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Customer requirements-based enterprise resource planning personalization using a federated learning technique
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DOI：10.7717/peerj-cs.3750.png)
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Abstract
En 中文
Background Enterprise Resource Planning (ERP) software is critical for managing organizational operations, but often requires customization to meet diverse user and organizational needs. Traditional ERP personalization methods may compromise data confidentiality and user privacy. This study addresses the need for a secure, adaptive framework that enables ERP software personalization while maintaining the confidentiality and integrity of user data. Methods We developed an adaptive framework that integrates federated learning with artificial intelligence (AI) and machine learning (ML) to customize ERP software. Federated learning allows decentralized data processing, preserving the privacy of user information. The methodology provides step-by-step guidelines for implementing federated learning in ERP customization. User feedback and organizational requirements are incorporated into the AI and ML models to tailor software functionalities to specific organizational contexts. Results The framework enables organizations to personalize ERP software efficiently, integrating AI and ML capabilities while safeguarding sensitive data. Each implementation of federated learning within the framework enhances system adaptability, allowing functional customization according to unique organizational requirements. Organizations benefit from improved operational efficiency, system functionality, and user satisfaction. Conclusions This study presents a novel approach to ERP software personalization by combining federated learning, AI, and ML. The proposed framework ensures secure handling of user data while providing highly adaptive and customizable ERP solutions. Implementation of this framework offers organizations enhanced software functionality, operational effectiveness, and a competitive advantage through tailored ERP solutions.
Keywords:
ERP
Customization
Personalization
AI
Machine learning
Federated learning
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