AI and ML-Driven Fleet and Route Optimization in SAP under Data Privacy Regulations with Image Denoising
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506005Keywords:
AI, Machine learning, Fleet optimization, Route planning, SAP supply chains, Data privacy regulations, Privacy-preserving AI, Federated learning, Differential privacy, Image denoising, Predictive maintenanceAbstract
This paper explores AI and machine learning (ML) techniques for fleet and route optimization within SAP-driven supply chains, emphasizing compliance with data privacy regulations. Effective fleet management requires real-time analysis of vehicle data, traffic patterns, and delivery schedules, often involving sensitive information that must be protected. The proposed framework integrates privacy-preserving mechanisms such as differential privacy and federated learning to ensure secure processing of operational data. Additionally, advanced image denoising techniques are applied to enhance the quality of sensor and camera inputs, improving route planning, obstacle detection, and predictive maintenance. By combining AI/ML-driven optimization with robust privacy safeguards, the system reduces operational costs, enhances delivery efficiency, and ensures regulatory compliance. Experimental results demonstrate significant improvements in route accuracy, fleet utilization, and data security, highlighting the benefits of privacy-aware intelligent systems in SAP-managed logistics.
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