A Multiple-Criteria Decision Sorting Model for Pharmaceutical Suppliers Classification Under Multiple Uncertainties

Abstract

Selecting and evaluating suppliers is a major supply-chain concern for any company. It is even more crucial in pharmaceutical industries since delivering the right product to the right people at the right time requires specific conditions of storage and strict rules regarding expiry dates. In this context, supplier selection seems to be a complex task that involves a variety of conflicting criteria such as quality, performance history, guarantee policies, productive capacity, price and time. Therefore, many Multiple-criteria Decision Making (MCDM) methods have been applied to solve the supplier selection problem. However, most methods address only the ranking and choice problems. Besides, evaluating suppliers with regard to each criterion involves the presence of uncertainties and heterogeneous information, i.e., qualitative and quantitative data. The objective of this work is to propose a sorting MCDM model for pharmaceutical supplier selection under multiple uncertainties and heterogeneous information. The proposed model is based on an integration of the FlowSort and SMAA methods and Fuzzy theory. It allows pharmaceutical companies to develop a rating system to classify suppliers into categories, as actual and potential suppliers, in a context with multiple uncertainties and heterogeneous data information.