Probabilistic Models for Manufacturing Lead Times
Machine Learning
2022-06-30 v2
Abstract
In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of laser manufacturing processes. We introduce probabilistic modelling in the domain and compare the models in terms of different abilities. While providing a comparison between the models in real-life data, our work has many use cases and substantial business value. Our results indicate that all of the models beat the company estimation benchmark that uses domain experience and have good calibration with the empirical frequencies.
Cite
@article{arxiv.2204.13792,
title = {Probabilistic Models for Manufacturing Lead Times},
author = {Recep Yusuf Bekci and Yacine Mahdid and Jinling Xing and Nikita Letov and Ying Zhang and Zahid Pasha},
journal= {arXiv preprint arXiv:2204.13792},
year = {2022}
}