An omnichannel retailer with a network of physical stores and online fulfillment centers facing two demands (online and in-store) has to make important, interlinked decisions—how much inventory to keep at each location and where to fulfill each online order from, as online demand can be fulfilled from any location with available inventory. We consider inventory decisions at the start of the selling horizon for a seasonal product, with online fulfillment decisions made multiple times over the horizon. To address the intractability in considering inventory and fulfillment decisions together, we relax the problem using a hindsight-optimal bound, for which the inventory decision can be made independent of the optimal fulfillment decisions, while still incorporating virtual pooling of online demands across locations. We develop a computationally fast and scalable inventory heuristic for the multilocation problem based on the two-store analysis. The inventory heuristic directly informs dynamic fulfillment decisions that guide online demand fulfillment from stores. Using a numerical study based on a fictitious network embedded in the United States, we show that our heuristic significantly outperforms traditional strategies. The value of centralized inventory planning is highest when there is a moderate mix of online and in-store demands leading to synergies between pooling within and across locations, and this value increases with the size of the network. The inventory-aware fulfillment heuristic considerably outperforms myopic policies seen in practice, and is found to be near-optimal under a wide range of problem parameters.

中文翻译：

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全渠道零售网络的联合库存和履行决策

拥有实体店和在线履行中心网络的全渠道零售商面临两种需求（在线和店内），必须做出重要的、相互关联的决策——在每个地点保留多少库存以及从哪里履行每个在线订单，就像在线一样可以从任何具有可用库存的位置满足需求。我们在季节性产品的销售期开始时考虑库存决策，在线履行决策在此期间进行了多次。为了解决同时考虑库存和履行决策的困难，我们使用事后最佳边界来放松问题，为此，库存决策可以独立于最佳履行决策，同时仍然结合跨位置的在线需求的虚拟池。我们为基于两店分析的多地点问题开发了一种计算速度快且可扩展的库存启发式方法。库存启发式直接通知动态履行决策，指导商店的在线需求履行。使用基于嵌入美国的虚构网络的数值研究，我们表明我们的启发式方法明显优于传统策略。当在线和店内需求适度混合时，集中库存计划的价值最高，从而在不同地点和跨地点汇集之间产生协同作用，并且该价值随着网络规模的增加而增加。库存感知履行启发式方法大大优于实践中看到的近视策略，并且在广泛的问题参数下被发现接近最佳。