Assessing the social sustainable supply chain indicators using an integrated fuzzy multi-criteria decision-making methods: a case study of Turkey


Yildizbasi A., Ozturk C., Efendioglu D., BULKAN S.

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2020 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası:
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10668-020-00774-2
  • Dergi Adı: ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY

Özet

Sustainability of environmental, economic, and social systems aims to not destroy the resources that future generations should have in today's shacks. However, for many years, the concept of sustainability has only been examined in the economic and environmental contexts, and the concept of social sustainability has been neglected. The social dimension, which is a very important factor besides economic and biophysical environment within the social structure, is one of the most basic pillars of sustainability. In this study, we focused on the example of companies in the automotive industry in Turkey to evaluate them in terms of social sustainability. Many automotive manufacturers and suppliers are operating in Turkey. However, some of them are local, and some of them are working with regional companies. This differentiation affects the level of self-development of companies. Therefore, the case study for the evaluation of Turkey's social sustainability will provide the opportunity to achieve significant results. In this study, four companies that are in a supplier status in the automotive sector were considered by the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (FTOPSIS), which are Multi-Criteria Decision-Making (MCDM) methods. The obtained results revealed the situation in terms of the social sustainability of the automotive industry companies in Turkey. Finally, there were evaluations of what kind of improvements should be made in the framework of social sustainability indicators based on the case study results.