@inproceedings{a98d0048b88c4f41a182b9faed9e3619,
title = "A neural network approach to predict deliverability in manufacturing",
abstract = "At present, estimating the deliverables of products in the manufacturing industry mainly depends on the knowledge of experts, but the knowledge of experts is sometimes difficult to obtain and often limited. The main purpose of this paper is to provide a method that can predict the deliverability of a product based on machine learning and data science technologies. The data used is real industry data and the machine learning algorithm used does fit the data set. The results of the model illustrate that using machine learning algorithms to predict deliverability is feasible. The machine learning algorithm achieves precision and recall percentages respectively. However, the findings also address some limitations. The machine learning algorithm has requirements on the form of the data. If the new data and the historical data have different forms, the machine learning algorithm cannot generalize well on new data.",
keywords = "Machine Learning, Neutral Network, Predictive Scheduling",
author = "Zhen Hao and Ahmed Abukar and Mozafar Saadat and Salman Saeidlou",
year = "2018",
doi = "10.3233/978-1-61499-902-7-361",
language = "English",
series = "Advances in Transdisciplinary Engineering",
publisher = "IOS Press BV",
pages = "361--366",
editor = "Keith Case and Peter Thorvald",
booktitle = "Advances in Manufacturing Technology XXXII - Proceedings of the 16th International Conference on Manufacturing Research, ICMR 2018, incorporating the 33rd National Conference on Manufacturing Research",
note = "16th International Conference on Manufacturing Research, ICMR 2018 ; Conference date: 11-09-2018 Through 13-09-2018",
}