Predictive Supply Chain Intelligence for Secure SAP Retail Operations and Real-Time Demand Forecasting
DOI:
https://doi.org/10.15662/IJEETR.2023.0506032Keywords:
Predictive Analytics, Supply Chain Intelligence, SAP Retail, Demand Forecasting, Machine Learning, Cybersecurity, Real-Time AnalyticsAbstract
Real-time supply chain intelligence has become essential for retail enterprises seeking to manage demand volatility, inventory uncertainty, logistics disruptions, and increasingly sophisticated cybersecurity threats. This study proposes an integrated predictive analytics framework for secure SAP retail network operations and demand forecasting. The framework combines SAP enterprise transaction data, point-of-sale records, inventory levels, supplier information, order histories, seasonal patterns, promotions, and logistics indicators to generate continuous supply chain insights. Data engineering processes are applied to cleanse, normalize, integrate, and securely transform heterogeneous retail data before predictive modeling. Machine learning models, including Random Forest, Gradient Boosting, Long Short-Term Memory networks, and ensemble forecasting, are employed to predict product demand and identify potential supply chain disruptions. A security intelligence layer monitors authentication events, network activity, API interactions, and abnormal transactional behavior across SAP-connected retail environments. Predictive outputs are integrated with operational dashboards to support inventory replenishment, procurement planning, warehouse allocation, and distribution decisions. The proposed methodology emphasizes real-time processing, predictive accuracy, data confidentiality, access control, and operational resilience. By combining demand forecasting with secure SAP network intelligence, the framework enables retailers to reduce stockouts, minimize excess inventory, improve response to disruptions, and strengthen enterprise security. The study demonstrates how predictive analytics can support intelligent, secure, and adaptive retail supply chain management.
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