|   | 
Details
   web
Record
Author Rozas Andaur, J.M.; Ruz, G.A.; Goycoolea, M.
Title Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company Type
Year 2021 Publication Electronics Abbreviated Journal Electronics
Volume 10 Issue 22 Pages 2787
Keywords out of stock; machine learning; classification algorithms; imbalance data; supply chain management; decision support; retail industry application
Abstract For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a certain commodity while attempting to buy it. This paper focuses on addressing this problem from a manufacturerís perspective, conducting a case study in a retail packaged foods manufacturing company located in Latin America. We developed two machine learning based systems to detect OOS events automatically. The first is based on a single Random Forest classifier with balanced data, and the second is an ensemble of six different classification algorithms. We used transactional data from the manufacturer information system and physical audits. The novelty of this work is our use of new predictor variables of OOS events. The system was successfully implemented and tested in a retail packaged foods manufacturer company. By incorporating the new predictive variables in our Random Forest and Ensemble classifier, we were able to improve their systemís predictive power. In particular, the Random Forest classifier presented the best performance in a real-world setting, achieving a detection precision of 72% and identifying 68% of the total OOS events. Finally, the incorporation of our new predictor variables allowed us to improve the performance of the Random Forest by 0.24 points in the F-measure.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume (down) Series Issue Edition
ISSN 2079-9292 ISBN Medium
Area Expedition Conference
Notes Approved
Call Number UAI @ alexi.delcanto @ Serial 1487
Permanent link to this record