Prediction of overall equipment effectiveness using machine learning

This study emphasizes the importance of performance indicators such as Overall Equipment Effectiveness (OEE) used in the management of dynamic environments in industrial enterprises and focuses on predicting the future values of this indicator. The study aims to predict future OEE values using data obtained from CNC machines in an enterprise. In line with this goal, machine learning algorithms were used to analyse and predict various data collected from production and machines. In the study, production data from a real enterprise is used and evaluated with three different prediction algorithms. The results are intended to determine the most effective forecasting algorithm and provide guidance for future applications. The study is analyzed in detail in four main chapters of the thesis. These are; a literature review focusing on OEE and machine learning, description of methodology and approaches, development of prediction algorithms and techniques, analysis and evaluation of the obtained results and discussion of the results. This study is expected to make a significant contribution to the development of methods that can be used to improve the performance of industrial enterprises.