Forecast of the efficiency and loss of quality by KNN and artificial neural networks methods

In this thesis, It is aimed to analyze the quality losses occurring in automation based mass production systems by using the previous data and to provide input for the employees' performance evaluation system. This study will increase the efficiency of the system and contribute to the continuity of the system. During the study, retrospective 650-day data were used, this data; KNN and Artificial Neural Networks algorithms are integrated into MS Excel program. Then, quality loss estimation is performed. The deviations of the predictions are examined. As a result, it has been decided that the artificial neural networks algorithm gives better results. Keywords: Artificial Neural Networks, KNN, MS Excel Solver, Loss of Quality.