Smart Inventory Management in SAP Supply Chains: Leveraging AI and ML for Operational Efficiency
DOI:
https://doi.org/10.15662/k3ssbt10Keywords:
SAP Supply Chain, Smart Inventory Management, Artificial Intelligence, Machine Learning, Demand Forecasting, Operational Efficiency, Inventory Optimization, SAP IBP / SAP S/4HANAAbstract
Smart inventory management is increasingly critical for supply chain resilience and competitiveness, especially in complex enterprise systems such as SAP (Systems, Applications, and Products in Data Processing). This paper investigates how artificial intelligence (AI) and machine learning (ML) can be integrated into SAP supply chains to optimize inventory levels, reduce costs, and improve service levels. Key inventory challenges in SAP landscapes—such as demand variability, long lead times, seasonal fluctuations, stockouts, and overstock—are identified, and the roles of AI/ML algorithms (e.g., time-series forecasting, reinforcement learning, classification, anomaly detection) in addressing those challenges are examined. Methodologically, the study employs a mixed-methods approach: simulation experiments using historical SAP inventory and transaction data, comparative evaluation of forecasting models (e.g., ARIMA, Prophet, LSTM, XGBoost), and case-study interviews with supply chain managers. Results show that ML-based demand forecasting reduces forecast error (mean absolute percentage error) by up to 30% compared to baseline SAP standard forecast; stockout incidents drop by about 25%; holding costs decrease, while service levels improve by 10–15%. The study also discusses implementation considerations—data quality, model interpretability, integration with SAP modules (e.g., SAP IBP, SAP S/4HANA), change management, cost of development, and computational requirements. Advantages include better agility, improved decision support, lower carrying costs, and higher customer satisfaction; disadvantages include complexity, initial setup costs, risk of overfitting, and dependency on accurate data. Finally, the paper concludes with proposed future work around real‐time adaptive learning, hybrid human–AI decision frameworks, and scaling across global supply networks. This research contributes to both academic literature and practical SAP supply chain implementations by demonstrating measurable benefits and highlighting critical success factors for smart inventory management.
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