Privacy-Aware Deep Neural Networks for Quality and Process Control in SAP Manufacturing
DOI:
https://doi.org/10.15662/IJARCST.2022.0505003Keywords:
Privacy-aware AI, Deep neural networks, SAP manufacturing, Quality prediction, Process control, Differential privacy, Federated learning, Cryptographic security, Secure supply chains, Data governanceAbstract
This paper presents the application of privacy-aware deep neural networks (DNNs) for quality prediction and process control in SAP-enabled manufacturing supply chains. Traditional manufacturing systems often struggle to balance predictive accuracy with compliance to strict data privacy regulations. By integrating privacy-preserving mechanisms such as differential privacy, federated learning, and cryptographic security, the proposed framework ensures confidentiality of sensitive production and supplier data while enabling intelligent process optimization. The DNN models are designed to detect anomalies, predict product quality outcomes, and recommend process adjustments in real time, leading to reduced defects, improved efficiency, and enhanced compliance. Experimental validation demonstrates that privacy-preserving DNNs achieve competitive accuracy compared to conventional models while providing robust protection for enterprise data. The study underscores the importance of combining advanced AI with privacy-focused techniques to create secure, scalable, and efficient manufacturing ecosystems within SAP environments.
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