Motivated by the real-world inventory management problem of a large network of pharmacies, this paper proposes and studies a practically relevant Prescriptive Analytics approach for data-driven dynamic inventory control of large portfolios of interrelated products. We extend existing research on weighted Sample Average Approximation by integrating a ‘global learning’ model that effectively exploits cross-learning opportunities within the product portfolio. The results of an extensive numerical evaluation on real-world data suggest that our approach outperforms relevant benchmarks—in particular, models that rely on ‘local learning’ strategies where weight functions are trained separately for each product. The numerical results also allow us to derive important practical and structural insights regarding the value of contextual information in our global learning framework.

中文翻译：

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针对大型产品组合的数据驱动型库存控制：规范性分析的实际应用


受大型药房网络的实际库存管理问题的启发，本文提出并研究了一种实用的规范性分析方法，用于对大量相互关联的产品组合进行数据驱动的动态库存控制。我们通过集成“全局学习”模型来扩展对加权样本平均近似的现有研究，该模型有效地利用了产品组合中的交叉学习机会。对真实世界数据进行广泛数值评估的结果表明，我们的方法优于相关基准，特别是依赖于“本地学习”策略的模型，其中每个产品的权重函数都是单独训练的。数值结果还使我们能够获得关于全球学习框架中情境信息价值的重要实践和结构性见解。