Early Adoption of AI-Enabled Route Optimization in SAP Logistics via GKE Deployment
DOI:
https://doi.org/10.21590/Keywords:
AI-Enabled Route Optimization, SAP Logistics, Google Kubernetes Engine (GKE), Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Prescriptive Insights, Supply Chain Optimization, Real-Time Decision Making, Cloud-Native Infrastructure, Transportation Management.Abstract
The increasing complexity of global logistics networks demands intelligent solutions to optimize routing, reduce operational costs, and enhance delivery efficiency. This paper explores the early adoption of AI-enabled route optimization within SAP logistics, deployed on Google Kubernetes Engine (GKE), to provide scalable, real-time decision-making capabilities. The proposed framework integrates SAP logistics and transportation data with advanced machine learning algorithms to predict traffic patterns, optimize delivery routes, and minimize fuel consumption. Leveraging GKE’s container orchestration, elasticity, and high availability, the system ensures robust, fault-tolerant deployment capable of handling large-scale logistics data across multiple regions. AI-driven predictive models analyze historical and real-time operational data to identify optimal routing strategies, while prescriptive modules recommend actionable adjustments to logistics schedules and resource allocation. Experimental evaluation demonstrates improvements in delivery efficiency, route planning accuracy, and overall supply chain responsiveness. This research highlights the transformative potential of combining AI, SAP, and cloud-native architectures to enable proactive, intelligent, and adaptive logistics management in modern enterprise environments.
