Zheng Jiafeng, Ma Ruijun
Graduate School of Architecture, Planning and Preservation, Columbia University, New York, NY 10027, USA.
School of Labor and Human Resources, Renmin University of China, Beijing 100872, China.
Comput Intell Neurosci. 2021 Jun 21;2021:6596548. doi: 10.1155/2021/6596548. eCollection 2021.
Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.
人力资源规划是人力资源管理的前提，而人力资源规划的基础工作是预测人力资源需求。科学合理的人力资源需求预测结果能够为企业人力资源规划和战略决策提供重要的数据支持，从而使人力资源管理在企业目标的实现中发挥更好的作用。由于人力资源需求受到多种因素的影响，各因素与人员需求之间存在高度的非线性和不确定性，以及企业人力资源数据的不完整性和不准确。本文选取自组织特征映射（SOM）人工神经网络预测模型作为预测模型，将样本数据的输入输出过程转化为非线性函数的最优解过程。在模型应用中，将人力资源需求预测指标体系作为SOM神经网络的输入，企业员工总数作为输出，从而解决人力资源需求影响因素与人力资源需求之间的非线性拟合问题。最后，通过对企业的实证分析，阐述了模型预测过程并实现了人力资源需求预测。
