Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria

Godspower Onyekachukwu Ekwueme

doi:10.59890/ijsr.v3i5.160

Authors

Godspower Onyekachukwu Ekwueme Industrial and Production Engineering Department, Faculty of Engineering in Nnamdi Azikiwe University, Awka, Anambra State

DOI:

https://doi.org/10.59890/ijsr.v3i5.160

Keywords:

Artificial Intelligence, Engineers, Manufacturing Firms

Abstract

This study examined “Efficiency of Artificial Intelligence among Engineers in Manufacturing Firms in Nigeria”. Methodology: Relevant data were drawn from selected journals, textbooks. Qualitative content analysis was used to analyze the data. Study conclusion and policy recommendations: The study concluded that the efficiency of artificial intelligence (AI) among engineers in manufacturing firms in Nigeria reflects both promising potential and significant challenges. Empirical evidence suggests that AI technologies, when effectively adopted, enhance productivity, accuracy, and task performance by automating routine engineering processes, enabling real-time monitoring, and improving data-driven decision-making. In firms with sufficient infrastructure and skilled personnel, AI integration has already resulted in measurable gains, such as reduced downtime, faster maintenance cycles, and improved product quality. Finally, the study recommends that to enhance the efficiency of artificial intelligence among engineers in manufacturing firms in Nigeria, it is recommended that firms invest in continuous training and capacity-building programs to equip engineers with the necessary AI-related technical skills. Government and private sector collaboration should be strengthened to improve digital infrastructure, such as stable power supply and high-speed internet, which are critical for the smooth operation of artificial intelligence systems.

References

Akinola, A. (2023). Challenges of AI adoption in the manufacturing industry in Nigeria. International Journal of Research and Innovation in Social Science.

Amaugo, O. (2024). Impact of AI adoption on business process automation and competitiveness in the manufacturing industry in Nigeria. International Journal of Research and Innovation in Social Science, 8(3), 5321‑5330.

Amaugo, O. (2024). Impact of AI technologies in optimizing manufacturing processes in the manufacturing industry in Nigeria. Texila Journal, 12(1).

Audu, Y. P., & Ihuoma, A. N. (2025). Artificial intelligence and the performance of manufacturing firms in North‑Central Nigeria: Reward system as the moderator. International Journal of Research and Innovation in Social Science.

Automotive Manufacturing Solutions. (2024). *Future of predictive maintenance, robots, quality control & digital twins using AI.

Chas Hamel, Md Manjurul Ahsan, & Shivakumar Raman. (2024). PMI‑DT: Leveraging Digital Twins and Machine Learning for Predictive Modelling and Inspection in Manufacturing.

Chukwuka, E. J., & Imide, I. O. (2025). Impact of Artificial Intelligence on employees’ productivity in technological organizations in Delta State, Nigeria. Jalingo Journal of Social and Management Sciences, 6 (3).

Disciplines.ng. (2025). Systems Engineering Tools and Software Used in Nigeria. ([disciplines.ng][8])

Dongre, N., Pandey, A., & Gupta, O. P. (2020). Artificial intelligence in accounting: opportunities & challenges. J. Xi’an Univ. Archit. Technol, 12, 1858-1864.

Enwereji, N. A., Ohamobi, I. N., & Nwokeji, E. (2022). Assessing the relationship between the utilization of computers and the academic achievement of postgraduate students in degree-awarding institutions in Anambra State. European Journal of Research and Reflection in Educational Sciences, 10(1), 2.

Ezeribe, C. (2019). Artificial Intelligence (AI) and the Accountancy Profession: The Threats of Obsolescence.

Lee, J., Kim, T., Jeon, S.‑H., Park, E.‑H., Kim, S., Lee, E.‑H., & Lee, J. (2023). Automation of trimming die design inspection by the zigzag process between AI and CAD domains.

Malawade, A. V., Costa, N. D., Muthirayan, D., Khargonekar, P. P., & Al Faruque, M. A. (2021). Neuroscience‑Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems.

Manafa, I. F., Ohamobi, I. N., & Osegbue, G. C. (2022). Utilization of ICT resources in the management of UBE in secondary schools in Anambra State. African Journal of Educational Management, Teaching and Entrepreneurship, 6.

NetSuite. (2025). 16 Applications of Machine Learning in Manufacturing in 2025.

Octal-Software. (2025). Machine Learning in Manufacturing: Key Use Cases and Benefits.

Odoh, L. C., Echefu, S. C., Ugwuanyi, U. B., & Chukwuani, N. V. (2018). Effect of artificial intelligence on the performance of accounting operations among accounting firms in South East Nigeria. Asian Journal of Economics, Business and Accounting, 7(2), 1-11.

Osegbue, G. C., Ohamobi, I. N., Ekwe, N. I., & Alordiah, C. O. (2025). AI-enhanced crisis management in schools. Nigerian Journal of Social Psychology, 8(1), 3.

PwC Nigeria. (2024). AI in Nigeria. PwC Nigeria.

Stancheva-Todorova, E. P. (2018). How artificial intelligence is challenging the accounting profession. Journal of International Scientific Publications, 12(1), 126-141.