Document Type

Conference Paper

Publication Date

2026

DOI

10.18260/1-2-1125-58287

Publication Title

2026 CIEC

Pages

32 pp.

Conference Name

The 50th Conference for Industry and Education Collaboration (2026 CIEC), February 4-6, 2026, New Orleans, Louisiana

Abstract

In the evolving field of automation engineering, staying aligned with industry software demands is critical to preparing graduates for the modern workforce. This study investigates the prevalence of leading industrial automation platforms—Rockwell / Allen-Bradley (RSLogix / Studio 5000), Siemens (TIA Portal / Step 7), and Schneider Electric (EcoStruxure / Unity Pro)—across job postings collected from Indeed using the keyword "automation engineering." The research compiles a structured dataset of job postings with seven fields: ID, Job Title, Organization, Rockwell / Allen-Bradley, Siemens, Schneider Electric, and Posting URL. Each entry is manually coded to indicate whether the listed software platforms are mentioned, allowing for frequency analysis and comparative evaluation. Preliminary insights suggest that while all three platforms have broad relevance, employer preference may vary significantly by region, sector, and job role. These findings aim to guide engineering technology educators in aligning curricula, lab resources, and certification pathways with market expectations, particularly in associate and bachelor’s degree programs where hands-on software training is central to student outcomes. By surfacing real-time demand signals from live job markets, this study supports the Engineering Technology Division’s mission to foster continuous program improvement and strengthen connections between educational institutions and the industries they serve.

Rights

© 2026 American Society for Engineering Education, 2026 CIEC Conference proceedings (February 4-6, 2026, New Orleans, Louisiana).

Original Publication Citation

Phan, T., Okafor, C., Okafor, W., Mehta, D., Souissi, M., Waterman, J., Mayberry, C., Thomas, M., Ayala, O., Price, A., & Manzo, M. (2026). Identifying industry-preferred automation software in engineering: An indeed-based analysis to inform engineering technology curriculum design [Conference paper]. 2026 CIEC, New Orleans, Louisiana. https://doi.org/10.18260/1-2-1125-58287

ORCID

0000-0003-0604-8606 (Ayala)

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