Detalhes do Documento

Accurately estimating the hours required for maintenance, repair and overhaul (MRO) operations in the aviation sector frequently depends on the experience and personal judgment of engineers, can lead to introducing errors, increased operating costs, and time-consuming decision-making. This work presents the development of a cost-effective application to monitor and predict MRO operations in an aeronautical company. The application integrates data-driven algorithms, particularly Machine Learning (ML), with Power BI to provide a dynamic and user-friendly visualisation of historical and predicted data, improving decisionmaking time and facilitating operational planning. The simple linear regression model was the most effective algorithm to predict MRO operation for the case study with a B? of 0.81, balancing simplicity and performance compared to other analysed models.