Authors: Dr. Pankaj Malik, Bhumi Wadhwani, Pushkar Rathore, Miraj Chourasiya, Stuti Bhati, Pushkar Bhati
Abstract: Efficient inventory replenishment is essential for minimizing stock-outs, excess inventory, and operational costs in modern retail and supply-chain environments. Traditional replenishment approaches generally rely on fixed reorder points and historical averages, which may not adequately capture nonlinear demand patterns, seasonal variations, promotional effects, and changing lead times. This paper proposes a Cloud-Based Intelligent Replenishment System (CBIRS) that integrates machine learning, cloud computing, and business analytics to enable dynamic and data-driven inventory replenishment. The proposed framework utilizes cloud-based data ingestion and storage to integrate historical sales, product, pricing, promotion, inventory, seasonal, and supplier lead-time information. Multiple machine learning models, including Random Forest, XGBoost, LightGBM, CatBoost, and Long Short-Term Memory (LSTM), are evaluated for demand forecasting, followed by a hybrid forecasting model for intelligent replenishment decisions. The predicted demand is integrated with safety-stock estimation, reorder-point calculation, lead-time analysis, and dynamic order-quantity optimization. Experimental evaluation demonstrates that the proposed hybrid model achieves an MAE of 9.72, RMSE of 15.63, MAPE of 7.86%, and R² of 0.94, outperforming the individual baseline models. At the inventory-management level, the proposed CBIRS reduces the stock-out rate to 4.2% and overstock rate to 8.3%, while achieving an estimated 21.1% reduction in total inventory cost compared with the static replenishment approach and a 95.8% service level. The results indicate that integrating machine learning forecasting with cloud-based analytics and dynamic replenishment can significantly improve inventory availability, cost efficiency, and supply-chain decision-making.
