Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need to match any given demand pattern to its most appropriate prediction model. To this end, we propose an automated model selection framework for retail demand forecasting. Specifically, we consider model selection as a classification problem, where classes correspond to the different models available for forecasting. We first build labeled training data based on the models’ performances in previous demand periods with similar demand characteristics. For future data, we then automatically select the most promising model via classification based on the labeled training data. The performance is measured by economic profitability, taking into account asymmetric shortage and inventory costs. In an exploratory case study using data from an e-grocery retailer, we compare our approach to established benchmarks. We find promising results, but also that no single approach clearly outperforms its competitors, underlying the need for case-specific solutions.

零售商提供范围广泛的库存单位 (SKU)，它们可能在例如需求数量、需求频率、需求规律和需求变化方面有所不同。鉴于需求模式的这种多样性，任何单一的需求预测模型都不可能在所有 SKU 中产生最高的预测准确度。为了通过改进预测来节省成本，因此需要将任何给定的需求模式与其最合适的预测模型相匹配。为此，我们提出了一个用于零售需求预测的自动模型选择框架。具体来说，我们将模型选择视为一个分类问题，其中类别对应于可用于预测的不同模型。我们首先根据模型在具有相似需求特征的先前需求周期中的表现构建标记的训练数据。对于未来的数据，我们然后根据标记的训练数据通过分类自动选择最有希望的模型。绩效以经济盈利能力衡量，同时考虑了不对称短缺和库存成本。在使用来自电子杂货零售商的数据的探索性案例研究中，我们将我们的方法与既定的基准进行比较。我们发现了有希望的结果，但也没有单一的方法明显优于其竞争对手，这是对特定案例解决方案的需求。我们将我们的方法与既定的基准进行比较。我们发现了有希望的结果，但也没有单一的方法明显优于其竞争对手，这是对特定案例解决方案的需求。我们将我们的方法与既定的基准进行比较。我们发现了有希望的结果，但也没有单一的方法明显优于其竞争对手，这是对特定案例解决方案的需求。