Deep Learning-Based Optimization of Cloud Enterprise Resource Planning (ERP) Systems for Adaptive Decision Support and Management Effectiveness Analysis
Modern enterprise resource planning (ERP) systems face the challenge of handling massive amounts of data and supporting real-time decision-making. With the rapid changes in the market environment, traditional ERP systems are limited in their ability to make adaptive decisions. This study aims to add...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10788717/ |
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| Summary: | Modern enterprise resource planning (ERP) systems face the challenge of handling massive amounts of data and supporting real-time decision-making. With the rapid changes in the market environment, traditional ERP systems are limited in their ability to make adaptive decisions. This study aims to address this issue by integrating deep learning techniques to enhance the management effectiveness of ERP systems. The study uses RNNs, CNNs and DRL models for time series prediction, image recognition and resource optimisation, respectively. The experimental results show that RNN achieves 95% accuracy in demand forecasting, CNN 98% accuracy in image recognition, and DRL achieves more than 10% cost savings in resource optimisation. The integrated ERP system achieved a 42.86% reduction in order processing time, a 25% improvement in inventory turnover, an 8% reduction in operating costs, and a 15% improvement in employee satisfaction. This study demonstrates the effectiveness of deep learning to enhance decision support in ERP systems and provides suggestions for future directions of improvement. |
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| ISSN: | 2169-3536 |