The Supply Chain: The Weak Link for Some Preferred Suppliers?
Bibliographic record
Abstract
SUMMARY More than ever before, the supply chain presents a significant challenge to firms that must develop a logistics system to help enhance product flow throughout their distribution channels. Of the various types of suppliers, those described as preferred should normally be in the best position to respond to the strategic aspirations of large order‐givers. Given the growing importance ascribed to supply chain management and supplier characterization in the literature, this article proposes to examine the actual contribution of various types of suppliers to supply chain integration. Following an empirical study focusing on a large multinational firm and its regular first‐tier suppliers, a detailed statistical analysis was conducted. Cluster analysis revealed the extent of a suppliers' logistics contributions. Overall, the findings suggest three types of contributions, and show that the intensity of a supplier's contribution has little to do with its status as a preferred supplier, depending instead on the sophistication of a supplier's logistics system. The system is characterized by a significant increase in the role of logistics in a firm's structures, through formalization of an organization, reinforcement of communication and information quality and the use of leading‐edge technology. The authors conclude that it may be tempting for a large order‐giver simply to expect supply chain performance from its preferred suppliers, rather than adding this characteristic to the others used as a basis for granting preferred status to some of its suppliers.
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How this classification was reachedexpand
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".