Study of Cost of Quality Behavior in Manufacturing Supply Chain Based on the Quality Maturity Status
Bibliographic record
Abstract
Measuring Cost of Quality (COQ) seems to be a critical factor for organizations in order to keep or grow their market share. However, until now the COQ has been measured almost exclusively only internally, i.e. within a company, while the role of a supply chain in delivering quality product to end users has been ignored. In this paper we argue that all the entities within supply chain affect the quality of a product or a service and their quality related activities should thus be inevitably considered. The objective of this research is to develop a mathematical model to estimate COQ as key performance measurement within manufacturing supply chain while considering quality Excellency status. Using classic PAF (Prevention-Appraisal-Failure) model classification to develop mathematical model and its integration with significant variables in supply chain entities are the key methodology in this work. Perceived quality is assumed as an appropriate definition of quality in manufacturing supply chain. Moreover, proposed model is examined against real time quality cost data of manufacturing supply chain in two intervals, first at quality immaturity period and then at quality maturity period. Statistical tools are used to validate the model and compare its behaviour in the two intervals. The results are then analyzed and discussed, and possible future works are presented.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".