Supplier Categorization with K-Means Type Subspace Clustering

Abstract

Many large enterprises work with thousands of suppliers to provide raw materials, product components and final products. Supplier relationship management (SRM) is a business strategy to reduce logistic costs and improve business performance and competitiveness. Effective categorization of suppliers is an important step in supplier relationship management. In this paper, we present a data-driven method to categorize suppliers from the suppliers’ business behaviors that are derived from a large number of business transactions between suppliers and the buyer. A supplier business behavior is described as the set of product items it has provided in a given time period, a mount of each item in each order, the frequencies of orders, as well as other attributes such as product quality, product arrival time, etc. Categorization of suppliers based on business behaviors is a problem of clustering high dimensional data. We used the k-means type subspace clustering algorithm FW-KMeans to solve this high dimensional, sparse data clustering problem. We have applied this algorithm to a real data set from a food retail company to categorize over 1000 suppliers based on 11 months transaction data. Our results have produced better groupings of suppliers which can enhance the company’s SRM.

This work was part-supported by the Intel University Research HPC Program and the EIES Science Foundation Project of Xi’an Jiaotong University.