Intercambiabilidad y canibalización en mantenimiento industrial: perspectivas para gestión de activos con machine learning, big data e inteligencia artificial
DOI:
https://doi.org/10.24054/rcta.v2i48.4514Palabras clave:
big data, canibalización, gestión de activos, intercambiabilidad, inteligencia artificial, mantenimiento industrialResumen
El presente estudio constituye un capítulo de reflexión sobre las técnicas de intercambiabilidad y canibalización en el mantenimiento industrial, examinando su implementación en la gestión de activos y su correlación con el Big Data y la Inteligencia Artificial. Mediante un enfoque metodológico cualitativo-descriptivo documental, se analizaron 43 fuentes académicas, priorizando literatura del período 2020-2025 y complementando con referencias seminales que sustentan modelos matemáticos y normativos de base (EOQ, RCM, taxonomía de activos, punto de reorden). Los resultados evidencian que la intercambiabilidad reduce costos de mantenimiento entre 25-30% y disminuye intervenciones correctivas hasta en 70%. La integración de sistemas CMMS con IA demostró precisiones superiores al 98% en la predicción de vida útil residual (RUL). La canibalización, aplicada con modelos predictivos basados en Big Data, mantiene disponibilidades operativas superiores al 90%. Los algoritmos LSTM y Random Forest optimizan inventarios de repuestos y la programación preventiva. Se concluye que la articulación de ambas estrategias con tecnologías emergentes maximiza el valor de los activos e incrementa la rentabilidad empresarial.
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Derechos de autor 2026 Juan David Bermúdez Royero
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
