Probabilistic Models for Manufacturing Lead Times
This work addresses lead time prediction for manufacturing businesses, but it is incremental as it applies existing probabilistic methods to a new domain.
The study tackled the problem of predicting manufacturing lead times by applying probabilistic models like Gaussian processes and gradient boosting variants, achieving results that outperformed the company's domain-experience benchmark and showed good calibration with empirical frequencies.
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.