Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning
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Department of Industrial and Information Systems Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
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Department of Data Science, Seoul National University of Science and Technology, Seoul 01811, Korea
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Korea Electronic Taxation System Association, Seoul 04791, Korea
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Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2018, 11(4), 70; https://doi.org/10.3390/jrfm11040070
Received: 30 September 2018
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Revised: 14 October 2018
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Accepted: 24 October 2018
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Published: 26 October 2018
(This article belongs to the Special Issue Supply Chain Management)
Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.
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Keywords:
supplier segmentation; purchasing strategy; portfolio model; e-invoice; machine learning; random forest
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MDPI and ACS Style
Hong, J.-s.; Yeo, H.; Cho, N.-W.; Ahn, T. Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning. J. Risk Financial Manag. 2018, 11, 70. https://doi.org/10.3390/jrfm11040070
AMA Style
Hong J-s, Yeo H, Cho N-W, Ahn T. Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning. Journal of Risk and Financial Management. 2018; 11(4):70. https://doi.org/10.3390/jrfm11040070
Chicago/Turabian StyleHong, Jung-sik, Hyeongyu Yeo, Nam-Wook Cho, and Taeuk Ahn. 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning" Journal of Risk and Financial Management 11, no. 4: 70. https://doi.org/10.3390/jrfm11040070
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