International Journal of Science, Engineering and TechnologyAuthors: Dr. Pankaj Malik, Ashwarya Garg, Akshat Carpenter, Aditya Verma, Alfaiz Mansoori, Vinayak Mishra
Abstract: Real-time inventory prediction is a major challenge in modern supply chain and warehouse management systems due to fluctuating customer demand, delayed stock updates, and inefficient forecasting methods. This research proposes an intelligent IoT-enabled inventory prediction framework using Time-Series Machine Learning techniques to improve inventory visibility and forecasting accuracy. The proposed system integrates IoT devices such as RFID tags, smart shelves, barcode scanners, and environmental sensors to continuously collect real-time stock movement and warehouse data. The collected streaming data is processed through preprocessing and feature engineering techniques before applying forecasting models including ARIMA, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Prophet, and XGBoost. The system predicts future inventory levels, reorder points, and demand fluctuations in real time, enabling proactive inventory management and automated replenishment decisions. Experimental evaluation was conducted using retail inventory datasets and simulated IoT warehouse data. The performance of the models was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and prediction accuracy metrics. Results indicate that the proposed LSTM-based IoT inventory prediction model achieved the best forecasting performance with an accuracy of 96.2%, RMSE of 8.14, and MAPE of 4.8%, outperforming traditional ARIMA and statistical forecasting approaches. The proposed framework reduced stockout situations by 28% and excess inventory costs by 21% compared to conventional inventory management systems. The integration of IoT and Time-Series Machine Learning significantly improved real-time inventory monitoring, warehouse efficiency, and supply chain responsiveness. The proposed research contributes toward intelligent Industry 4.0-based smart warehouse and predictive supply chain management systems.
DOI: http://doi.org/10.5281/zenodo.20700236
