4.7 Article

Assessing measures implemented by export-oriented RMG firms in an emerging economy during COVID-19

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COMPUTERS & INDUSTRIAL ENGINEERING
卷 165, 期 -, 页码 -

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2022.107963

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COVID-19; Supply Chain Disruption; Fashion Industry; Best-Worst Method; Risk Management

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This study assesses the measures implemented by the Ready-Made Garments (RMG) sector in Bangladesh during the COVID-19 pandemic. The findings highlight the most common and important measures taken for employees, suppliers, and buyers, providing a blueprint for future supply chain risk management during similar crises.
Manufacturing firms that continued production activities during the COVID-19 have been taking necessary measures to cope with the risks imposed by the pandemic. This study assesses the measures implemented by the Ready-Made Garments (RMG) sector in Bangladesh. With the increase in COVID-19 cases in Bangladesh, following government order, along with firms in other manufacturing sectors, the RMG firms had to shut-down their production between March 26 and April 25, 2020. Soon after the factories reopened, they had to take necessary actions to ensure employee safety, supply of raw materials, and purchase orders from buyers. Using a semi-structured interview approach, we identify 16 measures that have been implemented in the RMG sector in Bangladesh for the employees, suppliers and buyers. Then, we assess the degree of implementation of these measures using the Bayesian Best-Worst method. We find that providing healthcare safety, bringing previously outsourced activities in-house, and ensuring smooth delivery of existing orders were the three most implemented measures for employees, suppliers and buyers, respectively. On a higher level, the RMG industry professionals prioritised buyer-related measures the most, followed by employee and supplier-related. The analysed measures provide a blueprint for supply chain risk management during future waves of COVID-19 transmission and for other potential large-scale natural disasters.

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