The performance improvement of sustainable palm oil supply chain management after COVID-19: Priority indicators using F-AHP
Bibliographic record
Abstract
The performance of sustainable supply chain management today, especially for palm oil, continues to experience a drastic decline from the social, economic, and environmental perspectives. Both the supply and demand sides are undergoing severe disruption due to the COVID-19 pandemic. To survive the COVID-19 situation and afterward, the palm oil industry needs to focus on priority indicators for immediate improvement. For that reason, our study aims to determine the primary indicators used to assess the performance of sustainable supply chain management to improve the palm oil industry's performance immediately. The F-AHP method is used to rank which indicators are focused on the COVID-19 situation and thereafter. The findings of this study designate that there are three main indicators, namely from the economic side (adaptability), the social side (improving employee health and safety), and the environmental side (sustainable supplier management). This finding is beneficial for the industry and for supply chain actors such as suppliers, customers, and the government in taking attitudes and setting policies related to sustainable supply chain management in the face of pandemic.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".