Prescriptive Analytics in Procurement: Reducing Process Costs

In obtaining low-cost goods, the indirect expenses associated with sourcing suppliers can be substantial compared to the potential advantages of lower direct purchase costs. We addressed this problem as an exploration vs. exploitation trade-off. The proposed methodology uses a Bayesian technique to learn a stochastically optimal sourcing strategy directly from quotation data. We illustrate our approach using real quotation data for the procurement of electronic resistors (n=201,187). Rather than making optimal predictions, we concentrate on making optimal decisions. In doing so, we offered a significant improvement in purchase and procurement process costs. Our model is also more robust to prediction errors

Mining Suppliers from Online News Documents

Supplier intelligence represents an important type of competitive intelligence essential to organizational strategic planning and analysis, because the moves that the suppliers of a firm make will affect the business network’s health, which in turn will impact the performance of the firm. Central to supplier intelligence is to identify who the suppliers of a focal firm and its competitors are. Thus, an effective supplier mining technique that automatically identifies and discovers the suppliers of a firm and its competitors from publicly available information sources (news documents in this study) is desperately needed. In this study, we exploit text and link mining techniques to construct a supplier relationship mining (SRM) system to automatically discover supplier relationships concerning a focal company from online business news documents. Our empirical evaluation result suggests that our proposed SRM system outperforms its benchmark method in precision and recall