AI-Augmented Supply Chain Demand Forecasting in Oracle Fusion SCM
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I2P102Keywords:
Supply Chain Management, Demand Forecasting, Oracle Fusion Scm, Artificial Intelligence, Machine Learning, Oci Data Science, Forecasting Accuracy, Inventory Optimization, MAPEAbstract
The rising instability and complexity of supply chains globally require strong forecasting skills to control the service levels, as well as maximize inventories. Oracle Fusion Supply Chain Management (SCM) provides embedded Artificial Intelligence (AI) and Machine Learning (ML) models that improve accuracy in demand forecasting and also offers the use of past data, seasonal trends and current market changes. The paper demonstrates the possibilities of AI-enhanced demand forecasting in the Oracle Fusion SCM by comparing the efficiency of the Oracle embedded ML models with pre-built custom models and external platforms such as TensorFlow and PyTorch, developed using the Oracle Cloud Infrastructure (OCI) Data Science. This study provides a complete evaluation framework by investigating accuracies on forecasting, scalability, interpretability, integration complexity, and performance across Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and several other measures. To test the embedded and the custom solutions, we apply real-world data of a global retail supply chain use case. As our results show, native ML in Oracle is a convenient way of getting the job done and integrating with other services, but under some conditions, custom models with OCI Data Science can be better. We conclude by providing an advisory to help us select between native and custom ML based on business requirements, data access, and the maturity of this operation
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