International Journal of Operations and Logistics Management www.absronline.org/journals p-ISSN: 2310-4945; e-ISSN: 2309-8023 Volume: 3, Issue: 4, Pages: 351-371 (December 2014) © Academy of Business & Scientific Research *Corresponding author: Abbas Abbasi Assistance Professor, Management Department, Shiraz University, Shiraz, Iran E-Mail: aabbasi@shirazu.ac.ir 351 Supplier Selection Using Adaptive Neuro-Fuzzy Inference System and Fuzzy Delphi Abbas Abbasi 1 *, and Moloud Sadat Asgari 2 1. Assistance Professor, Management Department, Shiraz University, Shiraz, Iran 2. Master of Art in industrial Management, Management Department, Shiraz University, Shiraz, Iran One of the most important functions of the purchasing management in every organization is to settle on suitable providers. This decision is a sophisticated one since it requires exercising great care in paying simultaneous attention to conflicting and heterogeneous criteria. Therefore an appropriate method should be devised for supplier selection and identifying evaluation criteria. This study tries to investigate and present such a method. This study is carried out in two distinct phases. In the first phase, using previous literature, the evaluative criteria for supplier selection are identified and using the fuzzy Delphi method, the most important criteria for food industries are determined. In the second phase, using ANFIS method, the best suppliers are chosen among 60 selected suppliers. Ultimately, the data are analyzed by ANN method and the results obtained from both methods are compared. The results indicate that the ANFIS method provides a better performance compared to the ANN method regarding the supplier selection process in such a way that it reaches higher scores in almost all of the criteria. This study uses the proper method of fuzzy Delphi for determining the supplier evaluation criteria as well as the most recent and exact ANFIS method for supplier selection. Keywords: Supplier selection, adaptive neuro-fuzzy inference system (ANFIS), artificial Neural Network (ANN), fuzzy Delphi method INTRODUCTION The growth and development of information and communication tools in recent years have led the world’s companies and organizations to expand their markets all over the globe. Accordingly, the traditional local monopoly markets have been replaced by fully competitive global markets. Hence, organizations have to use each opportunity to optimize their competitive edge and commercial activities. In order to realize this objective, managers and scientists have agreed upon a unique conclusion positing that in order for an organization to survive the competitive environment, it should work with supply chain partners and in doing so it should improve the performance of the entire chain (Lin et al., 2009). The structure of a supply chain generally consists of a combination of potential suppliers, distributers, retailers and customers (Hugos, 2011). In recent years, the researchers as well as managers have found out that selecting the