Intraday shelf replenishment decision support for perishable goods

DOI:	https://doi.org/10.1016/j.ijpe.2020.107828
URL:	https://www.sciencedirect.com/science/article/abs/...
Weitere URL:	https://www.researchgate.net/publication/342059741...
Dokumenttyp:	Zeitschriftenartikel
Erscheinungsjahr:	2021
Titel einer Zeitschrift oder einer Reihe:	International Journal of Production Economics
Band/Volume:	231
Heft/Issue:	Article 107828
Seitenbereich:	1-14
Ort der Veröffentlichung:	Amsterdam [u.a.]
Verlag:	Elsevier Science
ISSN:	0925-5273 , 1873-7579
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Fachgebiet:	004 Informatik
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.