Proposing a Compact Instrument to Measure Supplier-Customer Relationships in the Context of TQM Activities
Bibliographic record
Abstract
This article reports on a study of the factors that characterize a complete supplier-buyer partnership, and a more cooperative orientation in the channel. Data were collected for 33 elements of supplier-customer relationships from 205 Canadian manufacturing organizations. The authors used principal component analysis to provide a relatively compact instrument that allows researchers and practitioners to measure supplier-customer relations more effectively and efficiently. This model of factor extraction indicates that three major components account for most of the scale variance. These components are: 1) improvement activities, 2) long-term purchasing, and 3) market adjustment. The application of this model to supplier-buyer relations has brought to light two main issues. First, the relationships between OEMs and their key suppliers have focused mainly on long-term purchasing arrangements and market adjustment. Second, joint improvement activities have not been given adequate attention despite the relative importance of this dimension of supplier-customer relations.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".