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Advances in Fuzzy Systems
Volume 2015 (2015), Article ID 841485, 14 pages
http://dx.doi.org/10.1155/2015/841485
Research Article

A Fuzzy Supplier Selection Application Using Large Survey Datasets of Delivery Performance

The University of Houston-Downtown, 326 N Main Street Houston, TX 77002, USA

Received 27 August 2014; Revised 10 October 2014; Accepted 13 October 2014

Academic Editor: Ferdinando Di Martino

Copyright © 2015 Jonathan Davis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. The values are derived from qualitative variables and expert opinion of membership in these variables found in industry survey data. The application is made to a supply chain management decision of supplier selection based upon delivery performance which is further divided into attributes that comprise this criterion. The algorithm allows multiple criteria to be considered for each decision parameter. Large sets of survey data regarding six suppliers in the electronic parts industry are gathered from over 150 purchasers and are analyzed through spreadsheet modeling of the fuzzy algorithm. The resulting decision support system allows supply chain managers to improve supplier selection decisions by applying fuzzy measures of criteria and associated beliefs across the dataset. The proposed model and method are highly adaptable to existing survey datasets, including datasets that have incomplete data, and can be implemented in organizations with low decision support resources, such as small and medium sized organizations.