Conference Agenda

One of the main problems of Total Productive Maintenance (TPM) is its superficial implementation, focused on basic routines rather than on data analysis, resulting in limited improvements in equipment availability and Overall Equipment Effectiveness (OEE), and a strong dependence on the human factor. For this reason, a systematic literature review of 70 studies was conducted, using Scopus, Web of Science, SciELO, Dialnet, and Google Scholar as primary databases, in order to rigorously analyze how the integration of Industry 4.0 technologies, such as Artificial Intelligence, IoT, and digital twins, strengthens Total Productive Maintenance (TPM) through the measurement, prediction, and optimization of equipment performance. Using the PRISMA methodology, recent scientific evidence from 2020 to 2025 is synthesized, demonstrating that the incorporation of data analytics, neural networks, digital twins, and IoT enables a transition from reactive maintenance to predictive and intelligent maintenance. The study identifies critical gaps related to standardization, organizational change management, and the scarcity of failure data, providing clear guidelines for future implementations and applied research lines. Finally, key aspects that should be strengthened and those requiring improvement are discussed in order to achieve an effective integration of AI, sensor-based systems, and risk models that support more accurate, culturally viable, and sustainable asset management over time.