A multi-step recommendation engine for efficient indirect procurement

Abstract

The proliferation of data mining techniques is common across various corporate functions in an organization to discover deeper insights for making better decisions. One such opportunity emerges in the procurement function to streamline the process of procuring indirect materials. This paper proposes a two-step approach 1) adaption of association rule mining to derive the associated materials and 2) identification of right set of supplier(s) for the associated materials based on supplier selection methodology - Data Envelopment Analysis (DEA). The two step approach is used in the purchase requisition process, as a recommendation engine to assist the requester (user who request for materials) with a list of associated materials that can be requested together and also recommend the right supplier(s) for the associated materials. This significantly reduces the number of purchase requests (PR), and thus reduces the man hours in the procure-to-pay cycle and optimizes the supplier base. This is implemented on a sample dataset and a case study is provided for illustration.