Spare Parts Identification and Provisioning Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper addresses the problem of spare parts identification and provisioning for multi-component systems. A decision tree considering technical, economical and strategical information available is presented. Mathematical models are proposed to predict, for each spare part, the required quantity over a given planning horizon. The objective may be to maximize either the reliability or the availability of the system. Analytic models are proposed to determine the inventory management parameters such as the order quantity, the order point, the safety stock and so forth. For different management strategies, short comments regarding some improvement issues are provided. Cet article traite de problèmes liés à l’identification et à la gestion de pièces de rechange pour des systèmes multicomposants. Un arbre de décision, tenant compte des informations techniques, économiques et stratégiques disponibles, a été développé pour identifier les composants pour lesquels des pièces de rechange doivent être prévues. Des modèles mathématiques permettant de prédire les besoins sur un horizon donné et pour des objectifs spécifiques (fiabilité, disponibilité) sont proposés. Les paramètres de gestion tels que les quantités économiques à commander, les points de commande et les stocks de sécurité sont déterminés à partir d'expressions analytiques développées spécifiquement pour différentes stratégies de gestion. De brefs commentaires portant sur l’amélioration des performances d'un système de gestion des pièces de rechange sont donnés. Keywords: maintenancespare partsidentificationprovisioningMots clés: maintenanceidentificationpièces de rechangegestion des stocks
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it