ABSTRACT
Suppliers are an integral part of every supply chain and companies more than ever rely entirely on their suppliers. Becoming triumph in today's marketplace depends on how successful enterprises deal with suppliers. As suppliers of a typical firm are not identical, companies ought to think strategically about their suppliers and certainly should have alternative strategies to treat them. Therefore, supplier segmentation can play a key role in supplier relationship management to cope, measure, and grow effective relations with their own suppliers. Supplier segmentation can be defined as classifying suppliers based on their similarities. The aim of this chapter is to propose a novel integrated multiple attribute decision-making (MADM) and data mining (DM) approach to supplier evaluation and segmentation. The existing literature is used to select the most suitable variables/criteria for evaluation and constructing a framework. The proposed methodology includes a combination of fuzzy group analytical hierarchical process (FGAHP), simple additive weighting (SAW), and two-stage cluster analysis as a DM tool. Fuzzy sets approach is employed to incorporate human judgments and vague information into the model. A case study was also conducted to illustrate the applicability of the model.
