A decision support model for sustainable supplier selection in sustainable supply chain management

This study is aimed at developing the most important and applicable criteria and their corresponding sub-criteria for sustainable supplier selection through a questionnaire-based survey. In addition, a hybrid model is proposed to identify the most sustainable supplier with respect to the determined attributes using an Iranian textile manufacturing company as case study. The first contribution of the research is developing a comprehensive list of sustainability criteria and sub-criteria and incorporating them into a questionnaire and distributing the questionnaire to academics and practitioners for establishing the importance and applicability of these criteria and sub-criteria. In order to demonstrate the robustness of the data obtained from the questionnaire, different established statistical tests (Cronbach's alpha and Mann-Withney U-Test) were applied. The results show that economic aspect is still the most essential aspect, followed by environmental aspect and finally social aspect. The second contribution is the development of a new hybrid model by integrating fuzzy preference programing, as one of the newest and most accurate fuzzy modification of Analytical Hierarchy Process, with Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. Fuzzy Preference Programming overcomes the shortcomings of the previous methods for obtaining the weight and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution prioritizes the suppliers and finds the best one under uncertainty. Generally, the developed list provides a basis that is helpful in improving suppliers' performance in terms of sustainability which leads to improvement in sustainable supply chain management performance. In addition, the developed hybrid model can deal with inconsistency, uncertainty and calculation complexity. Generally, the framework (including the first and second objectives) can be applied by managers to evaluate and determine their appropriate suppliers in the presence of uncertainty. (C) 2017 Published by Elsevier Ltd.