AI-Enabled Demand Forecasting in SAP: Machine Learning Models for Supply Chain Accuracy
DOI:
https://doi.org/10.15662/IJRAI.2023.0605003Keywords:
AI, Machine Learning, Demand Forecasting, SAP, Supply Chain Accuracy, Random Forest, Gradient Boosting, Recurrent Neural Networks, Integrated Business Planning, Forecast Error ReductionAbstract
Accurate demand forecasting is critical for optimizing supply chain operations, reducing costs, and improving customer satisfaction. Traditional forecasting methods often struggle to adapt to rapidly changing market dynamics, seasonality, and external disruptions. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) models within SAP supply chain systems to enhance demand forecasting accuracy. By leveraging SAP's advanced data management and analytics capabilities, AI-enabled models can process vast amounts of historical and real-time data to capture complex demand patterns. The study examines various ML algorithms, including Random Forest, Gradient Boosting, and Recurrent Neural Networks, applied to demand forecasting in SAP environments. The research methodology combines data analysis from SAP transactional databases, model development, and validation using real-world datasets from manufacturing and retail sectors. Results indicate that ML models significantly outperform traditional statistical methods such as ARIMA and exponential smoothing, reducing forecast errors by up to 25%. The paper discusses the integration challenges, including data preprocessing, feature engineering, and the need for scalable computational resources. Additionally, it highlights the benefits of embedding AI forecasting models into SAP Integrated Business Planning (IBP) and SAP Analytics Cloud for real-time decision support. The findings suggest that AI-enabled demand forecasting enhances supply chain responsiveness and agility, leading to improved inventory management, reduced stockouts, and optimized production scheduling. However, successful implementation requires addressing data quality issues, change management, and ongoing model retraining. This study provides valuable insights for supply chain professionals and researchers aiming to leverage AI and ML within SAP frameworks to drive operational excellence and competitive advantage.
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