Distribution-Free Process Monitoring with Conformal Prediction
This addresses quality control issues in complex manufacturing environments, offering a more robust and interpretable approach, though it is incremental as it enhances existing SPC methods.
The paper tackles the problem of unreliable process monitoring in manufacturing due to violated statistical assumptions in traditional Statistical Process Control (SPC) by integrating Conformal Prediction, resulting in a hybrid framework that provides distribution-free guarantees and enables proactive signals like 'uncertainty spikes'.
Traditional Statistical Process Control (SPC) is essential for quality management but is limited by its reliance on often violated statistical assumptions, leading to unreliable monitoring in modern, complex manufacturing environments. This paper introduces a hybrid framework that enhances SPC by integrating the distribution free, model agnostic guarantees of Conformal Prediction. We propose two novel applications: Conformal-Enhanced Control Charts, which visualize process uncertainty and enable proactive signals like 'uncertainty spikes', and Conformal-Enhanced Process Monitoring, which reframes multivariate control as a formal anomaly detection problem using an intuitive p-value chart. Our framework provides a more robust and statistically rigorous approach to quality control while maintaining the interpretability and ease of use of classic methods.