Impact Of AI-Based Manufacturing brought to Value Creation Model of Enterprises
DOI:
https://doi.org/10.61173/emwdx033Keywords:
AI-based manufacturing, supply chain, revo-lution, challengesAbstract
Nowadays, as the technology of AI advances by leaps and bounds, it has been gradually penetrating into every aspect of people’s life, for instance, the manufacturing industry. This industry is now standing on the important point changing from large scale, standardized production to automatic and personalized customization with the help of artificial intelligence. However, what’s partially different from the past is that AI is no longer a tool using for efficiency improving, but a precious resource on reshaping the industrial competition landscape, reconstructing the value chain of enterprises, and creating new business modes. With the question of how can AI bring revolution on manufacturing industry and facilitate its revolution, and what can manufacture enterprises do to face this revolution, this paper is going to find out the mechanism on how AI-based manufacturing can change the value creation model of enterprises, and make an even deeper discussion on new business modes, strategic changing challenge, as well as organizational change and some other effects that AI techniques have brought to companies, and at last, point out the perspectives on the future development of AI-based manufacturing enterprises.
References
[1] Buchmeister, B[orut]; Palcic, I[ztok] &Ojstersek, R[obert] (2019). Artificial Intelligence in Manufacturing Companies andBroader: An Overview, Chapter 07 in DAAAM International Scientific Book 2019, pp.081-098, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-24-2, ISSN 1726-9687, Vienna, Austria
[2] Wang, L., (2019). “From Intelligence Science to Intelligent Manufacturing,” Engineering, 5(4), pp. 615–618. 10.1016/ j.eng.2019.04.011
[3] Cardon, D., Cointet, J. P., and Mazières, A., (2018). “Neurons Spike Back: The Invention of Inductive Machines and the Artificial Intelligence Controversy,” Reseaux, 5(211), pp. 173– 220.
[4] El Naqa, I., Murphy, M.J. (2015). What Is Machine Dean&Francis ISSN 2959-6130 Learning? In: El Naqa, I., Li, R., Murphy, M. (eds) Machine Learning in Radiation Oncology. Springer, Cham. https://doi. org/10.1007/978-3-319-18305-3_1
[5] Lecun, Y., Bengio, Y., and Hinton, G., (2015), “Deep Learning,” Nature, 521(7553), pp. 436–444. 10.1038/ nature14539
[6] Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., and Zhang, J. (2020). “Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook.” ASME. J. Manuf. Sci. Eng. November 2020; 142(11): 110804. https://doi. org/10.1115/1.4047855
[7] Emmanuel, A. A., Tolulope, E. E., Agnes, C. O., (2024). International Journal of Science and Technology Research Archive, 2024, 06(01), 092–107
[8] Xu, R. H. (2024). A Method for Wal-Mart Sales Forecasting Based on Machine Learning. ICCBD 2024: 2024 International conference on cloud computing and big data, 350-355.
[9] https://doi.org/10.1145/3695080.3695141 Ashok, C. (2019) “AI in Supply & Procurement,” 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, pp. 308-316, doi: 10.1109/AICAI.2019.8701357.
[10] Jaffna, S. M., & Bhowmik, P. (2024). Kiva Robotics System: Revolutionizing Automation and Expanding Healthcare Applications. Cambridge Open Engage. doi:10.33774/coe-2024- r9tzn
[11] Rababah, K., Mohd, H., & Ibrahim, H. (2011). Customer relationship management (CRM) processes from theory to practice: The pre-implementation plan of CRM system. International Journal of e-Education, e-Business, e-Management and e-Learning, 1(1), 22-27.
[12] Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, 32(2), 144-157.
[13] Bouzedif, M. (2025). Blockchain and AI in Reverse Logistics: A Qualitative Synthesis of Strategic Applications and Challenges. London Journal, 449, 449U.
[14] Xing, F., Peng, G., Zhang, B., Zuo, S., Tang, J., & Li, S. (2020). Driving Innovation with the Application of Industrial AI in the R&D Domain. In International Conference on Human-Computer Interaction (pp. 244-255). Cham: Springer International Publishing.
[15] Okeleke, P. A., Ajiga, D., Folorunsho, S. O., & Ezeigweneme, C. (2024). Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of Engineering Research Updates, 7(1), 36-49.
[16] Han, S., & Sun, X. (2024). Optimizing product design using genetic algorithms and artificial intelligence techniques. IEEE Access.
[17] Schuhmacher, A., Brieke, C., Gassmann, O., Hinder, M., & Hartl, D. (2021). Systematic risk identification and assessment using a new risk map in pharmaceutical R&D. Drug discovery today, 26(12), 2786-2793.
[18] Liu, K., Wei, Z., Zhang, C., Shang, Y., Teodorescu, R., & Han, Q. L. (2022). Towards long lifetime battery: AI- based manufacturing and management. IEEE/CAA Journal of Automatica Sinica, 9(7), 1139-1165.
[19] Öhlinger, F., Greimel, L., Glawar, R., & Sihn, W. (2022). An approach for AI-based forecasting of maintenance orders for MRO scheduling. IFAC-PapersOnLine, 55(10), 2312-2317.
[20] Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B., & Sutherland, J. W. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia Cirp, 80, 506-511.
[21] Yusupova, N., Smetanina, O., Sazonova, E., & Agadullina, A. (2019). Intelligent Information Support for Decision Making in Maintenance and Equipment Repair Management. In 2019 XXI International Conference Complex Systems: Control and Modeling Problems (CSCMP) (pp. 192-197). IEEE.
[22] Guo, Y., Zhang, W., Qin, Q., Chen, K., & Wei, Y. (2023). Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources. Soft Computing, 27(7), 4061-4076.
[23] Hojageldiyev, D. (2019). Artificial intelligence opportunities for environmental protection. In SPE gas & oil technology showcase and conference (p. D011S002R003). SPE.
[24] Qin, M., Wan, Y., Dou, J., & Su, C. W. (2024). Artificial intelligence: intensifying or mitigating unemployment? Technology in Society, 79, 102755.
[25] Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys (CSUR), 55(3), 1-44.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.

This work is licensed under a Creative Commons Attribution 4.0 International License.
