End-To-End Automation Maturity Models and Workforce Impact

Authors

  • Yasser Gharib Abdelaziz Elgeddawy Organization Design and Transformation Consultant, Workforce and Manpower planning Expert, BSC Pharmaceutical Science, Jeddah, Kingdom of Saudi Arabia Author

DOI:

https://doi.org/10.32628/IJSRST2613117

Keywords:

End-to-end automation, Automation maturity models, Digital transformation, Industry, Workforce impact

Abstract

The swift progress of the digital transformation speeded up the process of end-to-end automation implementation in industries that transformed the operations of the organization, decision-making, and workforce relationships. Automation maturity models have become valuable tools in evaluating and advising organizations to follow the steps of automation progressively, especially the fields of Industry 4.0, smart manufacturing, DevOps and hyperautomation. Although the current models of maturity offer systematic ways of integrating technology and processes, the implication of it on the workforce is still broken and in many cases immature. This paper discusses models of end-to-end automation maturity with a closer consideration on its influence over the workforce. The study examines the effects of the various degrees of automation maturity on the job description, skills needed, human-technology interface, and employee experience through reviewing existing automation, digital transformation, and Industry 4.0 maturity models. These results point to an increasing disconnect between technology and human-focused factors and the necessity to incorporate the aspect of workforce (including reskilling, ergonomics, organizational culture, and job satisfaction) into maturity measurements. The article would add to the literature by summarizing the perspectives on automation maturity and suggesting a more holistic perspective that would bring end-to-end automation advancement to sustainable workforce development.

Downloads

Download data is not yet available.

References

Hetmanczyk, M. P. (2024). A Method for Evaluating the Maturity Level of Production Process Automation in the Context of Digital Transformation—Polish Case Study. Applied Sciences, 14(11), 4380. https://doi.org/10.3390/app14114380 DOI: https://doi.org/10.3390/app14114380

Gajdzik, B. (2022). Frameworks of the maturity model for industry 4.0 with assessment of maturity levels on the example of the segment of steel enterprises in Poland. Journal of Open Innovation: Technology, Market, and Complexity, 8(2), 77. https://doi.org/10.3390/joitmc8020077 DOI: https://doi.org/10.3390/joitmc8020077

Babar, Z. (2024). A study of business process automation with DevOps: A data-driven approach to agile technical support. American Journal of Advanced Technology and Engineering Solutions, 4(04), 01-32. https://doi.org/10.63125/3w5cjn27 DOI: https://doi.org/10.63125/3w5cjn27

George, A. S., George, A. H., Baskar, T., & Sujatha, V. (2023). The rise of hyperautomation: a new frontier for business process automation. Partners Universal International Research Journal, 2(4), 13-35. https://doi.org/10.5281/zenodo.10403036

Gökalp, E., & Martinez, V. (2022). Digital transformation maturity assessment: development of the digital transformation capability maturity model. International Journal of Production Research, 60(20), 6282-6302. https://doi.org/10.1080/00207543.2021.1991020 DOI: https://doi.org/10.1080/00207543.2021.1991020

Vance, D., Jin, M., Price, C., Nimbalkar, S. U., & Wenning, T. (2023). Smart manufacturing maturity models and their applicability: a review. Journal of Manufacturing Technology Management, 34(5), 735-770. https://doi.org/10.1108/JMTM-03-2022-0103 DOI: https://doi.org/10.1108/JMTM-03-2022-0103

Gunsberg, D., Callow, B., Ryan, B., Suthers, J., Baker, P. A., & Richardson, J. (2018). Applying an organisational agility maturity model. Journal of Organizational Change Management, 31(6), 1315-1343. https://doi.org/10.1108/JOCM-10-2017-0398 DOI: https://doi.org/10.1108/JOCM-10-2017-0398

Perera, S., Jin, X., Das, P., Gunasekara, K., & Samaratunga, M. (2023). A strategic framework for digital maturity of design and construction through a systematic review and application. Journal of Industrial Information Integration, 31, 100413. https://doi.org/10.1016/j.jii.2022.100413 DOI: https://doi.org/10.1016/j.jii.2022.100413

Weerabahu, W. S. K., Samaranayake, P., Nakandala, D., & Hurriyet, H. (2023). Digital supply chain research trends: a systematic review and a maturity model for adoption. Benchmarking: An International Journal, 30(9), 3040-3066. https://doi.org/10.1108/BIJ-12-2021-0782 DOI: https://doi.org/10.1108/BIJ-12-2021-0782

Hujran, O., Alarabiat, A., & AlSuwaidi, M. (2023). Analysing e-government maturity models. Electronic Government, an International Journal, 19(1), 1-21. https://doi.org/10.1504/EG.2023.127575 DOI: https://doi.org/10.1504/EG.2023.127575

Treviño-Elizondo, B. L., García-Reyes, H., & Peimbert-García, R. E. (2023). A maturity model to become a Smart Organization based on lean and Industry 4.0 synergy. Sustainability, 15(17), 13151. https://doi.org/10.3390/su151713151 DOI: https://doi.org/10.3390/su151713151

Ghaderi, H. (2020). Wider implications of autonomous vessels for the maritime industry: Mapping the unprecedented challenges. In Advances in transport policy and planning (Vol. 5, pp. 263-289). Academic Press. https://doi.org/10.1016/bs.atpp.2020.05.002 DOI: https://doi.org/10.1016/bs.atpp.2020.05.002

Saad, S. M., Bahadori, R., & Jafarnejad, H. (2021). The smart SME technology readiness assessment methodology in the context of industry 4.0. Journal of Manufacturing Technology Management, 32(5), 1037-1065. https://doi.org/10.1108/JMTM-07-2020-0267 DOI: https://doi.org/10.1108/JMTM-07-2020-0267

Çınar, Z. M., Zeeshan, Q., & Korhan, O. (2021). A framework for industry 4.0 readiness and maturity of smart manufacturing enterprises: a case study. Sustainability, 13(12), 6659. https://doi.org/10.3390/su13126659 DOI: https://doi.org/10.3390/su13126659

Elibal, K., & Özceylan, E. (2024). An industry 4.0 maturity model proposal based on total quality management principles: an application to an automotive parts manufacturer. IEEE Transactions on Engineering Management, 71, 10815-10832. https://dx.doi.org/10.2139/ssrn.4457997 DOI: https://doi.org/10.1109/TEM.2024.3397555

Teixeira, D., Pereira, R., Henriques, T., Silva, M. M. D., Faustino, J., & Silva, M. (2020). A maturity model for DevOps. International Journal of Agile Systems and Management, 13(4), 464-511. https://doi.org/10.1504/IJASM.2020.112343 DOI: https://doi.org/10.1504/IJASM.2020.112343

Lin, T. C., Sheng, M. L., & Jeng Wang, K. (2020). Dynamic capabilities for smart manufacturing transformation by manufacturing enterprises. Asian Journal of Technology Innovation, 28(3), 403-426. https://doi.org/10.1080/19761597.2020.1769486 DOI: https://doi.org/10.1080/19761597.2020.1769486

Reiman, A., Kaivo-Oja, J., Parviainen, E., Takala, E. P., & Lauraeus, T. (2021). Human factors and ergonomics in manufacturing in the industry 4.0 context–A scoping review. Technology in Society, 65, 101572. https://doi.org/10.1016/j.techsoc.2021.101572 DOI: https://doi.org/10.1016/j.techsoc.2021.101572

Hemon-Hildgen, A., Rowe, F., & Monnier-Senicourt, L. (2020). Orchestrating automation and sharing in DevOps teams: a revelatory case of job satisfaction factors, risk and work conditions. European journal of information systems, 29(5), 474-499. https://doi.org/10.1080/0960085X.2020.1782276 DOI: https://doi.org/10.1080/0960085X.2020.1782276

Downloads

Published

25-01-2026

Issue

Section

Research Articles

How to Cite

[1]
Yasser Gharib Abdelaziz Elgeddawy, Tran., “End-To-End Automation Maturity Models and Workforce Impact”, Int J Sci Res Sci & Technol, vol. 13, no. 1, pp. 136–147, Jan. 2026, doi: 10.32628/IJSRST2613117.