Supply chain contract selection in the healthcare industry: a hybrid mcdm method in uncertainty environment

Main Article Content

Ieva Meidute-Kavaliauskiene
Shahryar Ghorbani

Abstract

The aim of this research study is to address a critique of how and when a supply chain contract is selected based on critical success factors (CSFs) utilizing stepwise weight assessment ratio analysis (SWARA) and Evaluation by an Area-based Method of ranking (EAMR). This research study ranked supply chain contracts by the EAMR in uncertainty environments, such as when breaking down the health care industry. This is done by providing a theoretical framework for sustainable entrepreneurship in telecommunications industry, focusing on managerial and operational practices that should be modified, in accordance to a set of CSFs identified from experts in fertility hospital. As a novel strategy, in this research, the initial factors of selecting customized Supply Chain Management (SCM) were extracted via a Delphi method along with the EAMR to symbolize a decision matrix that needs primary weights acquired through the SWARA method by hesitant fuzzy number. CSFs for achieving SCM contract selection in fertility hospitals were found to rely on a tripod based on effectiveness, transparency, and accountability that are embedded within the ambit of managerial and operational practices, such as focusing and reducing cost and based on these factors the best SCM contract must be selected. Besides, the EAMR method has more reliability than other similar MCDM methods such as TOPSIS, MOORA, VIKOR, and so on main contribution of this paper is the combination of SWARA, EAMR, and using hesitant fuzzy set in the EAMR method. Finally, the result indicates that hospitals based on these CSFs must be selected contracts.

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Author Biographies

Ieva Meidute-Kavaliauskiene, Vilnius Gediminas Technical University

Vice-dean for Science at Business Management Faculty

Assoc.prof. dr.

Shahryar Ghorbani, Sakarya University

Dep.Management Information System, Phd student

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