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Construction of Emergency Procurement System and System Improvement Based on Convolutional Neural Network
1School of Management, Harbin University of Commerce, Harbin 150000, China.
Computational Intelligence and Neuroscience
|August 2, 2022
Summary
This study analyzes emergency equipment procurement using convolutional neural networks to improve contract management and balance stakeholder interests for better future emergency preparedness.
Area of Science:
- Operations Management
- Supply Chain Management
- Emergency Preparedness
Background:
- Nations employ diverse management models for emergency equipment procurement.
- Existing systems face challenges in effective contract management and interest balancing.
Purpose of the Study:
- To analyze factors influencing emergency procurement system operations.
- To enhance contract management within emergency procurement.
- To balance supply and demand interests in emergency equipment acquisition.
Main Methods:
- Convolutional Neural Network (CNN) analysis to identify influencing factors.
- Examination of contract management and memorandum implementation.
- Monitoring and management of procurement systems.
Main Results:
- Identified key factors impacting emergency procurement system operations.
- Established effective contract management and monitoring mechanisms.
- Achieved a balance of interests between supply and demand.
Conclusions:
- Standardizing emergency procurement contracts is crucial for system improvement.
- Effective management and monitoring maximize benefits for emergency equipment supply and demand.
- The proposed framework meets current and future emergency equipment procurement needs across different levels.
