This research focuses on improving the performance of machine learning (ML) automation in inspecting product damage photos for returns at PT XYZ. Machine learning performance indicators (accuracy, precision, sensitivity) are still below the company’s 90% KPI target. By using Failure Mode Effect Analysis (FMEA) and Fishbone Analysis, the research identifies and addresses key issues to enhance machine learning reliability, reduce costs, and improve customer satisfaction.