基于机器学习模型的零售销售预测与运营优化研究

Research on Retail Sales Forecasting and Operational Optimization Based on Machine Learning Models

摘要: 本文旨在构建高精度的零售周销售额预测模型，为库存管理和运营优化提供量化依据。研究整合商店信息、假日事件及宏观经济指标等多维数据，结合系统化特征工程与XGBoost算法实现建模。实验结果显示，模型在测试集上取得R² = 0.9852，MAE相对误差1.39%，RMSE相对误差2.05%，显著优于基准模型，能够有效刻画零售销售的复杂规律。基于模型结果，研究提出以下建议：零售商应动态调整订货量和安全库存，以降低缺货与积压风险；在营销预算分配上应重点关注假日和促销活动的边际贡献，以提升投入产出效率；同时，应结合宏观经济与门店特征，制定差异化定价与人员排班策略，以实现资源的最优配置与精细化运营。

Abstract: This paper aims to construct a high-precision weekly retail sales forecasting model to provide quantitative support for inventory management and operational optimization. The research integrates multi-dimensional data including store information, holiday events, and macroeconomic indicators. Modeling is achieved through systematic feature engineering combined with the XGBoost algorithm. Experimental results show the model achieves R² = 0.9852 on the test set, with a relative MAE error of 1.39% and a relative RMSE error of 2.05%. These metrics significantly outperform the baseline model, effectively capturing the complex patterns of retail sales. Based on the model outcomes, the study proposes the following recommendations: Retailers should dynamically adjust order quantities and safety stock levels to mitigate stockout and overstock risks. Marketing budget allocation should prioritize the marginal contribution of holiday and promotional activities to enhance input-output efficiency. Additionally, differentiated pricing and staffing strategies should be developed by integrating macroeconomic factors with store-specific characteristics to achieve optimal resource allocation and refined operations.