Real-Time Machine Learning for SAP Order Fulfillment: Leveraging AI on GKE

Abstract

Efficient order fulfillment is a cornerstone of modern supply chain performance, requiring fast, accurate, and adaptive decision-making. This paper presents a real-time machine learning framework for SAP order fulfillment, leveraging AI on Google Kubernetes Engine (GKE) to enhance operational efficiency and responsiveness. The proposed system integrates SAP transactional and inventory data with distributed ML models to optimize order processing, inventory allocation, and delivery scheduling. Deploying on GKE provides scalability, high availability, and fault-tolerant orchestration, enabling the system to handle large volumes of real-time data across complex supply chain networks. Machine learning models deliver predictive insights on demand patterns, inventory shortages, and potential fulfillment delays, while prescriptive recommendations support proactive decision-making. Experimental results demonstrate significant improvements in order accuracy, processing speed, and overall supply chain efficiency. This research underscores the potential of combining AI, real-time analytics, and cloud-native infrastructure to advance next-generation SAP order fulfillment processes.