Research on Inbound Logistics Demand Forecasting of Auto Parts Integrating Real-Time Production Plan Feedback and Error Compensation

Addressing nonlinear fluctuations in inbound logistics demand in the intelligent automotive industry, this paper proposes a SARIMA-LSTM-Attention forecasting model incorporating Real-Time Production Progress Feedback (RPF) features. By introducing production progress deviation into a hybrid framework that combines SARIMA-based linear forecasting with attention-enhanced LSTM residual correction, the proposed model effectively captures both linear trends and nonlinear demand disturbances. The model was evaluated using 995 daily observations collected from the inbound logistics system of a large new energy vehicle manufacturer from January 2023 to July 2025, with data from January 2023 to December 2024 used for training and validation and January to July 2025 reserved for testing. Compared with six representative benchmark models, the proposed model achieved the best overall performance, reducing sMAPE from 43.23% to 33.13% relative to the SARIMA baseline, representing an absolute reduction of 10.10 percentage points and a relative improvement of 23.36%. These results demonstrate the effectiveness of integrating real-time production feedback for demand forecasting and provide practical support for lean inventory management and logistics decision-making in automotive supply chains.