Managing Critical Spare Parts within a Buyer–Supplier Dyad: Buyer Preferences for Ownership and Placement
Why this work is in the frame
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Bibliographic record
Abstract
Despite the criticality and expense of spare parts, many firms lack a coherent strategy for ensuring needed supply of spare parts. Moreover, scientific research regarding a comprehensive spare parts strategy is sparse in comparison with direct material. Our research identifies and tests three literature‐based, theoretically anchored attributes that influence a buyer's preference for inventory ownership and inventory placement when managing the stock of a critical spare part. Our findings indicate that item specificity and item supply uncertainty are useful in predicting a buyer's preference for managing the inventory of a critical spare part. Furthermore, we find that buyers have (1) a strong preference for consignment‐based inventory management approaches, (2) a bias against inventory speculation despite its use in practice and analytical models, and (3) a strong preference for inventory postponement when the level of supply uncertainty is low.
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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 | 0.001 |
| 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 | 0.001 |
| 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 it