Intraday shelf replenishment decision support for perishable goods

Huber, Jakob ; Stuckenschmidt, Heiner

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Document Type: Article
Year of publication: 2021
The title of a journal, publication series: International Journal of Production Economics
Volume: 231
Issue number: Article 107828
Page range: 1-14
Place of publication: Amsterdam [u.a.]
Publishing house: Elsevier Science
ISSN: 0925-5273 , 1873-7579
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
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.

Economic SustainabilitySDG 8: Decent Work and Economic GrowthSDG 12: Responsible Consumption and Production

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