Quality in supply chains: an empirical analysis
Bibliographic record
Abstract
Purpose To analyze the state of supply chain quality management in manufacturing companies by testing several hypotheses regarding the knowledge these companies have about their different supply chain partners, the attributes that characterize customer‐supplier relationships and the factors that determine the development of quality specifications in a supply chain, and the effect of supply chain quality management activities of companies on product quality. Design/methodology/approach Six hypotheses related to supply chain quality management have been developed through literature review and tested using survey data from US manufacturing companies. Findings Provides information about the results of each hypothesis, their implications, and how these findings relate to the previous literature. Research limitations/implications The study offers insights into what the findings suggest and provides guidelines for future research to tackle issues raised by these findings. There were also some research limitations. For instance, the study relied on the perceptions of the respondents to operationalize the survey instrument, and the variables were mostly operationalized using single measures. Practical implications The study recommends ways managers can use the study's findings to improve supply chain quality. Originality/value This paper fills a void in the literature by focusing on quality in supply chain management.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".