Improving the Robustness of Data-Driven Pipelines via the Adaptive Neuro-Fuzzy Inference System: An Industrial Use Case

Modern manufacturing is faced with challenges such as rapidly changing operations, real-time decision making, and uncertain data. As a result, the demand for accurate predictive analytics is becoming increasingly critical. This study presents an integrated Adaptive Neuro-Fuzzy Inference System (ANFIS), a data-driven pipeline that combines fuzzy rule systems with machine learning to improve defect prediction, enabling informed decision-making even in the presence of uncertainty and noisy data. The approach combines fuzzy logic to manage imprecise data, while machine learning enables adaptive learning from data. Applied to a real-world manufacturing case study, the proposed ANFIS model achieved an accuracy of 92%, precision of 87%, and F1-score of 84% in defect prediction, outperforming standalone fuzzy systems (69%, 55%, and 55%) and artificial neural networks (88%, 82%, and 79%). This approach contributes with an additional viable technique to an automated data-pipeline, proven to improve model accuracy and adaptability under certain criteria, supporting sustainable and intelligent decision making in line with Industry 5.0 principles.