Privacy-Aware SAP-Integrated Machine Learning Models for Procurement Efficiency with Sign Language Interpretation Support
DOI:
https://doi.org/10.15662/IJARCST.2021.0405002Keywords:
Privacy-aware AI, SAP integration, Machine learning, Procurement efficiency, Sign language interpretation, Accessibility, Differential privacy, Federated learning, Secure data handling, Supplier evaluation, Inclusive digital transformationAbstract
This paper investigates the development of privacy-aware, SAP-integrated machine learning (ML) models to enhance procurement efficiency while incorporating sign language interpretation support for inclusive digital transformation. Procurement processes in enterprise systems often involve sensitive financial and vendor-related data, requiring strict adherence to privacy and compliance regulations. By embedding privacy-preserving techniques such as differential privacy, federated learning, and secure data handling into SAP-driven ML models, organizations can achieve accurate demand forecasting, supplier evaluation, and automated purchase decision-making without compromising data confidentiality. Additionally, the integration of sign language interpretation through AI-powered gesture recognition and natural language processing enables accessible procurement platforms, fostering inclusivity for hearing-impaired stakeholders. The proposed framework enhances procurement accuracy, decision-making speed, and accessibility while ensuring compliance with global data protection standards. This study highlights the dual benefits of secure, intelligent automation and universal accessibility within SAP procurement ecosystems.
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