L'essence de l'excellence de la chaîne logistique : le projet SC2020 du Massachusetts Institute of Technology
Bibliographic record
Abstract
Résumé En 2005, le Center for Transportation & Logistics du Massachusetts Institute of Technology a lancé un projet pluriannuel de recherche nommé Supply Chain 2020 (SC2020). Ce projet vise à déterminer les facteurs critiques pour le succès futur des chaînes logistiques. Une recherche qualitative pendant la phase 1 du projet a permis d’avancer les hypothèses suivantes : une «excellente chaîne logistique» fait partie intégrante de la stratégie concurrentielle de l’entreprise, la supporte et la met en valeur; elle donne une impulsion au modèle d’exploitation de la chaîne logistique de manière à soutenir un avantage concurrentiel; elle réalise adéquatement un ensemble équilibré de mesures de performance opérationnelle compétitives; elle se concentre sur un nombre limité de pratiques d’affaires «sur mesure» qui se renforcent mutuellement afin d’appuyer le modèle d’exploitation et d’atteindre les objectifs opérationnels. Pour qu’une chaîne logistique soit excellente – et procure un avantage concurrentiel –, ses gestionnaires doivent créer un ensemble évolutif de pratiques sur mesure basées sur une compréhension des principes opérationnels qui stimulent celles-ci.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".