Research In Person-Post Fit Score Model And Enterprise Application

Faced with the challenges of a knowledge-based economy and global economic integration, business-to-talent is becoming more competitive. After enterprise development strategies identified, achieving the strategic goal must be implemented in various positions and employees. To achieve this strategic conduction process, it must be clear delineation of duties and job responsibilities. In the same time, there are wide varieties of positions, and job requirements are differences. Different employees at the same positions on the creation of performance may also be differentiated. Enterprises need not only the best employees, but also the most suitable employees. How to find Person-Post (P-P) Fit become urgent problems.In the second chapter, firstly, the relevant theory about P-P Fit is analyzed, and then the Profile Match function module in human resources management system is discussed, and the existing deficiencies is pointed out. In the third chapters, this article explores the establishment of the P-P Fit score model, set up BP neural network model Based on the Analytic Hierarchy Process method. In the following chapter, according to the different status between pre-hire and post-hire, the P-P Fit score model is divided into two steps. In the post-hire step, there are some more existing capacity and behavior variables for analyzing P-P Fit. So it builds a model of behavior score, to determine which post the staff is fit for in the future. Then it discusses the relations between Enterprise organization decision-making and P-P Fit Model, and models for prediction of human resources business.In the last chapter, it uses a Customer Manager post in bank as an example for empirical analysis. In the P-P Fit score model, in the first level, it analyzes and screens variable weights for BP neural network construction by AHP, in the second level, with MATLAB neural network toolbox, P-P Fit score model is constructed in BP neural network, and analyze the results of verification after modeling.