Supplier Segmentation in Large Enterprises: A Supervised Machine Learning Approach

With today’s extensive outsourcing practices, the role of supplier relationship management (SRM) in supply chain management (SCM) has never been more critical. This study investigates the use of machine learning (ML) techniques to enhance supplier segmentation in large-scale enterprises (LSEs). Supervised ML algorithms, including boosting, decision trees (DT), k-nearest neighbors (KNN), random forest (RF), and support vector machines (SVM), were applied to a dataset of 32,089 purchase orders from ABC, a large-scale enterprise. The random forest algorithm demonstrated the highest performance, with an AUC score of $\mathbf{9 9. 6 \%}$ and precision of $\mathbf{9 7. 8 \%}$, indicating its robustness for supplier classification. The study’s key findings underscore how ML-driven segmentation improves procurement decision-making, enabling more accurate supplier categorization, which in turn optimizes resource allocation and strengthens SRM practices. These insights position ABC as a leader in adopting ML for supply chain management, offering a scalable and efficient solution to supplier segmentation.