Logistics collaboration in vehicle manufacturing: case studies with a triadic perspective
Bibliographic record
Abstract
This paper discusses logistics collaboration applied to a vehicle-manufacturing setting. We reach out to a larger segment of the supply chain by employing a novel unit of analysis, a logistics triad. The triad encompasses an Original Equipment Manufacturer, a Third-Party Logistics Service Provider, and first-tier suppliers. We use the framework known as Collaborative Transportation Management (CTM) to study collaborative transportation activities, focusing on the important relationship between logistics collaboration and logistics performance. Using a qualitative research orientation and a multi-case research strategy, we interviewed logistics practitioners in three countries to identify the enablers of CTM, the salient collaborative practices, and the performance outcomes. The study uncovers the extent to which CTM contributes to the operational and relational performance of the logistics triad. We provide empirical evidence of an ad hoc implementation of this notion. We propose avenues of intervention to preserve logistics collaboration and to enhance logistics performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".