An Integrated Decision Support System (DSS) for Sustainable Supplier Selection, Evaluation, and Benchmarking Using a FIS Approach

Applications of sustainable supplier selection criteria in supply chain management (SCM) remain underdeveloped compared to other evaluation methods. This study focuses on three main dimensions of sustainable supplier performance: economic, environmental, and social criteria. The research aims to identify significant criteria within each dimension that are crucial for sustainable supplier selection process. These criteria will be utilized to develop a decision support system (DSS) that integrates a fuzzy inference system. The proposed model offers a holistic approach to supplier evaluation by considering economic performance, environmental impact and social responsibility. By incorporating these dimensions, the model ensures that the selection process aligns with broader corporate and environmental goals. The approach enables companies to make informed and sustainable decisions, ultimately contributing to a more resilient and responsible SCM. Furthermore, the fuzzy inference system effectively handles the inherent uncertainty and vagueness in supplier performance data. To enhance the robustness and reliability of the decision-making process, the proposed model can be integrated with multi-objective linear programming models, making it a valuable tool for supply chain managers. The findings of this work revealed that the economic criteria of supplier selection focus on quality, flexibility, cost, lead time, relationships, and technical capability. The environmental criteria include resource consumption, eco-design, recycling, emissions, and sustainability practices. The social criteria emphasize stakeholder involvement, staff training, safety, rights, and accident prevention in supplier selection. In summary, this comprehensive framework evaluates and benchmarks supplier sustainable performance, supporting more resilient and sustainable SCM.