Quality in supply chains: an empirical analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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