Bibliographic record
Abstract
The uncertainty in the current business environment is driven by events such as economic crises, climate change, global terrorism, shortage of resources and so on.This causes traditional supply chain operations models to become obsolete and not able to ensure the sustainability and competitiveness of the organizations.In this context, resilience is defined as the ability of a company/ community/ environment/ people to recover after it has been exposed to an important disturbing event, for instance, a natural disaster as a hurricane hitting the main suppliers, thus creating lack of raw materials in production lines.This article tackles how the assessment of the supply chain resilience, considering this capacity, enables one to be better prepared for an unstable risky environment and the post disaster consequences.We propose a model based on three categories of indicators; the first one is related to achieving an organizational resilience (to assess by results of responsiveness, flexibility and effectiveness), the second one is related to attaining business resilience (to assess by cash-to-cash, days of inventory, days of receivables and days of payables), and the third one is related to having a labour resilience (to assess by labour capabilities to overcome vulnerable living conditions).Two Peruvian supply chain companies (which belong to the food and pharmaceutical sectors) have been studied by using the model; the main results allow concluding that they have a low resilience level, because of their current three-category indicator results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".