the potential losses from climate risks in supply chains exceed the costs of minimizing them by 2,9 times, and the empirically confirmed positive correlation between the circularity index and shareholder value growth indicates the formation of a market premium for sustainability. The study proves that circular innovations are being transformed from image initiatives into a fundamental factor of long-term competitiveness and financial stability of industrial enterprises, forming a new industrial paradigm based on deep technological integration and rethinking of value chains.
Keywords: circular economy; decarbonization; sustainable development; industrial innovation; supply chain.
A. L. Kudryashov (Financial University under the Government of the Russian Federation, Moscow)
Circular economy and industrial symbiosis as approaches to increasing resource efficiency and reducing the environmental footprint of regional industrial enterprises
The article examines the potential of the circular economy and industrial symbiosis as mechanisms for improving resource efficiency and reducing the environmental footprint of industrial enterprises in Russian regions. The aim of the study is to provide a comprehensive economic assessment of the possibilities for integrating technogenic and agro‑industrial waste into economic circulation and to identify the factors determining the profitability of symbiotic projects. The study demonstrates that accumulated technogenic resources in industrial centers (using the Novokuznetsk industrial hub as an example) possess a latent economic potential comparable to the annual budgets of Russian regions; however, their utilization is constrained by high capital and energy‑intensive processing costs. It is identified that substituting primary raw materials with technogenic materials in the construction industry can reduce production costs by 5–6 % and generate additional benefits through avoided environmental payments, while in agricultural regions a key avenue is bioenergy symbiosis based on converting livestock waste into biogas and organic fertilizers, reducing project payback periods to 4–5 years. The study finds that interregional differences in electricity tariffs create a «map» of investment attractiveness for energy‑intensive recycling clusters and predetermine territorial specialization, whereas stricter extended producer responsibility mechanisms and higher environmental fees fundamentally alter financial incentives, transforming recycling from a voluntary environmental initiative into a prerequisite for maintaining profitability. The article discusses the role of digital platforms and the reduction of transaction costs as critical conditions for scaling industrial symbiosis and outlines directions for regional policy aimed at supporting circular projects.
Keywords: circular economy; industrial symbiosis; resource efficiency; regional industrial policy; environmental footprint
M. E. Kosov (Financial University under the Government of the Russian Federation; Plekhanov Russian University of Economics; HSE University, Moscow); V. G. Starovoitov, O. A. Polyakova, E. V. Zolotarev, S. B. Reshetnikov (Financial University under the Government of the Russian Federation, Moscow); T. K. Chernysheva (Financial University under the Government of the Russian Federation; Plekhanov Russian University of Economics, Moscow)
Strategic risks in the sphere of civil aviation of the Russian federation under sanctions
The purpose of the study is to identify and predict the strategic risks of civil aviation in the Russian Federation under the conditions of sanctions restrictions based on artificial neural networks. The paper proposes criteria for classifying industry threats as strategic risks, develops a methodology for their identification, and builds a neural network model for predicting the likelihood of aviation incidents. The study analyzes the causes and factors of strategic risks in the field of civil aviation in the context of sanctions restrictions. Key strategic risks have been identified, including those related to the implementation of a Comprehensive Aviation Industry Development Program until 2030. A list of indicators characterizing strategic risks has been determined and a risk ranking has been performed using the expert survey procedure. An innovative method of strategic risk management using the ANN is substantiated and recommended for use. The calculation of forecasting industry-specific aviation incidents has been performed. The article shows that the use of a modern strategic risk management method based on the ANN makes it possible to improve the quality and effectiveness of management decisions in the field of civil aviation. The results of the study form the basis for adapting the Comprehensive Aviation Industry Development Program until 2030 and developing mechanisms for the sustainable functioning of the Russian aviation system in the face of sanctions.
Keywords: civil aviation; strategic risks; sanctions; identification of strategic risks; forecasting of strategic risks; artificial neural networks; neutralization of strategic risks.
N. V. Kuchkovskaya (Financial University under the Government of the Russian Federation, Moscow)
Tax incentives for technology parks and special economic zones as a factor in economic security and investment sustainability of industrial enterprises: an econometric assessment
The article is devoted to an econometric assessment of the impact of tax incentives provided within the infrastructure of technoparks and special economic zones on fixed capital investment of Russian industrial enterprises under conditions of macroeconomic turbulence and sanctions-related constraints. The aim of the study is to quantitatively measure the «net» contribution of preferential tax regimes to the dynamics of capital expenditures and to test the hypothesis of a nonlinear, saturating nature of their influence while simultaneously accounting for infrastructure quality and regional conditions. The results demonstrate a stable excess of investment growth rates among residents of preferential regimes over average market indicators by 15–25 percentage points, a private investment multiplier of 4–5 rubles per 1 ruble of budgetary tax expenditure, and the dominant role of the corporate profit tax incentive as the key driver of investment activity compared with property tax and social contribution incentives. A saturation point is identified for the effect of reducing the effective corporate profit tax rate (in the range of 2–5 %), below which further easing of the fiscal regime does not lead to a statistically significant increase in capital formation, and infrastructural and institutional factors become decisive. The study shows the complementarity of tax incentives and the quality of SEZ and technopark infrastructure, as well as substantial inter-zone variation in effectiveness, which supports a transition to differentiated adjustment of the depth of preferences depending on industrial specialization, project life-cycle stage, and actual investment performance.
Keywords: tax incentives, special economic zones, technoparks, fixed capital investment, econometric analysis
S. A. Tronin (Financial University under the Government of the Russian Federation, Moscow)
Corporate venture funds and accelerators as tools for economic security and technological sustainability of regional industrial leaders
In conditions of fragmentation of global markets and limited access of Russian industrial companies to foreign technologies, corporate venture funds and accelerators are becoming key tools for integrating startups into the production circuits of regional industrial leaders. The purpose of the study is a comparative analysis of the organizational models of corporate venture funds and accelerators and an assessment of their effectiveness in terms of transaction costs, the speed of integration of solutions and the economic effect created. The results show that an open corporate accelerator provides the highest capital efficiency (two-year operational ROI of over 200 %) with low funnel conversion and significant «bureaucratic tax», a venture studio demonstrates the highest conversion of a pilot into a commercial contract and compatibility with ERP systems of over 90 % with high pilot costs, internal incubators are characterized by high pilot conversion–the contract is based on the involvement of internal customers, and the «CVC as a service» model occupies an intermediate position in terms of costs and integration speed. It is shown that predictive analytics and Big Data solutions provide the best ratio of payback rate and OPEX reduction, digital twins maximize NPV by optimizing CAPEX with limited scalability, while computer vision systems have the greatest replication potential, and industrial robotics requires a longer investment horizon. The regional analysis shows a decrease in the ROI of pilot projects in remote industrial clusters in the Urals and Siberia by about 30 % due to increased logistical and organizational costs, a shortage of local competencies, and low penetration of digital services. It is concluded that hybrid models that combine a wide funnel of open accelerators, in-depth integration of venture studios, Fast Track mechanisms and the development of regional competence centers provide the greatest stability and strategic value, which allows industrial holdings to accelerate technology transfer, increase operational efficiency and strengthen technological sovereignty.
Keywords: corporate venture capital; corporate accelerators; startup integration; regional industrial leaders; technological sovereignty.
E. A. Yakovleva (Saint Petersburg State University of Economics; Saint Petersburg Branch of the Financial University under the Government of the Russian Federation, Saint Petersburg); Yu. E. Putikhin, E. S. Volkova (Saint Petersburg Branch of the Financial University under the Government of the Russian Federation, Saint Petersburg); A. I. Gubaydullina (Volga Region State University of Physical Education, Sports and Tourism, Kazan)
Modern strategic planning
This article examines the essence of strategic planning, where planning is a means not only of establishing goals and objectives for enterprise development, but also the basis for developing investment, financial, budgetary (tax) policies, specifying expected results, and justifying the calculation of planned technical and economic indicators for enterprise development. The authors propose the need to apply a dynamic format for presenting data, information, and knowledge in the enterprise management system, as well as the use of standard units for presenting data, information, and knowledge to coordinate planning work and connect the links of the organizational-technological (natural-product) chain in the process of continuous plan formation and implementation, as well as the reverse logical inference of (planning, coordination, organizational) planning decisions. The work offers a new perspective on strategic planning, using modern semiotic methods and logical-linguistic modeling. This can form the basis for further scientific research and publications.
Keywords: planning; situational management; complex socio-economic systems; logical-linguistic approach.