2023
doi: 10.12720/jait.14.6.1289-1300
Development of an Ensemble Modeling Framework for Data Analytics in Supply Chain Management
Chibuzor Udokwu
,
Robert Zimmermann ,
Patrick Brandtner* ,
Tobechi Obinwanne
- Josef Ressel Centre for Predictive Value Network Intelligence, Department for Logistics, University of Applied Sciences Upper Austria, Austria
- Manuscript receivedMarch 30, 2023
- revisedMay 5, 2023
- acceptedJuly 25, 2023
- publishedNovember 27, 2023
Abstract
The application of Data Analytics (DA) in Supply Chain Management (SCM) provides a plethora of opportunities for improving the performance, efficiency, and resilience of organizations by predicting the behaviour of the supply chain network. In this regard, the use of complex DA approaches that involve the combination of multiple Machine Learning (ML) models such as the ensemble approaches have shown to be highly effective in improving prediction accuracy and model performance. Still, the application of these ensemble approaches has not yet been properly explored in SCM analytics. Thus, this paper presents an ensemble model framework specifically designed for SCM issues by exploring problem types and use cases where ensemble approaches can be applied in SCM. The developed framework enables the selection of the right ensemble models for specific SCM DA tasks. Thus, this paper contributes to the scientific body of knowledge by presenting a systematic approach to ensemble modelling in SCM analytics by outlining suitable ensemble model compositions for different SCM use cases and data analytics problems. Hence the results of this paper provide a potential tool for DA practitioners in SCM to further improve the performance of their Supply Chain (SC) networks by selecting appropriate ensemble elements for different SCM analytics problems.
Keywords
- data analytics
- ensemble model
- consensus algorithm
- supply chain analytics
- predictive analytics
- machine learning
How to Cite
Chibuzor Udokwu, Robert Zimmermann, Patrick Brandtner, and Tobechi Obinwanne, "Development of an Ensemble Modeling Framework for Data Analytics in Supply Chain Management," Journal of Advances in Information Technology, vol. 14, no. 6, pp. 1289-1300, 2023. https://doi.org/10.12720/jait.14.6.1289-1300
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
