325 • Prognostic Integration: Integrating sensors and diagnostic tests into a cohesive "Strategic Maintenance Model" aims to extend the Mean Time Between Failure (MTBF). 5.4.1 Contribution to Operational Innovation The project drives innovation through: • Model-Based Intelligence: Moving beyond simple monitoring to intelligent diagnosis and prognosis based on advanced models. • OEM Knowledge Leverage: Utilizing original equipment manufacturer (OEM) expertise combined with big data to create unique analytical insights. • Tiered Solutions: Offering four distinct levels of data access and monitoring services allows for customized innovation based on specific customer maturity levels 5.4.2 Recommendations Based on Observed Outcomes • Eliminate Free Solutions: To maintain the value proposition, it is recommended to restrict free data solutions that compete with defined REM offerings. • Continuous Analytics Expansion: Regularly catalogue and evaluate existing data solutions to eliminate, consolidate, or optimize them. • Global Standardization: Implement global list pricing and baseline standards across all business units to ensure transparency and enterprise-wide alignment. REFERENCES Aguirre-Jofré, H., Eyre, M., Valerio, S., & Vogt, D. (2021). Low-cost internet of things (IoT) for monitoring and optimising mining small-scale trucks and surface mining shovels. Automation in Construction, 131, 103918. https://doi.org/https://doi.org/10.1016/j.autcon.2021.103918 Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges. Sensors, 13(12), 17472–17500. https://doi.org/10.3390/s131217472 Chaowasakoo, P., Seppälä, H., Koivo, H., & Zhou, Q. (2017). Digitalization of mine operations: Scenarios to benefit in real-time truck dispatching. International Journal of Mining Science and Technology, 27(2), 229–236. https://doi.org/10.1016/j.ijmst.2017.01.007 Cubas Glen, G. (2022). Transformación Digital para el soporte remoto de equipos de gran minería (EHM) [Thesis]. Universidad Internacional de la Rioja (Mexico). Desmond, J., & Karadjian, G. (2025). Alignment Workshop Report Out. Göger, S., Darol, E. S., & Uzun, S. (2026). Multiple screen addiction and neurological complaints in adolescents: A machine learning-based classification model. Acta Psychologica, 264, 106491. https://doi.org/https://doi.org/10.1016/j.actpsy.2026.106491 Grobis, M., Solterbeck, S., Kempf, O., & Ihlenfeldt, S. (2025). Classification and Evaluation of the Integration of Process Mining and Digital Twins: A Roadmap. Procedia CIRP, 134, 467–472. https://doi.org/https://doi.org/10.1016/j.procir.2025.02.155
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