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Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic

Xiaodong Wang^1,2, Xinchao Shi¹, Junbo Chen³

¹Business School, Zhengzhou University of Aeronautics, Zhengzhou, China.

|May 1, 2024

Summary

This study introduces an intelligent procurement system model using convolutional neural networks (CNN) and reinforcement learning to combat collusion. The novel approach achieves high accuracy in identifying procurement risks, enhancing system optimization.

Keywords:

CNN Collusion detection LSTM Purchase management Reinforcement learning

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Area of Science:

Business Administration
Computer Science
Artificial Intelligence

Background:

Corporate procurement faces challenges like inefficiency and noncompliance in evolving global markets.
Intelligent procurement systems require robust methods for risk mitigation, particularly against collusion behavior.

Purpose of the Study:

To present a novel procurement collusion identification model for intelligent procurement systems.
To mitigate risks associated with collusion behavior in corporate procurement management.

Main Methods:

A hybrid model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for feature analysis.
Integration of reinforcement learning to enhance model autonomy and intelligence for optimization.
Experimental analysis using diverse procurement data across five public datasets.

Main Results:

The proposed model achieved an average recognition accuracy of 95.1% across datasets.
Demonstrated superior performance compared to existing machine learning methodologies and common recognition networks.
Empirical findings confirm the model's proficiency in identifying procurement collusion.

Conclusions:

The developed model offers a pioneering solution for intelligent procurement system administration and optimization.
Provides a robust framework for risk mitigation against collusion in corporate procurement.
Highlights the potential of integrating CNN, LSTM, and reinforcement learning for advanced procurement management.