RUDN Journal of EconomicsRUDN Journal of EconomicsВестник Российского университета дружбы народов. Серия: Экономика2313-23292408-8986Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)3987410.22363/2313-2329-2024-32-2-235-250HUVIPJINNOVATIONS IN THE MODERN ECONOMYИННОВАЦИИ В СОВРЕМЕННОЙ ЭКОНОМИКЕResearch ArticleThe Digital Transformation: Unlocking New Dimensions in Manufacturing EfficiencyЦифровая трансформация: открытие нового измерения в эффективности производстваBaimingJinБайминЦзиньpostgraduate student, Department of National Economyаспирант кафедры национальной экономики, экономический факультет1042238023@pfur.ruVoskerichyanRobert O.ВоскеричянРоберт ОганесовичAssociate Professor, Faculty of Economics, Department of National Economyдоцент кафедры национальной экономики, экономический факультетvoskerichyan-ro@rudn.ruRUDN UniversityРоссийский университет дружбы народов30062024322INNOVATION AND INVESTMENT: OPPORTUNITIES AND PROSPECTSИННОВАЦИИ И ИНВЕСТИЦИИ: ВОЗМОЖНОСТИ И ПЕРСПЕКТИВЫ23525007072024Copyright ©; 2024, Baiming J., Voskerichyan R.O.Copyright ©; 2024, Баймин Ц., Воскеричян Р.О.2024Baiming J., Voskerichyan R.O.Баймин Ц., Воскеричян Р.О.https://creativecommons.org/licenses/by-nc/4.0https://journals.rudn.ru/economics/article/view/39874

The manufacturing sector stands on the cusp of the digital revolution that holds the promise of fundamentally reshaping its operational landscape. This paper delves into the transformative journey of digital integration within the manufacturing realm. Employing a scoping review methodology, this study amalgamates insights from prior literature and case study analyses to shed light on the digital transformation process and its consequent outcomes. The discourse initiates by scrutinizing the prevailing state of digital transformation in the manufacturing sector, with a particular focus on the embracement of Internet of Things (IoT), Artificial Intelligence (AI), Digital Twin (DT) and Robotics technologies that are at the forefront of driving efficiency and spurring innovation. The article then cites China’s experience in the digital transformation of manufacturing and outlines the challenges that manufacturers may encounter, including cultural inertia and skills deficiencies, and spells out strategic interventions to overcome these obstacles. Moreover, the discussion ventures into prospective trajectories and innovations in manufacturing digitalization, forecasting the ramifications of emergent technologies such as advanced robotics, 5G connectivity, sustainable manufacturing practices, and customization trends. The significance of this research’s contribution to the scholarly domain is underscored, culminating in an exhortation directed towards industry stewards and policy framers to champion and facilitate digital transformation, accentuating its strategic imperative and the competitive leverage it bestows. This article delineates a strategic framework for navigating the intricacies of digital transformation within the manufacturing sector, offering invaluable perspectives for academicians, industry practitioners, and policy architects endeavoring to unravel new paradigms of efficiency and competitive edge in the digital epoch.

Производственный сектор находится на пороге цифровой революции, которая обещает коренным образом изменить его операционный ландшафт. Исследование посвящено цифровой интеграции в сфере производства и освещает процесс цифровой трансформации и его последствия. Дискуссия начинается с анализа текущего состояния цифровой трансформации в производственном секторе, с особым акцентом на технологиях Интернета вещей (IoT), искусственного интеллекта (AI), цифрового двойника (DT) и робототехники, которые находятся на переднем крае повышения эффективности и стимулирования инноваций. Значительное внимание уделено опыту Китая в цифровой трансформации производства и вызовам, с которыми могут столкнуться производители, включая культурную инертность и недостаток навыков. Описаны пути преодоления этих препятствий. Рассматриваются варианты выхода на перспективные траектории и инновации в цифровизации производства, прогнозируются последствия появления таких технологий, как передовая робототехника, связь 5G, устойчивые производственные практики и тенденции кастомизации. Акцентируется стратегическая важность цифровой трансформации производства и конкурентные преимущества, которые она предоставляет. В исследовании очерчены стратегические рамки проблематики цифровой трансформации в производственном секторе, оно представляет интерес как для работников науки и образования, так и для практиков, задействованных в сфере цифровизации.

competitive strategydigital transformationinnovation challengesmanufacturing industryконкурентная стратегияцифровая трансформациявызовы инновацийпроизводственная индустрияAhmed, E., Yaqoob, I., Hashem, I.A. T., Khan, I., Ahmed, A.I. A., Imran, M., & Vasilakos, A.V. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459-471. https://doi.org/10.1016/j.comnet.2017.06.013Attaran, S., Attaran, M., & Celik, B.G. (2024). Digital Twins and Industrial Internet of Things: Uncovering operational intelligence in industry 4.0. Decision Analytics Journal, 10, 100398. https://doi.org/10.1016/j.dajour.2024.100398Banga, K. (2022). Digital technologies and product upgrading in global value chains: Empirical evidence from Indian manufacturing firms. The European Journal of Development Research, 1-26. https://doi.org/10.1057/s41287-020-00357-xBaranauskas, G. (2020). Digitalization impact on transformations of mass customization concept: conceptual modelling of online customization frameworks. Marketing & Management of Innovations, (3). https://doi.org/10.21272/mmi.2020.3-09Brunetti, F., Matt, D.T., Bonfanti, A., De Longhi, A., Pedrini, G., & Orzes, G. (2020). Digital transformation challenges: strategies emerging from a multi-stakeholder approach. The TQM Journal, 32(4), 697-724. https://doi.org/10.1108/TQM-12-2019-0309Budagov, A.S., & Sukhova, N.A. (2020). Problems of effective business digital transformation management. European Proceedings of Social and Behavioural Sciences. https://doi.org/10.15405/epsbs.2020.10.03.48De Oliveira, R.I., Sousa, S.O., & De Campos, F.C. (2019). Lean manufacturing implementation: bibliometric analysis 2007-2018. The International Journal of Advanced Manufacturing Technology, 101, 979-988. https://doi.org/10.1007/s00170-018-2965-yErol, T., Mendi, A.F., & Doğan, D. (2020, October). Digital transformation revolution with digital twin technology. In 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1-7). IEEE. https://doi.org/10.1109/ISMSIT50672.2020.9254288Georgakopoulos, D., Jayaraman, P.P., Fazia, M., Villari, M., & Ranjan, R. (2016). Internet of Things and edge cloud computing roadmap for manufacturing. IEEE Cloud Computing, 3(4), 66-73. https://doi.org/10.1109/MCC.2016.91Goel, R., & Gupta, P. (2020). Robotics and industry 4.0. A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development, 157-169. https://doi.org/10.1007/978-3030-14544-6_9Grieves, M. (2014). Digital twin: manufacturing excellence through virtual factory replication. White paper, 1, 1-7Gul, R., Leong, K., Mubashar, A., Al-Faryan, M.A. S., & Sung, A. (2023). The Empirical Nexus between Data-Driven Decision-Making and Productivity: Evidence from Pakistan’s Banking Sector. Cogent Business & Management, 10(1), 2178290. https://doi.org/10.1080/23311975.2023.2178290Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production Planning & Control, 33(16), 1573-1590. https://doi.org/10.1080/09537287.2021.1882690Kagermann, H. (2013) Securing Germany’s future as a production location. Implementation recommendations for the future project Industry 4.0. Berlin: Forschungsunion. 116 pKamble, S., Gunasekaran, A., & Dhone, N.C. (2020). Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. International journal of production research, 58(5), 1319-1337. https://doi.org/10.1080/00207543.2019 .1630772Lasi, H., Fettke, P., Kemper, H.G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & information systems engineering, 6, 239-242. https://doi.org/10.1007/s12599-0140334-4Li, R., & Qiao, H. (2019). A survey of methods and strategies for high-precision robotic grasping and assembly tasks-Some new trends. IEEE/ASME Transactions on Mechatronics, 24(6), 2718-2732. https://doi.org/10.1109/TMECH.2019.2945135Lom, M., Pribyl, O., & Svitek, M. (2016, May). Industry 4.0 as a part of smart cities. In 2016 Smart Cities Symposium Prague (SCSP) (pp. 1-6). IEEE. https://doi.org/10.1109/SCSP.2016.7501015Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of industrial information integration, 6, 1-10. https://doi.org/10.1016/j.jii.2017.04.005Saeed, S., Altamimi, S.A., Alkayyal, N.A., Alshehri, E., & Alabbad, D.A. (2023). Digital transformation and cybersecurity challenges for businesses resilience: Issues and recommendations. Sensors, 23(15), 6666. https://doi.org/10.3390/s23156666Shi, Y. (2022). Digital economy: Development and future. Bulletin of Chinese Academy of Sciences (Chinese Version), 37(1), 78-87. https://doi.org/10.16418/j.issn.1000-3045.20211217002Singh, M., Fuenmayor, E., Hinchy, E.P., Qiao, Y., Murray, N., & Devine, D. (2021). Digital twin: Origin to future. Applied System Innovation, 4(2), 36. https://doi.org/10.3390/asi4020036Sundar, R., Balaji, A.N., & Kumar, R.S. (2014). A review on lean manufacturing implementation techniques. Procedia Engineering, 97, 1875-1885. https://doi.org/10.1016/j.proeng.2014.12.341Tran, M.Q., Doan, H.P., Vu, V.Q., & Vu, L.T. (2023). Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects. Measurement, 207, 112351. https://doi.org/10.1016/j.measurement.2022.112351Wang, W., Guo, Q., Yang, Z., Jiang, Y., & Xu, J. (2023). A state-of-the-art review on robotic milling of complex parts with high efficiency and precision. Robotics and Computer-Integrated Manufacturing, 79, 102436. https://doi.org/10.1016/j.rcim.2022.102436Wang, Y., & Su, X. (2021). Driving factors of digital transformation for manufacturing enterprises: A multi-case study from China. International Journal of Technology Management, 87 (2-4), 229-253. https://doi.org/10.1504/IJTM.2021.120932Wolf, M., Semm, A., & Erfurth, C. (2018). Digital transformation in companies-challenges and success factors. In Innovations for Community Services: 18th International Conference, I4CS 2018, Žilina, Slovakia, June 18-20, 2018, Proceedings (pp. 178-193). Springer International Publishing. https://doi.org/10.1007/978-3-319-93408-2_13Yanyu W., Xin, S. (2021). Driving factors of digital transformation for manufacturing enterprises: a multi-case study from China. International journal of technology management. 87(2/4), 229-253.Zhu, Z., Tang, X., Chen, C., Peng, F., Yan, R., Zhou, L., Li, Z., & Wu, J. (2022). High precision and efficiency robotic milling of complex parts: Challenges, approaches and trends. Chinese Journal of Aeronautics, 35(2), 22-46. https://doi.org/10.1016/j.cja.2020.12.030