LSTM-XGBoost hybrid neural network for multi-scenario inventory and multi-dimensional comprehensive demand forecasting in the supply chain

Abstract

Due to the diverse range of product categories in e-commerce supply chains, precise prediction of warehouse inventory levels has become essential for improving resource allocation and minimizing operational expenses. This research delves into the modeling and analysis of inventory and sales performance across 350 product types within the supply chain framework. To enhance inventory forecasting accuracy, a multi-model evaluation system was developed, combining ARIMA, grey forecasting, and XGBoost regression techniques. Data preprocessing was rigorously validated using ADF tests for stationarity and residual white noise analysis. Based on a comparison of Mean Absolute Percentage Error (MAPE) metrics, the XGBoost model was chosen for its strong nonlinear modeling ability to forecast average monthly inventory levels over the next three months. For daily sales forecasting, the study utilized LSTM neural networks alongside the Prophet model for time series decomposition, allowing for the isolation of long-term trends, seasonal effects from holidays, and the impact of promotional events. A two-step calibration process enabled the model to effectively predict daily sales for the upcoming 90-day period. The findings suggest that the multi-model hyperparameter tuning strategy greatly improves the reliability of inventory forecasts, offering solid analytical backing for informed business decisions.