Analysis of Green Supplier Using ARAS Model Integration in the Decision-Making Process

https://doi.org/10.55529/jeimp.33.1.14

Authors

  • Dr. Asma Hashim Ex Lecturer UPTEC, Ex-Researcher Kalinga University, India
  • Dr. Syed Mohammad Faisal Assistant Professor, Department of Accounting, Faculty of Management, Jazan University, Saudi Arabia
  • Dr. Ahmad Khalid Khan Assistant Professor, Department of Accounting, Faculty of Management, Jazan University, Saudi Arabia

Keywords:

Green Supply Chain Management, Environmental Risks, ARAS Model, Decision Making, MCDM.

Abstract

Green supplier environmental friendliness is important to businesses; figuring out which suppliers in the industrial supply chain care about the environment is one of the most critical issues. We are now integrating ARAS for six green providers and the best green suppliers, assessment, and decision-making. This research will be used as a standard for assessing the general efficacy of green providers using ARAS. One of the critical methods that will be employed, the ARAS theory for modeling uncertainties, is to determine the significance of the criteria taken into account in this research and the alternatives related to fate. In the framework of this research, we rank green service providers according to pre-established criteria. The ARAS should then be used to assess the research's contribution to the evaluation of green suppliers to choose the best ones. The foundation of ARAS analysis's hierarchical process methodologies is integration into decision-making. When uncertainty occurs, it plays a vital role, and ARAS presents a solution as the best possible answer to the problem at hand.

Published

2023-04-07

How to Cite

Dr. Asma Hashim, Dr. Syed Mohammad Faisal, & Dr. Ahmad Khalid Khan. (2023). Analysis of Green Supplier Using ARAS Model Integration in the Decision-Making Process. Journal of Environmental Impact and Management Policy, 3(03), 1–14. https://doi.org/10.55529/jeimp.33.1.14

Similar Articles

<< < 2 3 4 5 6 7 

You may also start an advanced similarity search for this article.