Robotics competence centers as a tool for overcoming barriers to the adoption of industrial robots in mechanical engineering

Introduction. The main barriers to increasing the level of robotics in Russia are personnel shortages, financial uncertainty, and the monopolization of competencies. A scientifically sound architecture for robotics competence centers (RCC) is lacking.

Aim. To justify the creation of a network of RCCs, propose a functional model, an AI-based architecture, and a three-level hierarchy to improve the efficiency of robot implementation in mechanical engineering.

Materials and methods. Methods of system analysis, functional modeling, comparative analysis, and content analysis were used. Materials included statistics from the International Federation of Robotics (IFR), company survey results, and practices from Rosatom, Russian Railways, and the Federal Center for Competence in Labor Productivity (FCC).

Results. Three groups of barriers were identified. A model of the CCR consisting of six modules was proposed, including an AI core (knowledge base, predictive analytics, road map generation, and KPI monitoring). A three-level hierarchy has been developed: federal, regional, and sectoral CCR.

Conclusion. The proposed architecture differs from its analogues in the central role of AI in end-to-end automation of audit, monitoring, and decision-making. The expected effects include a 15-25 % reduction in implementation costs, a 6-9 month reduction in project timelines, and a 1.5-2.5 p. p. increase in productivity. The CCR network with an AI core can become an element of government policy. Key measures to achieve this include the inclusion of CCR in national projects, the standardization of requirements for AI cores, and a unified methodological framework.

1. Amelin S.V. Organization of production in mechanical engineering in the context of digital transformation. Production Organizer. 2020;28(1):17–23. (In Russ.).

2. Autor D.H. Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives. 2015;29(3):3–30. https://doi.org/10.1257/jep.29.3.3

3. Fedunina A.A., Gorodny N.A., Simachev Yu.V. The impact of robotization on the productivity of industrial enterprises in Russia. Russian Journal of Management. 2023;21(1):66–88. (In Russ.). https://doi.org/10.21638/spbu18.2023.104

4. Bannikov S.A. Global trends in robotics and prospects for its development in Russia. BENEFICIUM. 2023;(2):6–12. (In Russ.). https://doi.org/10.34680/BENEFICIUM.2023.2(47).6-12

5. Shevchenko O.P., Zolkin A.L., Khabibullin F.F., Zhiltsov S.A. Economic consequences of automation and robotization in industry. Economics and Management: Problems, Solutions. 2025;14(1):23–33. (In Russ.). https://doi.org/10.36871/ek.up.p.r.2025.01.14.003

6. Graetz G., Michaels G. Robots at work. Review of Economics and Statistics. 2018;100(5): 753–768. https://doi.org/10.1162/rest_a_00754

7. Acemoglu D., Restrepo P. Robots and jobs: Evidence from US labor markets. Journal of Political Economy. 2020;128(6):2188–2244. https://doi.org/10.1086/705716

8. Korobeynikova O.M., Dugina T.A., Mambetmuratov T.Zh. Factors limiting the investment activity of small and medium-sized businesses in Russia. Kazan Economic Bulletin. 2024;(1):53–58. (In Russ.).

9. Stepanov Yu.N., Yakovleva N.V., Ermakov S.G., Batalov D.I. Implementation of a project for the robotization of routine operations to increase the company’s operational efficiency (case study of Russian Railways). Intelligent Technologies in Transport. 2023;(3):14–21. (In Russ.). https://doi.org/10.24412/2413-2527-2023-335-14-21

10. Morozov O.A. Automation and robotization as the future of industrial production. Innovative Economy: Prospects for Development and Improvement. 2024;(4):84–89. (In Russ.).

11. Starovatova D.A. The relationship between the level of robotization and labor productivity: does business scale matter? Journal оf New Economy. 2023;24(1):81-103. (In Russ.). https://doi.org/10.29141/2658-5081-2023-24-1-4

12. Borisova O.V. et al. Measures of state financial support and risk factors affecting the cost of investment projects for the implementation of industrial robotics complexes. Finance: Theory аnd Practice. 2025;29(3):20-34. (In Russ.). https://doi.org/10.26794/2587-5671-2025-29-3-20-34

13. Kolchev V.D., Belkin I.O. Integration of lean production and digital technologies into the operational management of industrial enterprises. News of Higher Educational Institutions. Series «Economics, Finance and Production Management». 2023;(03):45–58. (In Russ.). https://doi.org/10.6060/ivecofin.2023573.653

14. Leventsov V.A., Leventsov A.N. Lean production and problems of its digitalization. Economics and Management. 2023;(2):20–27. (In Russ.).  https://doi.org/10.17513/snt.39493

15. Varlamov O.O. Application of mivar technologies of logical artificial intelligence for the creation of smart production systems. Information and Mathematical Technologies in Science and Management. 2025;(2):32–46. (In Russ.). https://doi.org/10.25729/ESI.2025.38.2.003.

16. Losev V.V., Kalinin A.O. On the development of flexible manufacturing systems. Informatics. Economics. Management. 2023;2(4):0247–0256. (In Russ.). https://doi.org/10.47813/2782-5280-2023-2-4-0247-0256

17. Makarova E.L., Firsova A.A., Dergachev A.A. Mechanisms of managerial decision-making in the field of digitalization of production processes of an industrial enterprise. Proceedings of Saratov University. New Series. Series: Economics. Management. Law. 2023;23(3):299–306. (In Russ.). https://doi.org/10.18500/1994-2540-2023-23-3-299-306

18. Severin A.I., Vinogradov Yu.M. Progress of robotics in the transition from Industry 4.0 to Industry 5.0. Proceedings of Tula State University. Technical Sciences. 2024;(1):356–361. (In Russ.). https://doi.org/10.24412/2071-6168-2024-1-356-357