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A Human Resource Demand Forecasting Method Based on Improved BP Algorithm

Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise's human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with...

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Autor principal: Lu, Xingguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983221/
https://www.ncbi.nlm.nih.gov/pubmed/35392044
http://dx.doi.org/10.1155/2022/3534840
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author Lu, Xingguang
author_facet Lu, Xingguang
author_sort Lu, Xingguang
collection PubMed
description Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise's human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with the corporate strategy, it is impossible for the enterprise to operate in full accordance with the plan during the operation process, so the human resource allocation plan only reflects the law of the enterprise's human resource needs during the enterprise development process. Giving effective guidance to the specific work of human resources is difficult. It is impossible to carry out effective human resources structure adjustment to adapt to changes in human resources demand due to changes in corporate tactics, business, scale, and other factors, especially when the current domestic human resources market has not yet fully formed. This paper examines the impact of key factors such as the company's business growth scale and production efficiency improvement on human resource needs with the goal of improving team structure, optimizing staff allocation, controlling labor costs, and improving efficiency and benefits. In this paper, we attempt to develop a human resource demand forecasting model based on business development and economic benefits and guided by intensive human resource development. We analyze and forecast the enterprise's total human resource employment, personnel structure, and quality structure using this model. In light of this, this paper employs an improved BP neural network to construct a human resource demand forecasting system, resulting in a new quantitative forecasting method for human resource demand forecasting with strong theoretical significance. Simultaneously, the human resource demand forecasting system developed can enable enterprises to carry out personnel demand forecasting from the actual situation, making forecasting more applicable, flexible, and accurate, allowing enterprises to realize their strategies through reasonable human resource planning.
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spelling pubmed-89832212022-04-06 A Human Resource Demand Forecasting Method Based on Improved BP Algorithm Lu, Xingguang Comput Intell Neurosci Research Article Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise's human resource input and output. The reality is that even if an enterprise designs a human resource allocation plan in accordance with the corporate strategy, it is impossible for the enterprise to operate in full accordance with the plan during the operation process, so the human resource allocation plan only reflects the law of the enterprise's human resource needs during the enterprise development process. Giving effective guidance to the specific work of human resources is difficult. It is impossible to carry out effective human resources structure adjustment to adapt to changes in human resources demand due to changes in corporate tactics, business, scale, and other factors, especially when the current domestic human resources market has not yet fully formed. This paper examines the impact of key factors such as the company's business growth scale and production efficiency improvement on human resource needs with the goal of improving team structure, optimizing staff allocation, controlling labor costs, and improving efficiency and benefits. In this paper, we attempt to develop a human resource demand forecasting model based on business development and economic benefits and guided by intensive human resource development. We analyze and forecast the enterprise's total human resource employment, personnel structure, and quality structure using this model. In light of this, this paper employs an improved BP neural network to construct a human resource demand forecasting system, resulting in a new quantitative forecasting method for human resource demand forecasting with strong theoretical significance. Simultaneously, the human resource demand forecasting system developed can enable enterprises to carry out personnel demand forecasting from the actual situation, making forecasting more applicable, flexible, and accurate, allowing enterprises to realize their strategies through reasonable human resource planning. Hindawi 2022-03-29 /pmc/articles/PMC8983221/ /pubmed/35392044 http://dx.doi.org/10.1155/2022/3534840 Text en Copyright © 2022 Xingguang Lu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lu, Xingguang
A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
title A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
title_full A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
title_fullStr A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
title_full_unstemmed A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
title_short A Human Resource Demand Forecasting Method Based on Improved BP Algorithm
title_sort human resource demand forecasting method based on improved bp algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8983221/
https://www.ncbi.nlm.nih.gov/pubmed/35392044
http://dx.doi.org/10.1155/2022/3534840
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