Abstract Retailers that offer perishable items are required to make hundreds of ordering decisions on a daily basis. For certain products, it is even necessary to make intraday decisions in order to increase the freshness of the goods while still serving the demand. We present a use case from the bakery domain where a part of the assortment has to be baked during the day as the delivered goods are not ready for sale. Hence, the operational performance depends on the decisions of the store personnel which can be optimized by a decision support system. Our approach to tackle this problem consists of two distinct phases: First, we forecast the hourly demand for each product. Second, the forecasts are input for a scheduling problem whose solution represents the baking plan that is provided to the store personnel. Based on our empirical evaluation, we conclude that forecasting accuracy has the biggest impact on the operational performance. More enhanced prediction methods noticeably outperform the reference methods. In particular, the machine learning based forecasting model significantly outperforms established time series models. If the computed schedules are executed as suggested, the customers can be served with freshly baked goods.

中文翻译：

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易腐货物当日货架补货决策支持

摘要 提供易腐烂物品的零售商每天需要做出数百次订购决定。对于某些产品，甚至需要做出日内决策，以在满足需求的同时增加货物的新鲜度。我们展示了一个来自面包店领域的用例，其中一部分分类必须在白天烘焙，因为交付的商品尚未准备好出售。因此，运营绩效取决于可以通过决策支持系统优化的商店人员的决策。我们解决这个问题的方法包括两个不同的阶段：首先，我们预测每个产品的每小时需求量。其次，预测是针对调度问题的输入，该问题的解决方案代表提供给商店人员的烘焙计划。根据我们的经验评估，我们得出结论，预测准确性对运营绩效的影响最大。更多增强的预测方法明显优于参考方法。特别是，基于机器学习的预测模型明显优于已建立的时间序列模型。如果按照建议执行计算出的时间表，则可以为顾客提供新鲜出炉的食品。