A fuzzy-Bayesian model for supplier selection

The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making process is approached as a static one rather than a highly dynamic process. Delays, lack of capacity, quality related issues are common examples of dynamic aspects that have a direct impact on long-term relationships with suppliers. This paper presents a novel method based on the integration of influence diagram and fuzzy logic to rank and evaluate suppliers. The model was developed to support managers in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting criteria, to study the sensitivity of the behavior of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. To be effective, the computational implementation of the method was embedded into an information system that includes several functionalities such as supply chain simulation and supplier’s databases. A case study in the biodiesel supply chain illustrates the effectiveness of the developed method.

► The model represents explicitly the decision maker’s knowledge.
► The model supports long term relationships with suppliers.
► Suppliers are evaluated and ranked through Influence Diagram and Fuzzy Logic.
► The case study demonstrated the effectiveness of the designed method.