摘要：Since not all suppliers are to be managed in the same way, a purchasing strategy requiresproper 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 judgementsor require significant efforts. To overcome the limitations, this paper proposes a novel approach forsupplier segmentation. The objective of this paper is to develop an automated and effective wayto 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 isproposed. To validate the effectiveness, the proposed method has been applied to real e-invoice dataobtained from an automobile parts manufacturer. Results of high accuracy and the area under thecurve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value forautomating the identification of core suppliers. The main benefits of the proposed approach includethe enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learningmethod to e-invoice data, our method results in more reliable segmentation in terms of selecting andweighting variables.
