Study of Students' Educational Needs and Competencies Bachelor of Engineering Program in Industrial Engineering

Authors

  • Theerapong Borirak School of Engineering, Eastern Asia University
  • Jarungrat Pansuwan School of Engineering, Eastern Asia University
  • Chamnarn Thongmark School of Engineering, Eastern Asia University
  • Thitikorn Maimun School of Engineering, Eastern Asia University
  • Yaowapha Pathomsirikul Doctor of Business Administration in Marketing, Faculty of Business Administration, Eastern Asia University

Keywords:

Industrial Engineering, Industry 4.0, Engineering Competencies, Decision-Making Factors, Digital Skills

Abstract

This article investigates the factors influencing students’ decisions to pursue further studies in Industrial Engineering (IE), the evolving role of industrial engineers in the context of Industry 4.0, and the competencies required to support competency-based curriculum design under an Outcome-Based Education (OBE) framework. The results indicate that students’ decision-making is influenced by three primary dimensions: individual factors, career-related motivations, and social influences. These factors, together with institutional quality and reputation, significantly affect students’ intentions to continue their studies in IE programs. The study highlights the expanding role of industrial engineers in integrating core Industry 4.0 technologies—including the Internet of Things (IoT), Cyber-Physical Systems (CPS), Artificial Intelligence (AI), Big Data analytics, Cloud Computing, and Robotics—into modern manufacturing systems. This transformation introduces challenges related to cybersecurity, investment costs, and organizational readiness, which must be addressed through appropriate competency development. The required competencies for industrial engineers are synthesized into three domains: technical, managerial, and human competencies. Key competencies include data-driven decision-making, automation and digital system integration, systems thinking, communication, and adaptability, which align with expected learning outcomes in OBE-oriented engineering programs. Insights from industry stakeholders indicate a strong expectation for industrial engineering graduates to be work-ready upon entry into the workforce, revealing competency gaps that motivate some graduates to pursue postgraduate education to enhance specialized skills. The findings emphasize the necessity of aligning curriculum design, learning outcomes, and assessment methods with industry-driven competencies through a competency-based and OBE-oriented approach. Strengthened collaboration between higher education institutions and industry is therefore recommended to ensure the relevance and effectiveness of industrial engineering education in the digital era.

References

Alkhamaiesh, S., & Cavanaugh, P. F. (2024). Training electric vehicle technicians in the u.s.a for the transition to electric vehicles: A literature review of the bipartisan infrastructure law implementation. paper presented at 2024 Asee north east section Fairfield, Connecticut: American society for engineering education. doi: 10.18260/1-2—45785

Alkhamaiesh, A., & Cavanaugh, J. (2024). Competency requirements for industrial engineers in the era of Industry 4.0. IEEE transactions on education, 67(1), 45–54.

Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education. (3rd ed.). Chicago, IL, USA: University of Chicago Press.

Biggs, J., & Tang, C. (2011). Teaching for quality learning at university. (4th ed.). Maidenhead, U.K.: McGraw-Hill Education.

Chen, Y., & Tai, H. (2021). Self-perceived competence and decision to pursue graduate education among engineering students. Studies in higher education, 46(9), 1875–1890.

Cobo, C. (2013). Mechanisms to identify and study the demand for innovation skills in world-renowned organizations. On the horizon, 21(2), 96-106.

Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for Industrie 4.0 scenarios. In Proceedings of the 49th Hawaii International Conference on System Sciences.

Inta, M. (2019). Soft Skills: The essential skills to beprofessionalism of the modern teachers.

Srinakharinwirot University journal of education, 20(1), 153–167. (in Thai)

Jackson, D. (2013). Business graduate employability: Where are we going wrong?. Higher education research & development, 32(5), 776-790.

Johnson, M., & Lee, Y. (2022). Bridging competency gaps between engineering graduates and industry

expectations. Journal of engineering education, 111(3), 612–630.

Kanokwan, K., Limkhajondech, P., & Laoraksakiat, R. (2003). Identification of factor affected on the

pursuit of bachelor degree program in industrial engineering. The engineering journal of

research and development, 25(2), 19–30. (in Thai)

Ketter, P. (2011). Soft skills are must-haves in future workplace. T & D, 65(9), 10–10.

Kitthao, S. (2003). Analysis and development of a cooperative education system for the

industrial engineering curriculum by applying QFD technique.

Pathum Thani: Rajamangala University of Technology Thanyaburi. (in Thai)

Kriangsinyos, O. (2023). Expectations and perceptions of undergraduate student in faculty of engineering towards educational management of King Mongkut’s University of Technology North Bangkok and King Mongkut’s University of Technology Thonburi. Journal of academic for public and private management, 5(2), 44–59. Doi: https://doi.org/10.14456/jappm.2023.19

Maimun, T., Thongmark, C., Rattanatai, B., & Hemvipat, K. (2022). Examining the movement of industry 4.0 and survival in the Covid-19 Era. EAU Heritage journal science and technology (online), 16(1), 37–55.

Phokanon, W. (2003). Application of quality function deployment technique in the design and development of the industrial engineering curriculum at Chulalongkorn University. Bangkok: Chulalongkorn University Intellectual Repository. Doi: https://doi.org/10.58837/CHULA.THE.2003.1422 (in Thai)

Quintero, W. R. (2022). Digital competences of the industrial engineer in Industry 4.0: A systematic vision. Production, 32, e20220028.

Quintero, W. R., & Maldonado, J. E. N. (2024). Competencies of the engineer in industry 4.0 context: A systematic literature review. Production, 34, e20230051.

Doi: https://doi.org/10.1590/0103-6513.20230051

Smith, J., Johnson, L., & Lee, K. (2022). A comprehensive review of cross-validation techniques in

machine learning model evaluation. Journal of machine learning research, 15, 123–145.

Tan, L., Lee Kong, T., Zhang, Z., Metwally, A. S. M., Sharma, S., Sharma, K. P., Sayed, M., & Zimon, E. D.

(2023). Scheduling and controlling production in an internet of things environment for industry 4.0:

An analysis and systematic review of scientific metrological data. Sustainability, 15, 7600. Doi: https://doi.org/10.3390/su15097600.

Thairat. (2017). The Modern University should be produce people to keep up the world. Retrieved from https://www.thairath.co.th/content/1143425 (in Thai)

Wang, N., Chen, J. W., & Tai, M. (2021). Blended learning for Chinese university EFL learners: Learning

environment and learner perceptions. Computer assisted language learning, 34(3), 297–323.

Doi: https://doi.org/10.1080/09588221.2019.1607881

Wang, S., & Li, D. (2021). Industrial engineering education for Industry 4.0. International journal of

engineering education, 37(2), 452–463.

Yorke, L. (2006). Employability in higher education: What It Is–What It Is Not. New York, U.K.: Higher

Education Academy.

Zhang, Z., Wang, Z. Y., & Li, Y. (2021). A review of research on quality development of China’s regional

economy. Resource development & market, 8, 928–933.

Downloads

Published

2026-02-19

Issue

Section

บทความวิชาการ