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Industry 4.0 (I4. 0) provides connectivity, data volume, new devices, miniaturization, inventory reduction, personalization, and controlled production. In this new era, production customization and data availability are essential to generate information that allows decision-making. The possibility of predicting the need for maintenance in the future and using this information for other processes is one of the manufacturing process challenges. In this context, this thesis proposal transcends the specific fact of applying predictive maintenance (PdM) and suggests ways to integrate processes, focusing on maintenance and production schedules. The objective is to create the Predictive Maintenance & Schedule (PdMS) to integrate maintenance and production schedules in a predictive way. At each sensor data reading and operational information, the machine’s remaining useful life (RUL) is predicted, deciding whether the machine will be part of the production process or not. Reinforcing that, this new Industry scenario allows Computing Applications, together with artificial intelligence and distributed computing, to become more effective in manufacturing processes. With the PdMS creation, the idea is to reduce downtime, improve communication between the maintenance and production sectors and allow future integration with the production, storage, and logistics sectors. The PdMS creation process was divided into two phases:(i) related to PdM, which describes to create and combine degradation indices using similarity patterns and application Savitzky-Golay and Kalman smoothing filters that allow noisy data to …