Evaluating the performance of circular suppliers in manufacturing sector: A rough Multi-Criteria decision making approach

Albayrak Ünal Ö., Kılıç Sarıgül R., Erkayman B., Pamucar D.

Environment, Development and Sustainability, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s10668-025-06627-0
  • Dergi Adı: Environment, Development and Sustainability
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, PASCAL, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Business Source Elite, Business Source Premier, CAB Abstracts, Geobase, Greenfile, Index Islamicus, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: AROMAN method, Circular supplier performance, Circular supply chain, OPA method, Rough number, Sensitivity analysis
  • Atatürk Üniversitesi Adresli: Evet

Özet

The concept of the circular economy (CE) is becoming increasingly important for a sustainable future in business. All industries should focus on adopting the principles of the circular economy and evaluating the performance of their suppliers according to these principles. However, evaluating supplier performance with a circular approach requires a complex decision-making process due to the limited scope of available criteria and the uncertainties in expert opinions. This study proposes a new hybrid approach to multi-criteria decision making (MCDM) based on a comprehensive criteria structure that includes technological, operational and strategical factors to overcome these limitations. The Rough Ordinal Priority Approach (R-OPA) method was used to determine the weighting coefficients of the criteria. A new method, the Alternative Ranking Order Method Accounting for Two-Step Normalization (R-AROMAN), was extended and used for the first time in evaluating supplier performance based on rough numbers. The integration of rough numbers in MCDM methods facilitates the handling of uncertainties associated with the problem. The proposed model is applied to a case study in a manufacturing company, the results are compared with other MCDM methods and the stability of the method is verified by a sensitivity analysis. This comprehensive approach enables decision-makers to manage uncertainty and make more effective strategic decisions in circular supplier selection.