Automation of Production Management Processes Using Artificial Intelligence: Impact on the Efficiency and Resilience of Manufacturing Systems
DOI:
https://doi.org/10.62486/latia2025311Keywords:
Artificial intelligence, Production management, Automation, Manufacturing systems, Predictive analytics, Smart manufacturingAbstract
The rapid technological advancement and global competition provokes the automation of production management processes through artificial intelligence. This study investigates the integration of artificial intelligence into production management and its influence on the efficiency and resilience of manufacturing systems. The research is motivated by the growing relevance of AI within the paradigm of Industry 4.0, where advanced digital technologies are transforming traditional production models. The main objective is assessing how AI technologies – such as machine learning, deep learning, predictive analytics, and intelligent automation – enhance core production functions, including planning, quality control, maintenance, logistics, and energy management. The study applies a mixed-method approach, combining comparative analysis, case study evaluation, and content analysis of scientific and industrial data. Empirical evidence (1653 records) was drawn from both international (e.g., Siemens, Fanuc, Bosch) and Ukrainian (e.g., Interpipe, Kernel) manufacturing companies. Results after screening, filtration, validation, verification and exclusion (50 records) demonstrate measurable improvements in key performance indicators, such as reduced downtime, decreased defect rates, increased logistical accuracy, and optimized energy use. At the same time, the paper addresses the challenges accompanying AI integration, including cybersecurity risks, social impacts, regulatory gaps, and organizational readiness. The research concludes that AI not only improves operational performance but also strengthens adaptive capacity and strategic stability, contributing to the formation of intelligent, self-learning, and data-driven production systems. This article will be of particular interest to production managers, industrial engineers, innovation strategists, policymakers, and academic researchers seeking to understand and apply AI for sustainable industrial transformation.
References
Potwora M, Vdovichena O, Semchuk D, Lipych L, Saienko V. The use of artificial intelligence in marketing strategies: Automation, personalization and forecasting. J of Man World. 2024;2:41-49. https://doi.org/10.53935/jomw.v2024i2.275 DOI: https://doi.org/10.53935/jomw.v2024i2.275
Pancholi S, Gupta MK, Bartoszuk M, et al. Transforming additive manufacturing with artificial intelligence: A review of current and future trends. Arch Computat Methods Eng. 2025. https://doi.org/10.1007/s11831-025-10283-y DOI: https://doi.org/10.1007/s11831-025-10283-y
Li H, Lu Z, Zhang Z, Tanasescu C. How does artificial intelligence affect manufacturing firms' energy intensity? Energy Econ. 2025;141:108109. https://doi.org/10.1016/j.eneco.2024.108109 DOI: https://doi.org/10.1016/j.eneco.2024.108109
Chan L, Hogaboam L, Cao R. Artificial intelligence in manufacturing. In: Applied Artificial Intelligence in Business. Springer. 2022;173–185. https://doi.org/10.1007/978-3-031-05740-3_11 DOI: https://doi.org/10.1007/978-3-031-05740-3_11
Wan J, Li X, Dai HN, Kusiak A, Martínez-García M, Li D. Artificial intelligence-driven customized manufacturing factory: Key technologies, applications, and challenges. arXiv. 2021. https://arxiv.org/abs/2108.03383
Bermeo-Ayerbe MA, Ocampo-Martinez C, Diaz-Rozo J. Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems. Energy. 2022;238(B):121691. https://doi.org/10.1016/j.energy.2021.121691 DOI: https://doi.org/10.1016/j.energy.2021.121691
Kabir MA, Keung JW, Bennin KE, Zhang M. Assessing the significant impact of concept drift in software defect prediction. In: Proc. 43rd Annu. Computer Software and Applications Conf. (COMPSAC). 2019;1:53–58. https://doi.org/10.1109/COMPSAC.2019.00017 DOI: https://doi.org/10.1109/COMPSAC.2019.00017
Gal Y. Uncertainty in deep learning [dissertation]. Cambridge (UK): University of Cambridge; 2016. http://106.54.215.74/2019/20190729-liuzy.pdf
Stadnyk I, Piddubnyi V, Mykhailyshyn R, Petrychenko I, Fedoriv V, Kaspruk V. The influence of rheology and design of modeling rolls on the flow and specific gravity during dough rolling and injection. J Adv Manuf Syst. 2022;22(2):403–421. https://doi.org/10.1142/s0219686723500208 DOI: https://doi.org/10.1142/S0219686723500208
Lavrinenko O, Danileviča A, Jermalonoka I, Ruža O, Sprūde M. The mobile economy: effect of the mobile computing devices on entrepreneurship in Latvia. Entrepreneursh Sustain Issues. 2024;11(3):335–347. https://doi.org/10.9770/jesi.2024.11.3(23) DOI: https://doi.org/10.9770/jesi.2024.11.3(23)
Shvets I, Hrabovenko O, Dotsenko S, Nesterenko V. The influence of mobile transport means. In: Proc. Int. Conf. Transport Means. 2020:671–675. https://rep.nuos.edu.ua/server/api/core/bitstreams/ead57793-f426-4a48-8970-19561356b034/content
Gubal GN, Stashenko MA. Improvement of an estimate of the global existence theorem for solutions of the Bogoliubov equations. Theor Math Phys. 2005;145:1736–1740. https://doi.org/10.1007/s11232-005-0195-6 DOI: https://doi.org/10.1007/s11232-005-0195-6
Hubal HM. The generalized kinetic equation for symmetric particle systems. Math Scand. 2012;110(1):140–160. https://www.jstor.org/stable/24493757 DOI: https://doi.org/10.7146/math.scand.a-15201
Dobrovolska O, Sonntag R, Masiuk Y, Bahorka M, Yurchenko N. Is increasing a share of R&D expenditure in GDP a factor in strengthening the level of innovation development in Ukraine compared with GII’s top countries? Probl Perspect Manag. 2023;21(4):713–723. https://doi.org/10.21511/ppm.21(4).2023.53 DOI: https://doi.org/10.21511/ppm.21(4).2023.53
Khlamov S, Savanevych V, Briukhovetskyi O, Trunova T. Big data analysis in astronomy by the Lemur software. Proc of the 6th IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). 2023:5-8. https://doi.org/10.1109/UkrMiCo61577.2023.10380398 DOI: https://doi.org/10.1109/UkrMiCo61577.2023.10380398
Kozlovskyi S, Bilenko D, Dluhopolskyi O, Vitvitskyi S, Bondarenko O, Korniichuk O. Determinants of COVID-19 death rate in Europe: empirical analysis. Probl Ekorozwoju. 2021;16(1):17–28. https://doi.org/10.35784/pe.2021.1.02 DOI: https://doi.org/10.35784/pe.2021.1.02
Tiurina A, Nahornyi V, Ruban O, Tymoshenko M, Vedenieiev V, Terentieva N. Problems and prospects of human capital development in post-industrial society. Postmodern Openings 2022;13(3):412-424. https://doi.org/10.18662/po/13.3/497 DOI: https://doi.org/10.18662/po/13.3/497
Li X, Hou B, Yu W, Lu X, Yang C. Applications of artificial intelligence in intelligent manufacturing: a review. Front Inf Technol Electron Eng. 2017;18:86–96. https://doi.org/10.1631/fitee.1601885 DOI: https://doi.org/10.1631/FITEE.1601885
Trabelsi MA. The impact of artificial intelligence on economic development. J Electron Bus Digit Econ. 2024;3(2):142–155. https://doi.org/10.1108/JEBDE-10-2023-0022 DOI: https://doi.org/10.1108/JEBDE-10-2023-0022
Apsilyam NM, Ashrapova LU. Artificial intelligence as a driver of digital transformation. Pioneering Stud Theor. 2023;1(2):4–8. https://pstjournal.uz/index.php/pst/article/view/7
Rane NL, Kaya O, Rane J. Artificial intelligence, machine learning, and deep learning applications in smart and sustainable industry transformation. Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry 5.0. 2024;28–52. https://doi.org/10.70593/978-81-981271-8-1_2 DOI: https://doi.org/10.70593/978-81-981271-8-1_2
Akhtar ZB. Artificial intelligence (AI) within manufacturing: An investigative exploration for opportunities, challenges, future directions. Metaverse. 2024;5(2):2731. https://doi.org/10.54517/m.v5i2.2731 DOI: https://doi.org/10.54517/m.v5i2.2731
Kubatko OV, Ozims SC, Voronenko VI. Influence of artificial intelligence on business decision-making. Mech Econ Regul. 2024;1(103):17–23. https://doi.org/10.32782/mer.2024.103.03 DOI: https://doi.org/10.32782/mer.2024.103.03
Cuntz A, Fink C, Stamm H. Artificial intelligence and intellectual property: an economic perspective. SSRN Electron J. 2024;(77):1-34. https://doi.org/10.2139/ssrn.4757971 DOI: https://doi.org/10.2139/ssrn.4858320
Rodrigues R. Legal and human rights issues of AI: gaps, challenges and vulnerabilities. J Responsible Technol. 2020;4:100005. https://doi.org/10.1016/j.jrt.2020.100005 DOI: https://doi.org/10.1016/j.jrt.2020.100005
Özkiziltan D, Hassel A. Artificial intelligence at work: an overview of the literature. SSRN Electron J. 2021;1-84. https://doi.org/10.2139/ssrn.3796746 DOI: https://doi.org/10.2139/ssrn.3796746
Tarasenko S, Karintseva O, Slabko T. Analysis of AI policy in Ukraine: normative impact on the restructuring of the economy. Ekon Pidpryiemnytstvo. 2024;(2)132:6. https://doi.org/10.32782/1814-1161/2024-2-6 DOI: https://doi.org/10.32782/1814-1161/2024-2-6
Babina T, Fedyk A. The effects of AI on firms and workers. Brookings Institution [Internet]. Available from: https://www.brookings.edu/articles/the-effects-of-ai-on-firms-and-workers/
Oyekunle D, Boohene D. Digital transformation potential: The role of artificial intelligence in business. Int J Prof Bus Rev. 2024;9(3):1–17. https://doi.org/10.26668/businessreview/2024.v9i3.4499 DOI: https://doi.org/10.26668/businessreview/2024.v9i3.4499
Budhiraja K. Smart AI gadgets for home that actually make life easier. Hindustan Times [Internet]. Available from: https://www.hindustantimes.com/technology/smart-ai-gadgets-for-home-that-actually-make-life-easier-101749040225058.html
Plathottam S, Rzonca A, Lakhnori R, Iloeje CO. A review of artificial intelligence applications in manufacturing operations. J Adv Manuf Process. 2023;5(3): e10159. https://doi.org/10.1002/amp2.10159 DOI: https://doi.org/10.1002/amp2.10159
Sofianidis G, Rožanec JM, Mladenić D, Kyriazis D. A review of explainable artificial intelligence in manufacturing. arXiv preprint. 2021. https://doi.org/10.48550/arXiv.2107.02295 DOI: https://doi.org/10.1561/9781680838770.ch5
Khalil RA, Saeed N, Moradi Fard Y, Al-Naffouri TY, Alouini M-S. Deep learning in industrial internet of things: potentials, challenges, and emerging applications. arXiv preprint. 2020. https://doi.org/10.48550/arXiv.2008.06701 DOI: https://doi.org/10.1109/JIOT.2021.3051414
Khlamov S, Savanevych V, Tabakova I, Kartashov V, Trunova T, Kolendovska M. Machine vision for astronomical images using the modern image processing algorithms implemented in the CoLiTec software. Measurements and Instrumentation for Machine Vision. 2024;12:269-310. https://doi.org/10.1201/9781003343783-12 DOI: https://doi.org/10.1201/9781003343783-12
Jagatheesaperumal SK, Rahouti M, Ahmad K, Al-Fuqaha A, Guizani M. The duo of artificial intelligence and big data for Industry 4.0: review of applications, techniques, challenges, and future research directions. arXiv preprint. 2021. https://doi.org/10.48550/arXiv.2104.02425 DOI: https://doi.org/10.1109/JIOT.2021.3139827
Dreyfus PA, Pélissier A, Psarommatis F, Kiritsis D. Data-based model maintenance in the era of Industry 4.0: A methodology. J Manuf Syst. 2022;63:304–316. https://doi.org/10.1016/j.jmsy.2022.03.015 DOI: https://doi.org/10.1016/j.jmsy.2022.03.015
Yue X, Kang M, Zhang Y. The impact of artificial intelligence usage on supply chain resilience in manufacturing firms: A moderated mediation model. J Manuf Technol Manag. 2025;36(4):759-776. https://doi.org/10.1108/JMTM-07-2024-0379 DOI: https://doi.org/10.1108/JMTM-07-2024-0379
Bullers WI, Nof SY, Whinston AB. Artificial intelligence in manufacturing planning and control. AIIE Trans. 1980;12(4):351–363. https://doi.org/10.1080/05695558008974527 DOI: https://doi.org/10.1080/05695558008974527
Orlov S, Trunova T, Hadzhyiev E, Okhotko H, Bondar Ye, Netrebin Yu. Usage of OpenAPI specification in distributed microservices-oriented architecture of the information system for astronomical data processing. CEUR Workshop Proceedings. 2024;3790:532-544. https://ceur-ws.org/Vol-3790/paper46.pdf
Archana T, Stephen RK. The future of artificial intelligence in manufacturing industries. In: Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI. IGI Global. 2024;20–40. https://doi.org/10.4018/979-8-3693-4276-3.ch004 DOI: https://doi.org/10.4018/979-8-3693-4276-3.ch004
Zenisek J, Holzinger F, Affenzeller M. Machine learning-based concept drift detection for predictive maintenance. Comp Ind Eng. 2019;137:106031. https://doi.org/10.1016/j.cie.2019.106031 DOI: https://doi.org/10.1016/j.cie.2019.106031
Chychun V, Chaplynska N, Shpatakova O, Pankova A, Saienko V. Effective management in the remote work environment. J of Sys and Man Sc. 2023;13(3):244-257. https://doi.org/10.33168/JSMS.2023.0317 DOI: https://doi.org/10.33168/JSMS.2023.0317
Vasylieva N, James H. The effect of urbanization on food security and agricultural sustainability. Econ and Soc. 2021;14(1):76-88. https://doi.org/10.14254/2071-789X.2021/14-1/5 DOI: https://doi.org/10.14254/2071-789X.2021/14-1/5
Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G. Learning under concept drift: a review. IEEE Trans Knowl Data Eng. 2018;31(12):2346–2363. https://doi.org/10.1109/TKDE.2018.2876857 DOI: https://doi.org/10.1109/TKDE.2018.2876857
Savanevych V, Khlamov V, Briukhovetskyi O, Trunova T, Tabakova I. Mathematical Methods for an Accurate Navigation of the Robotic Telescopes. Mathematics. 2023;11(10):2246. https://doi.org/10.3390/math11102246 DOI: https://doi.org/10.3390/math11102246
Bondarenko S, Shlafman N, Kuprina N, Kalaman O, Moravska O, Tsurkan N. Planning, Accounting and Control as Risk Management Tools for Small Business Investment Projects. Emerg Sci J. 2021;5(5):650–666. https://doi.org/10.28991/esj-2021-01302 DOI: https://doi.org/10.28991/esj-2021-01302
Vasylieva N, Pugach A. Economic assessment of technical maintenance in grain production of Ukrainian agriculture. Bulg J of Agricul Sc. 2017;23(2):198–203. https://www.agrojournal.org/23/02-04.pdf
Zavhorodnii A, Ohiienko M, Biletska Y, Bondarenko S, Duiunova T, Bodenchuk L. Digitalization of Agribusiness in the Development of Foreign Economic Relations of the Region. J of Inf Tech Manag. 2021;13:123-141. https://doi.org/10.22059/jitm.2021.82613
Orazbayev B, Orazbayeva K, Uskenbayeva G, Dyussembina E, Shukirova A, Rzayeva L, Tuleuova R. System of models for simulation and optimization of operating modes of a delayed coking unit in a fuzzy environment. Sci Rep. 2023;13:14317. https://doi.org/10.1038/s41598-023-41455-0 DOI: https://doi.org/10.1038/s41598-023-41455-0
Hrypynska NV, Dykha MV, Korkuna NM, Tsehelyk HH. Applying Dynamic Programming Method to Solving the Problem of Optimal Allocation of Funds between Projects. J of Auto and Inf Sc. 2020;52(1):56-64. https://doi.org/10.1615/JAutomatInfScien.v52.i1.60 DOI: https://doi.org/10.1615/JAutomatInfScien.v52.i1.60
Shvets I, Hrabovenko O, Dotsenko S, Nesterenko V. Results of the experimental research of the medium speed diesel engine work on soybean oil. Proc of the 24th Intern Conf, Transport Means. 2020:671–675. https://rep.nuos.edu.ua/server/api/core/bitstreams/ead57793-f426-4a48-8970-19561356b034/content
Ladonko L, Mozhaikina N, Buryk Z, Ostrovskyi I, Saienko V. Regional aspects of the economy modernization: the qualitative evidence from EU countries. Inter J for Qual Res. 2022;16(3):851-862. https://doi.org/10.24874/IJQR16.03-13 DOI: https://doi.org/10.24874/IJQR16.03-13
Vakhovych I, Kryvovyazyuk I, Kovalchuk N, Kaminska I, Volynchuk Y, Kulyk Y. Application of Information Technologies for Risk Management of Logistics Systems. Proc. of the 62nd International Scientific Conference on ITMS of Riga Technical University. 2021:1-6. https://doi.org/10.1109/ITMS52826.2021.9615297 DOI: https://doi.org/10.1109/ITMS52826.2021.9615297
Kryvovyazyuk I, Vakhovych I, Kaminska I, Dorosh V. Managerial innovations in methodology of solving export-import activity problems and ensuring international corporations business excellence. Quality - Access to Success. 2020;21(178):50-55. https://lib.lntu.edu.ua/sites/default/files/2021-01/QAS_Vol.21_No.178_Oct.2020_p.50-55.pdf
Ponomarenko I, Pavlenko V, Morhulets O, Ponomarenko D, Ukhnal N. Application of artificial intelligence in digital marketing. CEUR Workshop Proceedings. 2024;3662:155-166. https://ceur-ws.org/Vol-3662/paper22.pdf.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Kateryna Kolos , Oleg Kubrak , Yuliya Olimpiyeva, Pavlo Ihnatenko , Olena Furtat (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
