40th International Conference on Production Engineering of Serbia
ICPES 2025
Nis, Serbia, 18-19th september 2025
USE OF GENERATIVE AI IN THE CROATIAN MANUFACTURING INDUSTRY
Maja Trstenjak, Biljana Cvetic, Vanina Macowski Durski Silva, Bernhard Axmann
DOI: 10.46793/ICPES25.239T
As global manufacturing shifts toward the principles of Industry 5.0, emphasizing human-centricity, sustainability, and resilience, Generative Artificial Intelligence (GenAI) has emerged as a key enabler of next-generation industrial systems. While international research highlights the potential of GenAI tools to optimize workflows, enhance decision-making, and foster collaboration between humans and machines, the practical application of these technologies in the Croatian manufacturing industry remains underexplored. This study seeks to address that gap by examining the current state, usage patterns, and future potential of GenAI adoption among professionals in Croatian manufacturing companies.A structured online survey was distributed to 682 manufacturing professionals across Croatia, yielding 33 complete responses. The questionnaire gathered data on demographics, company characteristics, and the use of GenAI in general applications (e.g., ChatGPT, DALL·E), office productivity tools (e.g., Microsoft Copilot, Google Gemini), and automation systems. The results reveal that while awareness of GenAI is relatively high, actual usage is still low. Text-based tools are the most widely used, primarily for content generation and operational support. Use of AI-enhanced office tools and automation platforms is less common, and the intensity of usage varies significantly across respondents. Despite the modest adoption levels, the study finds strong interest in expanding the use of GenAI technologies: 85% of respondents indicated they plan to increase usage in the near future. However, challenges such as limited infrastructure, lack of training, and inconsistent integration across workflows remain prevalent. The findings underscore the need for targeted policy support, investment in digital infrastructure, and cross-sector collaboration to unlock the full potential of GENAI in the Croatian manufacturing sector. This research provides a valuable empirical foundation for future studies and strategic initiatives aimed at fostering sustainable, intelligent, and human-centered manufacturing systems in Croatia, in line with the broader goals of Industry 5.0
Industry 5.0, Generative AI, artificial intelligence, manufacturing
[1] K. P. Agrawal, “Towards Adoption of Generative AI in Organizational Settings,” J. Comput. Inf. Syst., vol. 64, no. 5, pp. 636–651, Sep. 2024, doi: 10.1080/08874417.2023.2240744.
[2] P. Boareto, A. Szejka, E. Loures, F. Deschamps, and E. Santos, “Accelerating Industry 4.0 and 5.0: The Potential of Generative Artificial Intelligence,” presented at the INNOVATIVE INTELLIGENT INDUSTRIAL PRODUCTION AND LOGISTICS, IN4PL 2024, PT I, M. Dassisti, K. Madani, and H. Panetto, Eds., 2025, pp. 456–472. doi: 10.1007/978-3-031-80760-2_29.
[3] M. Ghobakhloo, M. Fathi, M. Iranmanesh, M. Vilkas, A. Grybauskas, and A. Amran, “Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals,” JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, vol. 35, no. 9, pp. 94–121, May 2024, doi: 10.1108/JMTM-12-2023-0530.
[4] M. Mustapic, M. Trstenjak, P. Greguric, and T. Opetuk, “Implementation and Use of Digital, Green and Sustainable Technologies in Internal and External Transport of Manufacturing Companies,” Sustainability, vol. 15, no. 12, Art. no. 12, Jan. 2023, doi: 10.3390/su15129557.
[5] E. Mikolajewska, D. Mikolajewski, T. Mikolajczyk, and T. Paczkowski, “Generative AI in AI-Based Digital Twins for Fault Diagnosis for Predictive Maintenance in Industry 4.0/5.0,” APPLIED SCIENCES-BASEL, vol. 15, no. 6, Mar. 2025, doi: 10.3390/app15063166.
[6] P. Jourabchi Amirkhizi, S. Pedrammehr, S. Pakzad, and A. Shahhoseini, “Generative Artificial Intelligence in Adaptive Social Manufacturing: A Pathway to Achieving Industry 5.0 Sustainability Goals,” Processes, vol. 13, no. 4, Art. no. 4, Apr. 2025, doi: 10.3390/pr13041174.
[7] S. Sai, R. Sai, and V. Chamola, “Generative AI for Industry 5.0: Analyzing the Impact of ChatGPT, DALLE, and Other Models,” IEEE Open Journal of the Communications Society, vol. 6, pp. 3056–3066, 2025, doi: 10.1109/OJCOMS.2024.3400161.
[8] K. S. Kiangala and Z. Wang, “An experimental hybrid customized AI and generative AI chatbot human machine interface to improve a factory troubleshooting downtime in the context of Industry 5.0,” Int J Adv Manuf Technol, vol. 132, no. 5, pp. 2715–2733, May 2024, doi: 10.1007/s00170-024-13492-0.
[9] J. Yang, Y. Wang, X. Wang, X. Wang, X. Wang, and F.-Y. Wang, “Generative AI Empowering Parallel Manufacturing: Building a ‘6S’ Collaborative Production Ecology for Manufacturing 5.0,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–15, 2024, doi: 10.1109/TSMC.2024.3349555.
[10] V. Terziyan and O. Kaikova, “Hybrid Additive Manufacturing: A Convergence of Physical, Digital, and Social Realms Driven by Generative AI,” presented at the ADVANCES AND TRENDS IN ARTIFICIAL INTELLIGENCE: THEORY AND APPLICATIONS, IEA-AIE 2024, H. Fujita, R. Cimler, A. Hernandez-Matamoros, and M. Ali, Eds., 2024, pp. 427–441. doi: 10.1007/978-981-97-4677-4_35.
[11] Y. Masuda et al., “Vision Paper for Enabling Generative AI Digital Platform Using AIDAF in Healthcare and Manufacturing Industry,” in Human Centred Intelligent Systems, A. Zimmermann, R. Schmidt, L. C. Jain, and R. J. Howlett, Eds., Singapore: Springer Nature, 2025, pp. 177–190. doi: 10.1007/978-981-97-8598-8_16.
[12] “Redefining Human-Machine Collaboration: Industry 5.0 to Improve Safety and Efficiency | IEEE Latin America Transactions.” Accessed: Jul. 29, 2025.
[Online]. Available: https://latamt.ieeer9.org/index.php/transactions/article/view/9412
[13] H. Wang, M. Liu, and W. Shen, “Industrial-generative pre-trained transformer for intelligent manufacturing systems,” IET Collab. Intell. Manufact., vol. 5, no. 2, p. e12078, Jun. 2023, doi: 10.1049/cim2.12078.
[14] J. Leng et al., “AIGC-empowered smart manufacturing: Prospects and challenges,” Robotics and Computer-Integrated Manufacturing, vol. 97, p. 103076, Feb. 2026, doi: 10.1016/j.rcim.2025.103076.
[15] “From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins | International Journal of Precision Engineering and Manufacturing-Green Technology.” Accessed: Jul. 29, 2025.
[Online]. Available: https://link.springer.com/article/10.1007/s40684-025-00750-z
[16] J.-F. Dang, T.-L. Chen, and H.-Y. Huang, “The human-centric framework integrating knowledge distillation architecture with fine-tuning mechanism for equipment health monitoring,” Advanced Engineering Informatics, vol. 65, p. 103167, May 2025, doi: 10.1016/j.aei.2025.103167.
[17] N. Berx, W. Decré, J. De Schutter, and L. Pintelon, “A harmonious synergy between robotic performance and well-being in human-robot collaboration: A vision and key recommendations,” Annual Reviews in Control, vol. 59, p. 100984, Jan. 2025, doi: 10.1016/j.arcontrol.2024.100984.