A decision-support approach under uncertainty for evaluating reverse logistics capabilities of healthcare providers in Iran. Issue 5 (15th September 2020)

• Record Type: Journal Article
• Authors: Karbassi Yazdi, Amir; Wanke, Peter Fernandes; Hanne, Thomas; Bottani, Eleonora
• Is Part Of: Journal of enterprise information management. Volume 33:Issue 5(2020)
• Page Start: 991
• Page End: 1022
• Publication Date: 2020-09-15
• Subjects: Reverse logistics -- Critical success factors -- ARAS method -- Z-numbers -- Healthcare industry -- Best-worst method -- Multi-criteria decision making; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-09-2019-0299
• Languages: English
• ISSNs: 1741-0398

Abstract

Abstract : Purpose: This paper aims to assess and prioritize manufacturing companies in the healthcare industry based on critical success factors (CSFs) of their reverse logistics (RL). The research involves seven medical device companies located in the Tehran Province, Iran. Design/methodology/approach: To identify and prioritize companies based on CSFs of RL, the study proposes a three-phase decision-making framework that integrates the Delphi method, the best-worst method (BWM) and the Additive Ratio Assessment (ARAS) method with Z-numbers. The weights required for this method are obtained by a variant of the BWM based on Z-numbers, denoted as Z-numbers Best-Worst Method, or ZBWM. Since decision-makers face an uncertain environment, Z-numbers, which are a kind of fuzzy numbers, are applied. Findings: First, after customizing CSFs by the Delphi method and obtaining 15 CSFs of RL, these are ranked by the hybrid BWM-ARAS method with Z-numbers. Results reveal which company appears to perform best with respect to their RL implementations. Based on this result, healthcare device companies should choose the highest priority company based on the selected RL CSFs and results from using the BWM-ARAS method with Z-numbers. Originality/value: The contribution of this paper is using a hybrid ARAS-BWM method based on Z-numbers. Each of these methods has some merits compared to other similar methods. The combination of these methods contributes a new approach for prioritizing companies based on RL CSFs with high accuracy and reliability.