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arXiv · 2204.13792

Probabilistic Models for Manufacturing Lead Times

Recep Yusuf Bekci·Yacine Mahdid·Jinling Xing·Nikita Letov·Ying Zhang·Zahid Pasha
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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.

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Recep Yusuf Bekci, Yacine Mahdid, Jinling Xing, Nikita Letov, Ying Zhang, Zahid Pasha. 2022-04-28. Probabilistic Models for Manufacturing Lead Times. https://arxiv.org/abs/2204.13792

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