Investigating critical criteria for supplier quality development
Bibliographic record
Abstract
Identifying critical criteria for supplier quality development is vital for improving the performance of suppliers. In this paper, we determine critical criteria for supplier quality development based on review of literature and discussion with supplier quality experts from industry. The criticality of the criteria is determined based on: (a) Kano’s model (must haves, satisfiers, dissatisfiers); (b) Hill’s manufacturing strategy (order winners and order qualifiers); and (c) criteria weights. The results of the study yield the following as the must-be and order winning elements for supplier quality evaluation: price; delivery performance; service; environment; health and safety; ISO 9000; European Foundation for Quality Management; quality management policy; understanding of customer requirements; supplier product quality; process control; quality inspection programs; process for handling complaints; and a system for corrective action. For supplier quality development, the must-haves and order winning elements are: honoring outstanding suppliers; involving suppliers early in product and process development; and sending instructors and technical consultants to the supplier’s site. The proposed results have strong practical applicability and can be used by decision makers for supplier performance evaluation, setting targets for performance improvement, resource allocation for supplier quality development, and designing necessary operations for improving the performance of their 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 | 0.037 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".