Automated Demand Forecasting in small to medium-sized enterprises
中小企业的自动化需求预测AI 解读 ↓
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中小企业的自动化需求预测
- 研究问题
- 在缺少专职数据科学团队的中小企业中，如何自动完成销售预测，并根据企业间需求模式差异选择合适模型？
- 方法与思路
- 作者构建包含数据准备、训练和验证选模的时间序列预测流程，预选统计与机器学习模型，并加入多模型集成预测。
- 材料陈述的贡献
- 摘要称，食品和电气行业企业中的测试表现存在差异：部分企业优于朴素预测，一家项目制企业未获益，没有单一模型普遍最优。
- 阅读关注点
- 应核对训练与验证的时间划分、预测期限、模型调参及集成规则，并检查各企业的朴素基准、评价指标和流程维护成本。
- 阅读边界
- 依据 arXiv 预印本摘要，不能确认同行评审或期刊接收；摘要未提供样本规模和具体误差，企业测试不能直接证明普遍适用。
原文摘要
In response to the growing demand for accurate demand forecasts, this research proposes a generalized automated sales forecasting pipeline tailored for small- to medium-sized enterprises (SMEs). Unlike large corporations with dedicated data scientists for sales forecasting, SMEs often lack such resources. To address this, we developed a comprehensive forecasting pipeline that automates time series sales forecasting, encompassing data preparation, model training, and selection based on validation results. The development included two main components: model preselection and the forecasting pipeline. In the first phase, state-of-the-art methods were evaluated on a showcase dataset, leading to the selection of ARIMA, SARIMAX, Holt-Winters Exponential Smoothing, Regression Tree, Dilated Convolutional Neural Networks, and Generalized Additive Models. An ensemble prediction of these models was also included. Long-Short-Term Memory (LSTM) networks were excluded due to suboptimal prediction accuracy, and Facebook Prophet was omitted for compatibility reasons. In the second phase, the proposed forecasting pipeline was tested with SMEs in the food and electric industries, revealing variable model performance across different companies. While one project-based company derived no benefit, others achieved superior forecasts compared to naive estimators. Our findings suggest that no single model is universally superior. Instead, a diverse set of models, when integrated within an automated validation framework, can significantly enhance forecasting accuracy for SMEs. These results emphasize the importance of model diversity and automated validation in addressing the unique needs of each business. This research contributes to the field by providing SMEs access to state-of-the-art sales forecasting tools, enabling data-driven decision-making and improving operational efficiency.