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.

翻译：供应链管理中的需求预测对于优化库存、减少浪费及提升客户满意度至关重要。传统方法常忽略天气、节假日及设备故障等外部因素，导致预测效率低下。本研究探讨了机器学习算法在零售与自动售货机领域需求预测中的应用。研究采用四种机器学习算法——极端梯度提升、自回归积分滑动平均模型、Facebook Prophet 与支持向量回归——进行库存需求预测。通过系统引入星期类型、节假日及销售偏差指标等外部变量以提升预测精度。实验表明，在引入外部变量后，XGBoost 模型以22.7的平均绝对误差取得最优性能；ARIMAX 与 Fb Prophet 模型亦表现出显著改进，而 SVR 模型性能相对不足。外部因素的整合能显著提升需求预测模型的精度，其中 XGBoost 被证实为最高效的算法。本研究为零售与自动售货机系统的库存管理优化提供了稳健的理论框架。