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

Jung-sik Hong, Hyeongyu Yeo, Nam-Wook Cho and Taeuk Ahn
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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

JRFM, 2018, vol. 11, issue 4, 1-13

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.

Keywords: supplier segmentation; purchasing strategy; portfolio model; e-invoice; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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