Inteligencia artificial y gemelos digitales en el mantenimiento predictivo industrial: cartografía de la literatura latinoamericana

Artificial intelligence and digital twins in industrial predictive maintenance: mapping the Latin American literature

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Introducción: La inteligencia artificial (IA) y los gemelos digitales representan tecnologías clave dentro de la Industria 4.0, al transformar los sistemas tradicionales de mantenimiento hacia modelos predictivos basados en análisis de datos, optimización operativa y toma de decisiones automatizada. Objetivo: Analizar la estructura de la literatura científica sobre inteligencia artificial aplicada al mantenimiento predictivo industrial, identificando vacíos de conocimiento con implicaciones prácticas para la toma de decisiones de inversión industrial en América Latina durante el periodo 2022-2026. Metodología: Se realizó una revisión de alcance siguiendo las directrices PRISMA-ScR. La búsqueda incluyó estudios provenientes de Scopus, Web of Science, IEEE Xplore, SciELO, PubMed, Google Académico y repositorios institucionales. El corpus final estuvo conformado por 47 fuentes, distribuidas en 28 publicaciones académicas (59,6 %) y 19 documentos institucionales, corporativos o periodísticos (40,4 %). Resultados: Los hallazgos evidenciaron un campo de investigación fragmentado organizado en cuatro corrientes principales: desarrollo algorítmico, aplicación de gemelos digitales, revisión y adopción tecnológica, y análisis económico-normativo. Se identificó una limitada producción científica regional y ausencia de estudios que cuantifiquen de manera directa el retorno económico de la implementación de estas tecnologías en entornos industriales. Conclusiones: La literatura actual presenta avances significativos en el desempeño técnico del mantenimiento predictivo basado en IA, pero mantiene una brecha crítica entre la precisión tecnológica y la evaluación económica de su implementación. Se requieren investigaciones que integren indicadores predictivos con resultados financieros y operativos para orientar decisiones de inversión industrial en América Latina.
Background: Artificial intelligence (AI) and digital twins are key technologies within Industry 4.0, transforming traditional maintenance systems toward predictive models based on data analytics, operational optimization, and automated decision-making. Objective: To analyze the structure of the scientific literature on artificial intelligence applied to industrial predictive maintenance, identifying knowledge gaps with practical implications for industrial investment decision-making in Latin America during the 2022–2026 period. Methodology: A scoping review was conducted following the PRISMA-ScR guidelines. The search included studies from Scopus, Web of Science, IEEE Xplore, SciELO, PubMed, Google Scholar, and institutional repositories. The final corpus comprised 47 sources, including 28 academic publications (59.6%) and 19 institutional, corporate, or journalistic documents (40.4%). Results: The findings revealed a fragmented research field organized around four main streams: algorithmic development, digital twin applications, technology review and adoption, and economic and regulatory analysis. Limited regional scientific output was identified, along with a lack of studies directly quantifying the economic return on implementing these technologies in industrial settings. Conclusions: The current literature shows significant advances in the technical performance of AI-based predictive maintenance while maintaining a critical gap between technological accuracy and the economic assessment of implementation. Further research is needed to integrate predictive indicators with financial and operational outcomes to support industrial investment decisions in Latin America.

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Fernández Sánchez, D. G., Zúñiga Incalla, R. A., Barrientos-Quintanilla, K. P., & Escurra Vicésar, M. D. (2026). Inteligencia artificial y gemelos digitales en el mantenimiento predictivo industrial: cartografía de la literatura latinoamericana. Impulso, Revista De Administración, 6(15), 1-16. https://doi.org/10.59659/impulso.v.6i15.367
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Vol. 6 Núm. 15 (2026): Volumen 6 | Número 15 | Julio – Septiembre - 2026
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Artículos de Revisión
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

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Fernández Sánchez, D. G., Zúñiga Incalla, R. A., Barrientos-Quintanilla, K. P., & Escurra Vicésar, M. D. (2026). Inteligencia artificial y gemelos digitales en el mantenimiento predictivo industrial: cartografía de la literatura latinoamericana. Impulso, Revista De Administración, 6(15), 1-16. https://doi.org/10.59659/impulso.v.6i15.367

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