Abstract Organizations require short-term up to long-run aggregated forecasts for making strategic, tactical, and operational decisions for their supply chain management. In supply chain forecasting, the Tt emphasis is primarily on the accuracy while coherency of forecasts often gets ignored. This paper proposes a novel cross-temporal forecasting framework (CTFF) to generate coherent forecasts at all levels of a retail supply chain. A deep learning method, the long-short-term-memory network, is used as the base forecasting method in the CTFF. The performance of the CTFF is evaluated on point-of-sales data from a large multi-channel retail supply chain. Through several performance metrics and statistical tests, we conclude that forecasts from the CTFF are significantly better than the direct forecasts. In addition, improvements are significant and consistent across cross-sectional and temporal levels of a supply chain. Further, it has been observed that bottom-up forecasts are more accurate than top-down forecasts when point-of-sales data is used for forecasting in online and offline retail supply chain.

中文翻译：

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用于供应链预测的跨时间层次框架和深度学习

摘要 组织需要短期到长期的汇总预测，以便为其供应链管理制定战略、战术和运营决策。在供应链预测中，Tt 的重点主要是准确性，而预测的一致性往往被忽视。本文提出了一种新颖的跨时间预测框架 (CTFF)，以在零售供应链的各个层面生成连贯的预测。深度学习方法，即长短期记忆网络，被用作 CTFF 中的基础预测方法。CTFF 的性能是根据来自大型多渠道零售供应链的销售点数据进行评估的。通过多项性能指标和统计测试，我们得出结论，CTFF 的预测明显优于直接预测。此外，在供应链的横截面和时间级别上，改进是显着且一致的。此外，据观察，当销售点数据用于在线和离线零售供应链的预测时，自下而上的预测比自上而下的预测更准确。