<![CDATA[Computer Numerical Control (CNC) machines remove material from a blank or workpiece using digital controls to produce custom-designed parts. Maintaining their accuracy and precision under challenging conditions after long-term usage is crucial. This study aims to evaluate CNC product quality using Overall Equipment Effectiveness (OEE) and enhance long-term performance through data-driven approaches. The method of this study focuses on analyzing scrap rate data, employing a u-chart to monitor stability, and applying machine learning regression models—K-Nearest Neighbour (KNN) and Random Forest (RF)—to forecast scrap rates. These forecasts help identify when preventive maintenance is necessary, preserving machine precision over time. This study also applied visualization of results with Microsoft Power BI to enhance data interpretation, aiding quick responses to potential problems. Results indicate that RF outperforms KNN in predicting scrap rates. Stacking these models further improves accuracy, offering a more reliable decision-making tool for anticipating quality issues. By detecting anomalies early, manufacturers can implement timely maintenance, minimizing downtime and prolonging CNC machine lifespan. In conclusion, integrating scrap rate analysis, statistical process control, and advanced machine learning techniques can maintain product quality and reduce inaccuracies. Companies should include more proactive maintenance planning by employing better forecasting.]]>
