Digital Transformation in Production Systems: A Global Business Perspective
DOI:
https://doi.org/10.56830/WRBA07202605Keywords:
Digital Transformation, Production System, Global BusinessAbstract
Digital transformation in manufacturing represents the broad integration of digital technologies across manufacturing value chains and structures, transforming business strategies, organizational capabilities, and operational dynamics. The Internet of Things, realtime analytics, smart robotics, additive manufacturing, artificial intelligence, and automation enable increasingly self-sufficient operations. These techniques support planning, scheduling, adequate management, process control, defect prediction, predictive maintenance, and inventory control. Aid logistics and optimization. Change beyond technology adoption requires strategic transformation, plant management, workforce development, and new skills to tackle efficiency gaps and align production with evolving cost structures. Digitization yields productivity gains, cost reductions, performance improvements, efficiencies, and synergies as organizational outcomes, and stop-person value. Cross-local scale identifies North America, Europe, and South Korea as major adopters of Industry 4.0 and Internet of Things technologies. However, excessive investment, uncertain returns, and flexible capabilities hinder adoption in emerging economies. Overall, the composition shows several known country-wide pathways and limited commitments, indicating that the creation of virtual transformation is multifaceted and requires context-sensitive techniques for sustained and aggressive performance.
References
Abdelkader, S., Amissah, J., & Abdel-Rahim, O. (2024). Virtual power plants: an in-depth analysis of their advancements and importance as crucial players in modern power systems. Energy, Sustainability and Society, 14(1), 52. DOI: https://doi.org/10.1186/s13705-024-00483-y
Baslyman, M. (2022). Digital transformation from the industry perspective: definitions, goals, conceptual model, and processes. IEEE Access, 10, 42961-42970. DOI: https://doi.org/10.1109/ACCESS.2022.3166937
Belli, L., Davoli, L., Medioli, A., Marchini, P. L., & Ferrari, G. (2019). Toward Industry 4.0 with IoT: Optimizing business processes in an evolving manufacturing factory. Frontiers in ICT, 6, 17. DOI: https://doi.org/10.3389/fict.2019.00017
Bigliardi, B., Filippelli, S., Petroni, A., & Tagliente, L. (2022). The digitalization of supply chain: a review. Procedia Computer Science, 200, 1806-1815. DOI: https://doi.org/10.1016/j.procs.2022.01.381
Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., et al. (2022). A computer science perspective on digital transformation in production. ACM Transactions on Internet of Things, 3(2), 1-32. DOI: https://doi.org/10.1145/3502265
Chehri, A., Zimmermann, A., Schmidt, R., & Masuda, Y. (2021). Theory and practice of implementing a successful enterprise IoT strategy in the industry 4.0 era. Procedia computer science, 192, 4609-4618. DOI: https://doi.org/10.1016/j.procs.2021.09.239
Chen, C., Lee Kong, T., & Kan, W. (2023). Identifying the promising production planning and scheduling method for manufacturing in Industry 4.0: a literature review. Production & manufacturing research, 11(1), 2279329. DOI: https://doi.org/10.1080/21693277.2023.2279329
Črešnar, R. D. (2022). It takes two to tango: technological and non-technological factors of Industry 4.0 implementation in manufacturing firms. R. Črešnar. Review of Managerial Science, 17(3), 827-853. DOI: https://doi.org/10.1007/s11846-022-00543-7
De Paiva Pereira, P. E. (2018). Industry 4.0: from strategic maturity models to operational deployment using lean six sigma tools.
Deng, K. (2018). Three Empirical Studies on Digital Innovation Management. New Organizing Logic of Antecedents and Consequences of Innovation.
Evans, N., Miklosik, A., Bosua, R., & Qureshi, A. M. (2022). Digital business transformation: An experience-based holistic framework. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2022.3221984
Fenta, E. W., Tsegaye, A. A., Abere, A. E., & Tefera, G. T. (2025). Opportunities in flexible manufacturing systems in the near future. Global journal of flexible systems management, 26(2), 247-267. DOI: https://doi.org/10.1007/s40171-025-00437-z
Frankó, A., Hollósi, G., Ficzere, D., & Varga, P. (2022). Applied Machine Learning for IIoT and Smart Production. Methods to Improve Production Quality, Safety and Sustainability. DOI: https://doi.org/10.3390/s22239148
Gonue7alves Machado, C., Kurdve, M., Winroth, M., & Bennett, D. (2018). Production Management and Smart Manufacturing from a Systems Perspective.
Jin, Z., Wang, H., Luo, C., & Guo, C. Y. (2024). The impact of digital transformation on international carbon competitiveness: Empirical evidence from manufacturing decomposition. Journal of cleaner production. DOI: https://doi.org/10.1016/j.jclepro.2024.141184
Kumar, V., Sindhwani, R., Behl, A., Kaur, A., & Pereira, V. (2024). Modelling and analysing the enablers of digital resilience for small and medium enterprises. Journal of enterprise information management, 37(5), 1677-1708. DOI: https://doi.org/10.1108/JEIM-01-2023-0002
Mastilo, Z. (2017). Impact of Digital Growth in Modern Business. DOI: https://doi.org/10.11114/bms.v3i4.2650
Mele, G., Capaldo, G., & Secundo, G. (2024). Revisiting the idea of knowledge-based dynamic capabilities for digital transformation. Journal of Knowledge. DOI: https://doi.org/10.1108/JKM-02-2023-0121
Oberg, C., Shams, T., & Asnafi, N. (2018). Additive manufacturing and business models. Current knowledge and missing perspectives. DOI: https://doi.org/10.22215/timreview/1162
Oliveira, M., & Afonso, D. (2019). Industry focused in data collection: how industry 4.0 is handled by big data. DOI: https://doi.org/10.1145/3352411.3352414
Pech, M., & Vrchota, J. (2022). The product customization process in relation to industry 4.0 and digitalization. Processes. DOI: https://doi.org/10.3390/pr10030539
Qorbani, D., & Groesser, S. (2020). The impact of Industry 4.0 technologies on production and supply chains.
Ramzi, B., Ahmad, H., & Zakaria, N. (2019). A Conceptual Model on People Approach and Smart Manufacturing.
RISI, G. (2025). Digitalization, Artificial Intelligence, and wage inequalities. a task-based analysis of italian labor market dynamics with an urban perspective.
Sanneman, L., Fourie, C., & A. Shah, J. (2020). The State of Industrial Robotics: Emerging Technologies, Challenges, and Key Research Directions. DOI: https://doi.org/10.1561/9781680838015
Tenakwah, E. S., & Watson, C. (2025). Embracing the AI/automation age: preparing your workforce for humans and machines working together. Strategy & Leadership. DOI: https://doi.org/10.1108/SL-05-2024-0040
Von Leipzig, T., Gamp, M., Manz, D., Schottle, K., Ohlhausen, P., Oosthuizen, G., et al. (2017). Initialising customer-orientated digital transformation in enterprises. DOI: https://doi.org/10.1016/j.promfg.2017.02.066
Wan, J., Li, X., Dai, H. N., Kusiak, A., Martínez-García, M., & Li, D. (2021). Artificial Intelligence-Driven Customized Manufacturing Factory. Key Technologies, Applications, and Challenges. DOI: https://doi.org/10.1109/JPROC.2020.3034808
Wang, Y., Wang, T., & Wang, Q. (2024). The impact of digital transformation on enterprise performance: An empirical analysis based on China’s manufacturing export enterprises. DOI: https://doi.org/10.20944/preprints202308.1266.v1
Wessel, L., Baiyere, A., Ologeanu-Taddei, R., Cha, J., & Blegind Jensen, T. (2021). Unpacking the Difference Between Digital Transformation and IT-Enabled Organizational Transformation.
Whysall, Z., Owtram, M., & Brittain, S. (2019). The new talent management challenges of industry 4.0. DOI: https://doi.org/10.1108/JMD-06-2018-0181
Yaqub, M. Z., & Alsabban, A. (2023). Industry-4.0-enabled digital transformation: Prospects, instruments, challenges, and implications for business strategies. Sustainability. DOI: https://doi.org/10.3390/su15118553
Zhai, H., Yang, M., & Chan, K. C. (2022). Does digital transformation enhance a firm's performance? Evidence from China. Technology in society. DOI: https://doi.org/10.1016/j.techsoc.2021.101841








