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Robust supplier selection: Systematic approach using data envelopment analysis: In the post-pandemic era
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Author (aut): Athaudage, Ganisha Nayanathara Premarathna
Author (aut): Lee, Wing Ki
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Degree granting institution (dgg): University Canada West. Master of Business Administration
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Abstract |
Abstract
With the existence of COVID-19, the whole economy experienced an unprecedented
challenge. Organizations must be resilient to the ever-changing and unanticipated market to avoid
being out of the fierce competition. In an era of information explosion, managers require a
systematic, explicable, comparative, and traceable approach to evaluate and choose suppliers. In
recent years, procurement strategies have been revamped due to the disruption in the global supply
chain by the pandemic and war in Europe. A wrong supplier selection decision seriously damages
the company’s supply chain, operations, and reputation. Therefore, partnering with a sustainable
supplier is a prerequisite for business success. With the rising importance of sustainability,
choosing a competent supplier is one of the significant strategic management decisions. A
sustainable supplier impacts business operations and accelerates long-term growth, enhancing
efficiency and effectiveness. In the post-pandemic era, it is expected to have new approaches to
define inputs and outputs to rank suppliers and logistics firms. This study uses Data Envelopment
Analysis (DEA) to identify a sustainable supplier. Our approach involves selecting suitable inputs
and outputs, improving the accuracy and relevance of the study to find more robust suppliers.
The results of this research were implemented in the business intelligence system of Miraab
Company, which is known as a reliable industrial valve manufacturing company in Iran. |
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pdf file; 77 pgs
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PUBLISHED
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Keywords
Decision making unit (DMU)
Supply chain
Robust supplier
Efficiency
Data envelopment analysis (DEA)
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ucw_317.pdf653.6 KB
100-Extracted Text.txt125.05 KB
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English
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Robust supplier selection: Systematic approach using data envelopment analysis: In the post-pandemic era
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669286
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