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Human Resource Planning and Configuration Based on Machine Learning.

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This study uses backpropagation neural networks (BPNN) and radial basis function neural networks (RBFNN) for effective human resource demand forecasting. These predictive models support optimal workforce planning and allocation, enhancing enterprise efficiency.

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Area of Science:

  • Business Administration
  • Computer Science
  • Data Science

Background:

  • Human resources are critical enterprise assets.
  • Accurate human resource demand forecasting is essential for resource allocation and optimization.
  • Predictive modeling offers a data-driven approach to workforce planning.

Purpose of the Study:

  • To analyze human resource needs using predictive models.
  • To determine key elements for company human resource allocation.
  • To improve enterprise operational efficiency through targeted HR planning.

Main Methods:

  • Employed backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) for forecasting.
  • Utilized historical company data as training and testing samples.
  • Developed predictive models to forecast current human resource demand.

Main Results:

  • Both BPNN and RBFNN effectively predicted required personnel numbers.
  • The proposed methods demonstrated the capability to forecast human resource demand.
  • The predictive models provided actionable insights for HR planning.

Conclusions:

  • The study validates the effectiveness of BPNN and RBFNN in human resource forecasting.
  • Predictive modeling supports strategic human resource planning and allocation.
  • Accurate forecasting enhances overall enterprise operational efficiency.