用外部因素提升库存预测，XGBoost表现最佳。
A Data-Driven Predictive Framework for Inventory Optimization Using Context-Augmented Machine Learning Models
- 融合天气、节假日等外部因素增强预测精度
- XGBoost模型MAE低至22.7，优于其他三类算法
- 适合零售与自动售货机系统的库存优化决策
供应链管理中的需求预测对优化库存、减少浪费和提升客户满意度至关重要。传统方法常忽略天气、节假日、设备故障等外部影响，导致效率低下。本研究采用四种机器学习算法——极端梯度提升（XGBoost）、自回归积分滑动平均（ARIMA）、Facebook Prophet（Fb Prophet）和支持向量回归（SVR），在零售与自动售货机领域进行库存需求预测。系统性地引入工作日、节假日及销售偏差等外部变量以提升精度。结果显示，引入外部因素后，XGBoost取得最低均绝对误差（MAE）22.7，显著优于其他模型；ARIMAX与Fb Prophet亦有明显提升，而SVR表现较弱。研究表明，融入外部因素可大幅提升预测准确性，其中XGBoost为最优算法。该研究为零售与自动售货系统提供了高效的库存管理框架。
原文摘要 · Abstract (English)
Demand forecasting in supply chain management (SCM) is critical for optimizing inventory, reducing waste, and improving customer satisfaction. Conventional approaches frequently neglect external influences like weather, festivities, and equipment breakdowns, resulting in inefficiencies. This research investigates the use of machine learning (ML) algorithms to improve demand prediction in retail and vending machine sectors. Four machine learning algorithms. Extreme Gradient Boosting (XGBoost), Autoregressive Integrated Moving Average (ARIMA), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR) were used to forecast inventory requirements. Ex-ternal factors like weekdays, holidays, and sales deviation indicators were methodically incorporated to enhance precision. XGBoost surpassed other models, reaching the lowest Mean Absolute Error (MAE) of 22.7 with the inclusion of external variables. ARIMAX and Fb Prophet demonstrated noteworthy enhancements, whereas SVR fell short in performance. Incorporating external factors greatly improves the precision of demand forecasting models, and XGBoost is identified as the most efficient algorithm. This study offers a strong framework for enhancing inventory management in retail and vending machine systems.
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