# Distribution-Free Process Monitoring with Conformal Prediction

arXiv: https://arxiv.org/abs/2512.23602 · HTML: https://mlsanity.com/paper/2512.23602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Computing Full Conformal Prediction Set with Approximate Homotopy](https://mlsanity.com/paper/1909.09365/) (2019)
- [Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees](https://mlsanity.com/paper/2504.17721/) (2025)
- [On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing](https://mlsanity.com/paper/2203.03150/) (2022)
- [Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction](https://mlsanity.com/paper/2404.13002/) (2024)
- [Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction](https://mlsanity.com/paper/2501.07185/) (2025)

## Built on by (most cited first, 1 of 1)

- [Towards Universal Representation-Based Process Control](https://mlsanity.com/paper/2609.30790/) (2026)

## Abstract (arXiv)

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.