Version 1
: Received: 10 December 2019 / Approved: 11 December 2019 / Online: 11 December 2019 (12:21:26 CET)
How to cite:
Abdulla, A.; Baryannis, G.; Badi, I. Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques. Preprints2019, 2019120154. https://doi.org/10.20944/preprints201912.0154.v1
Abdulla, A.; Baryannis, G.; Badi, I. Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques. Preprints 2019, 2019120154. https://doi.org/10.20944/preprints201912.0154.v1
Abdulla, A.; Baryannis, G.; Badi, I. Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques. Preprints2019, 2019120154. https://doi.org/10.20944/preprints201912.0154.v1
APA Style
Abdulla, A., Baryannis, G., & Badi, I. (2019). Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques. Preprints. https://doi.org/10.20944/preprints201912.0154.v1
Chicago/Turabian Style
Abdulla, A., George Baryannis and Ibrahim Badi. 2019 "Weighting the Key Features Affecting Supplier Selection using Machine Learning Techniques" Preprints. https://doi.org/10.20944/preprints201912.0154.v1
Abstract
Supplier selection is an important part of supply chain management (SCM) for any organisation to achieve their objectives. The problem has attracted great interest from academics and practitioners. The selection process starts with determining the most important criteria out of a wide range. Many academic researchers apply multi-criteria decision-making (MCDM) techniques for supplier selection. However, the complexity of such approaches may increase significantly, especially when considering a large number of suppliers and selection criteria. This paper proposes an integrated approach combining machine learning classification with the Analytic Hierarchy Process (AHP) to select and evaluate the most suitable supplier. A Decision Tree (DT) classifier is used to select the most important criteria, instead of applying AHP on the complete set of criteria. The applicability of the approach is demonstrated using data from Libyan companies. Results show that decision trees can successfully lead to a most important subset of selection criteria, which would lead to a less complex application of AHP.
Keywords
supplier selection; machine learning; decision tree
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
Commenter:
The commenter has declared there is no conflict of interests.
Commenter: Aaryan Kapur
The commenter has declared there is no conflict of interests.
Github: github.com/aaryan-kapurgithub.com/aaryan-kapur
linkedin: linkedin.com/in/aaryankapurlinkedin.com/in/aaryankapur
Website: aaryankapur.techaaryankapur.tech
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