Research on Intelligent Supply Chain Forecasting Based on Multi-Model Hyperparameter Optimization

In the e-commerce supply chain, transportation cost is one of the key factors affecting the overall supply chain cost. Reasonable planning of transportation routes and warehousing layout can effectively reduce transportation costs, thereby improving supply chain efficiency and benefits. Accurate demand forecasting plays a core role in determining the appropriate location and number of warehousing sites. Accurate forecasting can greatly enhance decision-making processes such as inventory optimization and distribution planning. This paper uses optimization algorithms and multi-model ensemble learning to accurately predict future demand, aiming to improve the adaptability and competitiveness of the supply chain. For this issue, the dataset is first standardized, and outliers are identified and removed. Subsequently, visual analysis of different data dimensions is conducted to preliminarily identify the influencing factors and features of demand forecasting. Based on the combination of merchants, warehouses, and products, demand quantity characteristics and structural features are quantified from three dimensions: central tendency, data distribution, and dispersion. The SSA Sparrow Search Algorithm-optimized KMeans clustering is used to process the quantity and structural features of historical demand, and sequence decomposition is applied to identify the demand patterns for each category, as shown in Figure 15. Finally, the paper evaluates the advantages and disadvantages of the constructed model and the handling of issues, proposes improvements to the model, and extends the model.