Industrial Process Automation and Business Technology Management: A Cobb–Douglas Elasticity Perspective
DOI:
https://doi.org/10.61954/2616-7107/2025.9.4-2Keywords:
Automation, Industrial Development, Cobb-Douglas Function, Productivity, Resource Optimisation.Abstract
Introduction. Technological advances and the emergence of artificial intelligence have contributed to the growing importance of industrial process automation. Furthermore, automation is a key driver of Industry 4.0, impacting industrial productivity and growth. A reliability-based approach is proposed to assess the impact of automation that quantitatively links technology investments to economic indicators, particularly the Cobb-Douglas production function.
Aim and tasks. This study aims to demonstrate a direct link between automation and industrial production using the Cobb-Douglas model. This model bridges the gap between classical production functions and modern technologies through linear and log-linear models, allowing for a more accurate assessment of the impact of automation on industry.
Results. The study found that adding one robot per 10,000 employees corresponds to a 0.055 percentage point increase in the industrial share. After applying the log-linear (Cobb-Douglas) specification, a result/coefficient of 0.53 was obtained, meaning that the industrial share would increase by 0.53% if only a 1% increase in robot deployment were realised. This result confirms that automation is a significant production factor with a proportional effect. The analysis showed that elasticity-based models can capture industrial dynamics more realistically than linear models. The empirical results provide a measurable basis for positioning automation alongside traditional factors, such as labour and capital. The comparative model approach provides evidence for the suitability of the Cobb-Douglas forms in contemporary industrial research, and the empirical findings lay the foundation for further large-scale research with broader datasets.
Conclusions. Automation contributes to the strategic development of industrial competitiveness, and its effectiveness is achieved only in conjunction with innovation, digitalisation, education, and support from institutional and economic environments. Automation increases added value, as confirmed by the Cobb-Douglas model. However, sustainable development also requires human capital, innovation, and effective environmental policies. A comprehensive approach will allow for an objective assessment of the industry's potential and the development of a sustainable development strategy.
References
Brannick, M. T., Salas, E., & Prince, C. (1997). Team performance assessment and measurement: Theory, methods, and applications. Mahwah, NJ: Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410602053
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. New York: W.W. Norton & Company.
Carayannis, E. G., & Campbell, D. F. J. (2019). Smart Quintuple Helix Innovation Systems. Springer. https://doi.org/10.1007/978-3-030-01517-6
Chen, H., & Tian, Z. (2022). Environmental uncertainty, resource orchestration and digital transformation: A fuzzy-set QCA approach. Journal of Business Research, 139, 184–193. https://doi.org/10.1016/j.jbusres.2021.09.048
Chesbrough, H. (2020). Open innovation results: Going beyond the hype and getting down to business. Oxford University Press. https://doi.org/10.1093/oso/9780198841906.001.0001
Du, J., Zhao, C., Hu, Y., & Chen, X. (2024). Impact of industrial robots on labor income share: Empirical evidence from Chinese A-listed companies. Sustainability, 16(16), 6928. https://doi.org/10.3390/su16166928
Edmondson, A. C., & Harvey, J.-F. (2018). Cross-boundary teaming for innovation: Integrating research on teams and knowledge in organizations. Human Resource Management Review, 28(4), 347–360. https://doi.org/10.1016/j.hrmr.2017.03.002
Eurostat. (2023). Industry statistics at a glance. Retrieved from https://ec.europa.eu/eurostat
Eurostat. (2024). National accounts and GDP by industry (nama_10_a10). Retrieved September 20, 2025, from https://ec.europa.eu/eurostat
Geels, F. W. (2019). Socio-technical transitions to sustainability: A review of criticisms and elaborations of the Multi-Level Perspective. Current Opinion in Environmental Sustainability, 39, 187–201. https://doi.org/10.1016/j.cosust.2019.06.009
Hackman, J. R. (2011). Collaborative intelligence: Using teams to solve hard problems. San Francisco, CA: Berrett-Koehler.
Hassoun, A., Aït-Kaddour, A., Abu-Mahfouz, A. M., Rathod, N. B., Bader, F., Barba, F. J., … Regenstein, J. (2022). The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 63(23), 6547–6563. https://doi.org/10.1080/10408398.2022.2034735
International Federation of Robotics. (2023). World Robotics Report 2023. Frankfurt: IFR. https://ifr.org/img/worldrobotics/2023_WR_extended_version.pdf
Kniaziev, S., & Soldak, M. (2024). Momentum of industrial growth: Methods of calculation and ways of use. Economics Ecology Socium, 8(2), 12–33. https://doi.org/10.61954/2616-7107/2024.8.2-2
Leblanc, R. (2024). Building and leading effective teams: Strategies for today’s dynamic organizations. New York, NY: Routledge.
Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial Artificial Intelligence for Industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
Lencioni, P. (2002). The five dysfunctions of a team: A leadership fable. San Francisco, CA: Jossey-Bass.
Liu, Y. (2023). Impact of industrial robots on environmental pollution: evidence from China. Scientific Reports, 13, Article 20769. https://doi.org/10.1038/s41598-023-47380-6
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. https://doi.org/10.1016/j.futures.2017.03.006
National Statistical Institute of Bulgaria (2023). Statistical Yearbook 2023. https://www.nsi.bg/en/publications/statistical-yearbook-2023-2225
National Statistical Institute of Bulgaria. (2024). Gross Domestic Product by production method. Sofia: NSI. Retrieved September 20, 2025, from https://www.nsi.bg
OECD. (2023). AI in Science: Challenges and Opportunities. Paris: OECD Publishing. https://doi.org/10.1787/aa3d5c64-en
OECD. (2025). Empowering the Workforce in the Context of a Skills-First Approach. OECD Skills Studies. OECD Publishing. https://doi.org/10.1787/345b6528-en
Paschen, J., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004
Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 96–114.
Rennings, K. (2000). Redefining innovation — eco-innovation research and the contribution from ecological economics. Ecological Economics, 32(2), 319–332. https://doi.org/10.1016/S0921-8009(99)00112-3
Wageman, R., & Hackman, J. R. (2010). Leading teams: Setting the stage for great performances. Boston, MA: Harvard Business School Press.
World Bank. (2024). World Development Indicators. Washington, DC: World Bank. Retrieved September 20, 2025, from https://data.worldbank.org
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006
Zeng, D., Chen, H., Lusch, R., & Li, S. H. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13–16. https://doi.org/10.1109/MIS.2010.151
Zhang, L., Gan, T., & Fan, J. (2023). Do industrial robots affect the labour market? Evidence from China. Economics of Transition and Institutional Change, 31(3), 787–817. https://doi.org/10.1111/ecot.12356
Zhang, X., Yu, Q., Liu, J., He, Y., & Xu, A. (2024). The application of industrial robots and changes in capital–labour income disparity in China. Economic Analysis and Policy, 84, 42–56. https://doi.org/10.1016/j.eap.2024.08.020
Zhao, C., Zhu, Z., Wang, Y., & Du, J. (2024). The impact of industrial robots on green total factor energy efficiency: Empirical evidence from Chinese cities. Energies, 17(20), 5034. https://doi.org/10.3390/en17205034
Zhou, K., Liu, T., & Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2147–2152. https://doi.org/10.1109/FSKD.2015.7382284
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Economics Ecology Socium

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
If the article is accepted for publication in the journal «Economics. Ecology. Socium» the author must sign an agreementon transfer of copyright. The agreement is sent to the postal (original) or e-mail address (scanned copy) of the journal editions.



















