A 3-R principle for characterizing failure in relief supply chains’ response to natural disasters
Bibliographic record
Abstract
Purpose – The purpose of this paper is to introduce a 3R (right time, right place, and right material) principle for characterizing failure in humanitarian/relief supply chains’ response to natural disasters, and describes a Bayesian methodology of the failure odds with regard to external factors that may affect the disaster-relief outcome, and distinctive supply chain proneness to failure. Design/methodology/approach – The suggested 3Rs combine simplicity and completeness, enclosing all aspects of the 7R principle popular within business logistics. A fixed effects logistic regression model is designed, with a Bayesian approach, to relate the supply chains’ odds for success in disaster-relief to potential environmental predictors, while accounting for distinctive supply chains’ proneness to failure. Findings – Analysis of simulated data demonstrate the model’s ability to distinguish relief supply chains with regards to their disaster-relief failure odds, taking into account pertinent external factors and supply chain idiosyncrasies. Research limitations/implications – Due to the complex nature of natural disasters and the scarcity of subsequent data, the paper employs computer-simulated data to illustrate the implementation of the proposed methodology. Originality/value – The 3R principle offers a simple and familiar basis for evaluating failure in relief supply chains’ response to natural disasters. Also, it brings the issues of customer orientation within humanitarian relief and supply operations to the fore, which had only been implicit within the humanitarian and relief supply chain literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".