Strategies to Manage the Impacts of the COVID-19 Pandemic in the Supply Chain: Implications for Improving Economic and Social Sustainability
Bibliographic record
Abstract
This paper aims to identify the negative impacts of the COVID-19 outbreak on supply chains and propose strategies to deal with the impacts in the context of the readymade garment (RMG) industry supply chain of an emerging economy: Bangladesh. To achieve the aims, a methodological framework is proposed through a literature review, expert inputs, and a decision-aid tool, namely the grey-based digraph-matrix method. A total of 10 types of negative impacts and 22 strategic measures to tackle the impacts were identified based on the literature review and expert inputs. Then, the grey-based digraph-matrix was applied for modeling the strategic measures based on their influence to deal with the impacts. Findings reveal that the strategies “manufacturing flexibility”, “diversify the source of supply”, and “develop backup suppliers” have significant positive consequences for managing the impacts of the COVID-19 pandemic in the RMG supply chain. The findings help industrial managers recover from supply chain disruptions by identifying and classifying the impacts and strategies required to manage the major supply chain disturbances caused by the COVID-19 pandemic. As a theoretical contribution, this study is one of few initial attempts to evaluate the impacts of the COVID-19 outbreak and the strategies to deal with the impacts in the supply chain context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".