Digital application to support decision-making for the safe management of work at height in a cement company
DOI:
https://doi.org/10.63728/riisds.v11i1.331Keywords:
App, Digital tool, Occupational health and safety, Working at heightAbstract
This study was conducted at a young Mexican company dedicated to the extraction of raw materials and the manufacture of cement, mortar, and white cement. Performing work at heights at the company represents a high level of risk, requiring rigorous evaluation processes and manual authorizations. This process generates operational delays and hinders the efficiency of decision-making. The objective of this work is to create and implement a digital application that facilitates decision-making in the safe management of work at heights at a Mexican cement company. The theoretical framework was based on the regulatory requirements established in NOM-009-STPS-2011 and the ANSI Z359.2-2011 standard. The study was developed using an observational and descriptive methodological approach, considering a population of 170 workers through direct observation of their activities at the plant. Based on the information obtained, a digital application was designed using Microsoft Power Apps integrated with SharePoint and Power BI. The results demonstrated a 51.9% reduction in the time required for safety evaluations and authorizations. In conclusion, the implementation of digital tools contributes significantly to operational management and improved decision-making in high-risk prevention in industrial environments.
Downloads
References
Ammad, S., Mostafa, S., & Stewart, R. A. (2025). Development of a safety-oriented framework for fall prevention in construction projects using smart PLS-SEM analysis. Journal of Safety and Sustainability, 2(4), 268–284.
https://doi.org/10.1016/j.jsasus.2025.10.004
American National Standards Institute. (2011). ANSI Z359.2-2011: Minimum requirements for a comprehensive managed fall protection program (American National Standard). ANSI.
Bojorquez-Delgado, G., Bojorquez-Delgado, J., & Flores-Rosales, M. A. (2022). Diseño de un sistema inteligente para inspeccionar el flujo de información de una red Modbus mediante lógica difusa en sistemas agroindustriales. Revista Interdisciplinaria de Ingeniería Sustentable y Desarrollo Social, 8(1), 77–89.
https://doi.org/10.63728/riisds.v8i1.152
Cagno, E., Accordini, D., Neri, A., Negri, E., & Macchi, M. (2024). Digital solutions for workplace safety: An empirical study on their adoption in Italian metalworking SMEs. Safety Science, 177, 106598. https://doi.org/10.1016/j.ssci.2024.106598
Castillo-Bolaños, B. N., Esquivel-Rodríguez, J., Castillo-Castillo, O., & Guillen-Reyes, D. N. (2025). Diseño, fabricación e implementación de un dashboard en líneas de producción de la industria automotriz. Revista Interdisciplinaria de Ingeniería Sustentable y Desarrollo Social, 11(1), 335–347.
https://doi.org/10.63728/riisds.v11i1.335
Gómez-García, M., Gómez-Macias, M. G., Salas-Cabrera, R., García-Reyes, L. A., Esquivel-Rodríguez, J., & Romero-Treviño, J. A. (2024). Diseño de un sistema automatizado para optimizar el cultivo de trucha arcoíris en poblaciones de alta marginación tamaulipecas. Revista Interdisciplinaria de Ingeniería Sustentable y Desarrollo Social, 10(1), 582–601. https://doi.org/10.63728/riisds.v10i1.67
Guglielmi, A., Leva, A., Pellicci, M., Gnoni, M. G., & Tornese, F. (2025). The value of structured occupational safety data and cluster analysis: A case study from the Italian National Surveillance System. Journal of Safety Science and Resilience, 6, 100210. https://doi.org/10.1016/j.jnlssr.2025.03.003
Guo, H., Mo, Y., Guo, F., Kang, R., Tang, K., & Ma, Q. (2025). Association analysis of causative factors of fall from height accidents. Journal of Safety Science and Resilience, 6, 100221. https://doi.org/10.1016/j.jnlssr.2025.100221
Hernández Sampieri, R., Fernández Collado, C., & Baptista Lucio, P. (2018). Metodología de la investigación (6.ª ed.). McGraw-Hill Education.
Ibrahim, Y., Jad, S., JuHyeong, R., & Ramy, H. (2024). Safety 4.0: Harnessing computer vision for advanced industrial protection. Manufacturing Letters, 41, 1342–1356. https://doi.org/10.1016/j.mfglet.2024.09.161
Karatas, I. (2025). Deep learning-based system for prediction of work at height in construction site. Heliyon, 11, e41779. https://doi.org/10.1016/j.heliyon.2025.e41779
Liu, Y., Zhang, J., Shi, L., Huang, M., Lin, L., Zhu, L., Lin, X., & Zhang, C. (2024). Detection method of the seat belt for workers at height based on UAV image and YOLO algorithm. Array, 22, 100340. https://doi.org/10.1016/j.array.2024.100340
Martínez-Corona, J. I., Palacios-Almón, G. E., & Oliva-Garza, D. B. (2023). Guía para la revisión y el análisis documental: propuesta desde el enfoque investigativo. Ra Ximhai, 19(1), 67–83. https://doi.org/10.35197/rx.19.01.2023.03.jm
Min, J., Kim, Y., Lee, S., Jang, T.-W., Kim, I., & Song, J. (2019). The Fourth Industrial Revolution and its Impact on Ocupational Health and Safety, Worker's Compensation and Labor Conditions. Safety and Health at Work, 10, 400-408. https://doi.org/10.1016/j.shaw.2019.09.005
Pasman, H., & Behie, S. W. (2024). Safety 5.0: Safety management issues in sustainable, human-centric, and resilient industrial systems. Journal of Safety and Sustainability. https://doi.org/10.1016/j.jsasus.2024.11.003
Peng, J. L., Liu, X., Peng, C., & Shao, Y. (2023). Comprehensive factor analysis and risk quantification study of fall from height accidents. Heliyon, 9, e22167.
https://doi.org/10.1016/j.heliyon.2023.e22167
Santillan-Valdelamar, M. G., Dimas-Díaz, F., Martínez-Corona, J. I., & Palacios-Almón, G. E. (2024). Documentary analysis on productivity in enterprises. DYNA, 91(233), 104–113. https://doi.org/10.15446/dyna.v91n233.114104
Secretaría del Trabajo y Previsión Social. (2011). Norma Oficial Mexicana NOM-009-STPS-2011, condiciones de seguridad para realizar trabajos en altura. https://dof.gob.mx/normasOficiales/4377/stps/stps.htm
Son, S., Na, Y., & Han, B. (2024). Assessment of risk priorities by cause of construction safety accidents: A case study of falling accidents in South Korea. Heliyon, 10(23), 1-9, Article e40303. https://doi.org/10.1016/j.heliyon.2024.e40303
Zermane, A., Mohd Tohir, M. Z., Zermane, H., Baharudin, M. R., & Mohamed Yusoff, H. (2023). Predicting fatal fall from heights accidents using random forest classification machine learning model. Safety Science, 159, 106023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jonathan Daniel Estrada-Barrera, Juan Patricio Trejo-Mendoza, Alejandro Gálvez-Mendoza

This work is licensed under a Creative Commons Attribution 4.0 International License.
Derechos de autor y cesión no exclusiva
De conformidad con la política editorial y con lo establecido en los artículos publicados, las personas autoras que publican en RIISDS:
-
Conservan íntegramente los derechos morales y patrimoniales de sus obras.
-
Otorgan a la revista RIISDS un derecho no exclusivo de primera publicación, necesario para la difusión científica.
-
Aceptan que su obra sea difundida bajo la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0).
Las personas autoras conservan permanentemente:
-
El derecho de reconocimiento de autoría.
-
El derecho a la integridad de la obra.
-
El derecho a ser citados correctamente en cualquier reutilización.
-
Mantienen la titularidad total de los derechos patrimoniales.
-
Permiten, mediante CC BY 4.0, la reproducción, distribución, comunicación pública y transformación de la obra.
RIISDS:
-
Ejerce únicamente el derecho de primera publicación.
-
No reclama exclusividad ni restringe el uso posterior del artículo.
-
No impone embargos ni limitaciones adicionales.