An Early Warning Risk and Control Model for Manpower Capital Investment Using Data Warehousing and Computational Intelligence.

Comput Intell Neurosci

School of Electronics and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang 150022, China.

Published: April 2022

AI Article Synopsis

  • Human capital is crucial for enterprise development, focusing on improving personnel quality and core competitiveness.
  • The mobility of human resources has increased with market economy growth, which elevates investment risks associated with human capital and negatively impacts enterprise income and growth.
  • To mitigate these risks, the paper proposes an early warning and control model using data warehousing and computational intelligence, alongside methods for recruitment and training to safeguard against potential losses.

Article Abstract

Human capital plays an important role in the development of enterprises. Investing in human capital is the main focus of enterprises to improve personnel quality and enhance their core competitiveness. With the development of market economy, the function of human resource market allocation has been improved and the mobility of enterprise human resources has been enhanced leading to the increase in investment risk of enterprise human capital. Enterprise human capital investment risk has a negative impact on enterprises, reduces the income of enterprises' human capital investment, and affects their growth. Hence, enterprises need to avoid the risk of human capital investment or minimize the negative impact of risk. Using the data warehouse and computational intelligence, this paper constructs the early warning and control model for human capital investment risk and analyzes the existing approaches during the recruitment process and training, investment, and production, among enterprises. Finally, this paper proposes the corresponding control method according to the model inspiration.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8970945PMC
http://dx.doi.org/10.1155/2022/7624135DOI Listing

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