Cloud-Native Demand Forecasting in SAP: Leveraging AI and ML on Google Kubernetes Engine
DOI:
https://doi.org/10.15662/IJRAI.2021.0404003Keywords:
Cloud-Native, Demand Forecasting, SAP, Artificial Intelligence (AI), Machine Learning (ML), Google Kubernetes Engine (GKE), Predictive Analytics, Prescriptive Insights, Supply Chain Optimization, Real-Time Data ProcessingAbstract
Accurate demand forecasting is critical for optimizing supply chain operations and reducing operational costs in SAP environments. This paper presents a cloud-native demand forecasting framework leveraging AI and machine learning (ML) on Google Kubernetes Engine (GKE) to enable scalable, real-time insights. The proposed system integrates SAP data sources with distributed ML models to predict demand patterns, inventory requirements, and potential supply chain disruptions. By deploying on GKE, the framework achieves elasticity, high availability, and fault tolerance, allowing enterprises to handle dynamic workloads and large-scale datasets efficiently. Predictive analytics models provide short- and long-term demand forecasts, while prescriptive modules recommend actionable strategies for procurement, production, and warehouse management. Experimental results demonstrate improvements in forecast accuracy, operational efficiency, and decision-making agility. This research highlights the benefits of combining AI, ML, and cloud-native architectures to enhance SAP-driven supply chain planning and resilience.
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