Data-Driven Warehouse Management for Power Materials: Integrating UWB Positioning with Demand Forecasting

This study addresses two critical issues in power material warehouse management: insufficient positioning accuracy leading to inefficient inventory auditing and uncontrolled material movement, and procurement-demand imbalances caused by subjective forecasting methods. We present an integrated warehouse management system that synergizes Ultra-Wideband (UWB) centimeter-level real-time positioning with data-driven demand forecasting. The UWB subsystem, built on STM32F1 microcontrollers (STMicroelectronics, Geneva, Switzerland) and DW1000 RF modules (Decawave Ltd., Dublin, Ireland), achieves high-precision location tracking by employing the Double-Sided Two-Way Ranging (DS-TWR) method combined with trilateration and triangular centroid algorithms. The data-driven procurement subsystem utilizes a vast historical dataset (4.86 million records from 36,988 grid projects, 2020–2024) to train demand prediction models. A comparative evaluation of six algorithms identified the Random Forest (RF) model as optimal, demonstrating superior performance with 89.2% accuracy, a Mean Absolute Error (MAE) of 5.48, and a Mean Absolute Percentage Error (MAPE) of 4.89%. The RF model effectively incorporates key factors like failure rates and seasonal cycles. Experimental validation confirmed the UWB subsystem’s robustness, with an average positioning error of 12.05 cm. The integrated system enables precise material tracking, 3D trajectory reconstruction, and generates data-informed procurement signals—including replenishment warnings, optimized order quantities, and adaptive resupply cycles. This approach significantly reduces surplus inventory while maintaining high material availability, offering a scientific, data-driven solution for enhancing efficiency in power material management.