Application of data analytics to improve on-time delivery in project-based production planning in a Small and Medium-Sized Enterprise (SME) in the metal-mechanical industry dedicated to the manufacturing of industrial silos
DOI:
https://doi.org/10.33448/rsd-v15i4.50931Keywords:
Digitalization, Big Data, Production planning, Metal-mechanical SMEs, On-time delivery, Industrial Engineering.Abstract
Digital transformation has become a key factor in improving the competitiveness of small and medium-sized enterprises (SMEs) in the metal-mechanical sector. This study presents the application of a data analytics model focused on project-based production planning in an SME dedicated to the manufacturing of industrial silos and complementary components. The main objective was to improve the On-Time Delivery (OTD) rate through the use of accessible digital tools. The methodology was based on the analysis of simulated operational data corresponding to a six-month period, including variables related to production volume, workload, rework, logistics, and transportation availability. The predictive model was implemented in Google Colab using regression techniques to estimate the monthly performance of the production system. The results show that the multiple linear regression model achieved a coefficient of determination R² = 0.862, indicating strong explanatory capacity for the On-Time Delivery (OTD) indicator. The historical analysis showed an average compliance rate of 69.36%, while under an operational optimization scenario characterized by reduced rework, decreased finished goods accumulation, and improved transportation availability the projected OTD increased to 81.92%, representing an improvement of 12.56 percentage points. These findings highlight the potential of data analytics as a decision-support tool for production planning in metal-mechanical SMEs.
References
APICS. (2018). Supply chain operations reference model (SCOR). APICS.
Benítez Torres, S. G., & Barrezueta Avellán, F. F. (2023). Big Data y analítica predictiva en la toma de decisiones empresariales. Revista de Investigación y Gestión, 7(1), 35–46. https://editorialinnova.com/index.php/rig/article/view/103/314
Cevallos Guamán, E. J., Jacho Gallo, A. K., & Córdova Vaca, A. M. (2024). Big data y analítica predictiva en la toma de decisiones empresariales. Revista Ingenio Global, 4(1), 45–58.
Chávez, J. A., & Rodríguez, M. E. (2022). Big Data y su incidencia en la gestión de la cadena de suministro. Innovare: Revista de Ciencia y Tecnología, 11(1), 89–101. https://portalderevistas.uam.edu.ni/index.php/iyA/article/view/158/132
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883.
Cordoves Mustelier, D., & Frutos, M. (2024). Impacto y evolución del big data en la logística: Una revisión exhaustiva de tendencias y prácticas actuales. Revista Científica de Logística y Transporte, 6(2), 112–128.
Gómez, M. A., & Herrera, D. J. (2023). El uso del Big Data en la gestión de la cadena de suministro: Ventajas competitivas y desafíos para las organizaciones. Innovación y Argumentación, 4(2), 45–59. https://portalderevistas.uam.edu.ni/index.php/iyA/article/view/158/132
González-Morales, A., & Hernández-Ramírez, C. (2022). Transformación digital en PyMEs manufactureras mexicanas: Desafíos y oportunidades. Revista de Innovación y Tecnología, 8(2), 45–59.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S., Childe, S., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.
Instituto Nacional de Estadística y Geografía (INEGI). (2023). Censos Económicos 2023. Gobierno de México.
Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on supply chain resilience. International Journal of Production Research, 57(3), 829–846.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative Industrie 4.0. Acatech.
Marín, F., & Sciolla, M. (2023). Big Data como herramienta de mejora de la cadena de suministro. Universidad Nacional de La Plata. https://sedici.unlp.edu.ar/bitstream/handle/10915/177360/Documento_completo.pdf
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
OECD. (2019). OECD SME and entrepreneurship outlook 2019. OECD Publishing.
Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica. Santa Maria: UAB/NTE/UFSM.
Ramírez-Treviño, J., & Soto-López, D. (2023). Adopción de Big Data y digitalización en el sector metalmecánico mexicano. Revista de Ingeniería Industrial Latinoamericana, 12(1), 112–130.
Risemberg, R. I. C., Wakin, M., & Shitsuka, R. (2026). A importância da metodologia científica no desenvolvimento de artigos científicos. E-Acadêmica, 7(1), e0171675. https://doi.org/10.52076/eacad-v7i1.675
Romero, L. A. (2023). Big Data y transformación digital en la gestión de la cadena de suministro: Análisis de tendencias y aplicaciones en América Latina [Trabajo final de grado, Universidad Nacional de La Plata].
Salazar López, C. A. (2022). Implementación de analítica de datos y Big Data en la cadena de suministro para la optimización logística en empresas manufactureras peruanas [Tesis de maestría, Pontificia Universidad Católica del Perú]. Repositorio Institucional PUCP. https://tesis.pucp.edu.pe/server/api/core/bitstreams/9f855091-750e-422d-be5c-7ba08a0d8481/content
Slack, N., Brandon-Jones, A., & Johnston, R. (2016). Operations management (8th ed.). Pearson.
Vailos. (2023). Big Data en la cadena de suministro. https://www.vailos.com/big-data-en-la-cadena-de-suministro/
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors.
Computers & Industrial Engineering, 101, 572–591.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Andrik Erick Castillo Leyva, Francisca Santana Robles
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
