A Human Resource Demand Forecasting Method Based on Improved BP Algorithm

Area of Science:

• Business Administration
• Management Science
• Human Resource Management

Background:

• Enterprise development hinges on human resources, yet current allocation plans struggle to adapt to dynamic business changes.
• Inflexible human resource structures hinder adaptation to evolving corporate tactics, scale, and market conditions.
• A fully formed domestic human resources market is still developing, complicating strategic adjustments.

Purpose of the Study:

• To develop a human resource demand forecasting model integrating business development and economic benefits.
• To enhance team structure, optimize staff allocation, control labor costs, and boost efficiency.
• To provide a quantitative forecasting method for enterprise human resource needs.

Main Methods:

• Examined the impact of business growth scale and production efficiency on human resource needs.
• Developed a human resource demand forecasting model guided by intensive human resource development.
• Employed an improved BP neural network to construct a human resource demand forecasting system.

Main Results:

• The model forecasts total human resource employment, personnel structure, and quality structure.
• The developed system offers a new, theoretically significant quantitative forecasting method.
• The system enables applicable, flexible, and accurate personnel demand forecasting based on actual situations.

Conclusions:

• The human resource demand forecasting system facilitates strategic realization through effective human resource planning.
• Accurate forecasting allows enterprises to adapt proactively to changing demands.
• Optimized human resource allocation enhances overall enterprise efficiency and benefits.