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Quality control is crucial in production and plays a key role in ensuring the delivery of superior products. The research presented in this paper aimed to establish a robust decision-support tool to enhance the production oversight process in candle oil cartridge manufacturing. The study included two stages. The first stage was concentrated on isolating and identifying the critical factors that have a significant statistical influence on product quality. Following this, advanced machine learning techniques such as the Support Vector Machine, Regression Trees, K-Nearest Neighbors, and the Artificial Neural Network were harnessed. These models showcased validation accuracies that ranged from 84.5% to 86.9%. Notably, the ANN model emerged as the best due to its unmatched AUC value, which indicates its effectiveness for accurate classification. These insights shed light on the enormous potential of state-of-the-art methods in overcoming manufacturing challenges and pave the way for industries to adopt and integrate rigorous quality control mechanisms.
