Privacy-Preserving AI for Stock Replenishment in SAP Supply Chains: Machine Learning and Deep Learning Forecasting Approaches
DOI:
https://doi.org/10.15662/IJRAI.2021.0406004Keywords:
Privacy-preserving AI, Stock replenishment, SAP supply chains, Machine learning, Deep learning, Forecasting, Federated learning, Differential privacy, Secure multiparty computation, Inventory optimization, Data governanceAbstract
This paper explores privacy-preserving artificial intelligence (AI) techniques for stock replenishment in SAP-driven supply chains, focusing on machine learning (ML) and deep learning (DL) forecasting approaches.Traditional forecasting methods often face challenges in balancing accuracy with data security and compliance, especially in large-scale, data-intensive environments. By integrating privacy-preserving mechanisms such as federated learning, differential privacy, and secure multiparty computation, organizations can leverage sensitive enterprise data without compromising confidentiality. The study highlights how ML and DL models—including time series forecasting, recurrent neural networks (RNNs), long short-term memory (LSTM), and transformer-based architectures—can significantly enhance demand prediction, reduce stockouts, and optimize inventory turnover. Furthermore, it examines how privacy-aware AI models align with data governance regulations while enabling collaborative forecasting across global supply chain networks. The findings suggest that combining advanced predictive models with robust privacy-preserving strategies creates a resilient, efficient, and compliant replenishment framework in SAP ecosystems.
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