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Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

Author

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  • Jung-sik Hong

    (Department of Industrial and Information Systems Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

  • Hyeongyu Yeo

    (Department of Data Science, Seoul National University of Science and Technology, Seoul 01811, Korea)

  • Nam-Wook Cho

    (Department of Industrial and Information Systems Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea)

  • Taeuk Ahn

    (Korea Electronic Taxation System Association, Seoul 04791, Korea)

Abstract

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.

Suggested Citation

  • Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
  • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:4:p:70-:d:178637
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    References listed on IDEAS

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    Cited by:

    1. Brylowski, Martin & Schröder, Meike & Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Machine learning in supply chain management: A scoping review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 377-406, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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