Market demand in the wood furniture supply chain is strongly affected by seasonal fluctuations, unexpected disturbances, and multi-factor coupling, which often limits the accuracy and stability of traditional single forecasting models. To address this issue, an AMBOP-SRC model is proposed for market demand forecasting in the wood furniture supply chain, with the aim of improving forecasting accuracy, robustness, and generalization capability.

First, the wood furniture sales data were cleaned, normalized, and aggregated at the weekly level to construct a demand time series. Then, a SARIMA model was employed to capture the linear trend and seasonal patterns of the demand series, and the initial residuals were extracted. Next, an SVR model optimized by particle swarm optimization (PSO-SVR) was used to fit the nonlinear fluctuations contained in the residual series. On this basis, an adaptive movement block optimization procedure (AMBOP) algorithm was introduced to dynamically optimize the parameters and collaborative forecasting process of the hybrid model, thereby improving search efficiency and prediction stability in a high-dimensional parameter space. Finally, ablation experiments, comparative experiments, and generalization experiments were conducted, and model performance was evaluated using indicators such as RMSE and MAE.

Ablation experiments demonstrate that the absence of any component in the AMBOP-SRC forecasting model leads to a significant decline in predictive performance. The AMBOP method not only optimizes model parameters but also effectively balances the relationship between seasonal fluctuation modeling and emergency response, achieving deep adaptation to the characteristics of the furniture industry. Performance comparison experiments show that the AMBOP-SRC forecasting model significantly outperforms classical forecasting methods in the corresponding field, effectively improving the accuracy and interpretability of wooden furniture supply chain demand forecasting. Generalization experiments indicate that the AMBOP-SRC forecasting model exhibits strong adaptability across different datasets, making it applicable to demand forecasting in key stages of the wooden furniture supply chain.

The AMBOP-SRC forecasting model is highly suitable for wooden furniture supply chain demand forecasting, featuring high prediction accuracy and excellent generalization performance. Its results provide crucial decision-making references for furniture enterprises in optimizing inventory turnover rates and dynamically adjusting production plans, thereby enhancing supply chain resilience.