Distribution-Free Process Monitoring with Conformal Prediction

TL;DR

This paper presents a novel hybrid framework that enhances traditional Statistical Process Control by integrating conformal prediction, providing distribution-free, model-agnostic guarantees for more reliable and interpretable process monitoring.

Contribution

It introduces two new methods: conformal-enhanced control charts and process monitoring, combining conformal prediction with classic SPC for improved robustness.

Findings

Provides distribution-free process monitoring methods

Enables visualization of process uncertainty and proactive signals

Reframes multivariate control as anomaly detection

Abstract

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.