Sustainable/Robust Supplier Selection in the Post-Pandemic Era: Using Data Envelopment Analysis
Bibliographic record
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 sustainable/robust suppliers. The results of this research have been implemented in the business intelligence system of a company.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".