M
Maryam Garshasbi
Researcher at University of Regina
Publications - 5
Citations - 125
Maryam Garshasbi is an academic researcher from University of Regina. The author has contributed to research in topics: Supply chain & Rough set. The author has an hindex of 2, co-authored 5 publications receiving 33 citations.
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Journal ArticleDOI
Strategies to Manage the Impacts of the COVID-19 Pandemic in the Supply Chain: Implications for Improving Economic and Social Sustainability
Hasin Md. Muhtasim Taqi,Humaira Nafisa Ahmed,Sumit Paul,Maryam Garshasbi,Syed Mithun Ali,Golam Kabir,Sanjoy Kumar Paul +6 more
TL;DR: In this article, a methodological framework is proposed through a literature review, expert inputs, and a decision-aid tool, namely the grey-based digraph-matrix method, to identify the negative impacts of the COVID-19 outbreak on supply chains and propose strategies to deal with the impacts.
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Admitting risks towards circular economy practices and strategies: An empirical test from supply chain perspective
TL;DR: In this article, the authors evaluated risks associated with circular economy practices and strategies in circular supply chains (CSCs) in response to the research gap related to the risks in CSCs in the current body of knowledge.
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Critical success factors for implementing green supply chain management in the electronics industry: an emerging economy case
TL;DR: In this paper, critical success factors for the implementation of green supply chain management (GSCM) for the electronics industry of an emerging economy are examined based on a literature review.
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Evaluating interaction between internal hospital supply chain performance indicators: a rough-DEMATEL-based approach
TL;DR: The study results indicate that the most critical aspects of hospital supply chain performance are completeness of treatment, clinical care process time and no delay in treatment.
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Productivity modeling of apparel industry using Hierarchical Evidential Reasoning
TL;DR: It can be observed that Yager's rule and Dempster-Shafer theory present administrators with a decision-making mechanism, which can help them to prioritize productivity characteristics, assessing the traits, and judgmental assessment to gain the total consequence of productivity.