A study of deep learning-based algorithms for supply chain logistics demand forecasting

Song Meijing () and Nor Hasliza Md. Saad ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 3, 1640-1654

Abstract: To accurately capture the dynamic changes and patterns of supply chain logistics demand, a prediction algorithm based on BiLSTM-AM is proposed. After collecting relevant data on supply chain logistics demand, outliers were removed using the Local Outlier Factor (LOF) to enhance data quality. From the cleaned dataset, features such as the supply chain logistics demand growth rate, inventory turnover rate, seasonal index, customer order volume, supplier delivery cycle, and transportation efficiency were extracted to construct a time series feature set for supply chain logistics demand. Based on the Long Short-Term Memory (LSTM) neural network model, a bidirectional structure was introduced, combined with an Attention Mechanism (AM), to establish a BiLSTM-AM-based supply chain logistics demand forecasting model, which was then trained using the gradient descent method. The obtained time series of supply chain logistics demand features were input into the trained forecasting model, and its output represented the forecasted supply chain logistics demand results. The experiment shows that the algorithm can accurately predict the demand for supply chain logistics. After applying this algorithm, the on-time delivery rate for each month is above 95%, and the decrease in logistics costs ranges from 0.3 to 0.5, indicating strong application value.

Keywords: BiLSTM; Deep learning; Feature items; Logistics demand forecasting; Local outliers; Supply chain. (search for similar items in EconPapers)
Date: 2025
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