Labor productivity prediction in manufacturing industry based on BP neural network

Fengke Wang; Yujia He

doi:10.1117/12.3031147

19 July 2024 Labor productivity prediction in manufacturing industry based on BP neural network

Fengke Wang, Yujia He

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131815S (2024) https://doi.org/10.1117/12.3031147
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In order to support the robust growth of China's national economy, labor productivity research and analysis in the country's manufacturing sector are crucial. On the basis of research and analysis of a large number of relevant literature, a manufacturing labor productivity prediction model was established based on BP neural network, and finally seven indicators were identified as input variables, with manufacturing labor productivity as the output variable. In this study, China's statistical data during the period of 2001-2020 were selected as the training set and test set in order to build a prediction model and analyze its prediction results. It is concluded that the neural network model can effectively predict the labor productivity in manufacturing industry with high feasibility.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fengke Wang and Yujia He "Labor productivity prediction in manufacturing industry based on BP neural network", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131815S (19 July 2024); https://doi.org/10.1117/12.3031147

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