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Integrating Machine Learning into Supply Chain Management:Challenges and Opportunities.

Authors :
Falkner, Dominik
Bögl, Michael
Gattinger, Anna
Stainko, Roman
Zenisek, Jan
Affenzeller, Michael
Source :
Procedia Computer Science; 2024, Vol. 232, p1779-1788, 10p
Publication Year :
2024

Abstract

Machine learning is a popular tool for solving problems, however, incorporating it into a use case with additional business logic poses many challenges. Training, managing and storing many different models is not an easy task, requiring the use of multiple frameworks and languages. To take full advantage of existing frameworks it is necessary to facilitate communication between different programming languages. This paper presents an approach to integrating machine learning in a real-world use case which involves predicting demand for a diverse set of products and combining it with business rules and other components to establish a system that improves and automates the ordering process. Machine learning models are trained on real-world data from a retailer in Austria and the predictions are incorporated into a heuristic that controls and manages stock levels. This work focuses on the challenges that emerge from the integration of machine learning and presents a message bus based architecture to address them. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
232
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
176148864
Full Text :
https://doi.org/10.1016/j.procs.2024.01.176