Development of Quality Cost Model within a Supply Chain Environment
Bibliographic record
Abstract
Determining the quality cost is one of the best ways that can assist industrial or business organizations to know clearly the investment and return of their quality improvement efforts. The information provided by accurate quality cost calculation is also a significant tool that can assist our assessment of the effectiveness of quality management system, as well as identification of quality issues within the organization and creation of opportunities for improvement. The purpose of this paper is to show the development of a quality cost model that includes all possible quality cost components such as Prevention, Appraisal and Failure (P.A. F) costs. Based on reviewing and analyzing various quality cost models, a generic quality cost model is developed. The proposed model can be used as a tool to calculate various quality costs. In addition, it can be used to determine the most serious failure cost. A case study has used to validate the proposed model. In this case, the implementation showed that the model is able to identify and quantify the hidden cost related to the quality in electronic assembly plant. Also, it is used to identify the potential improvement opportunities within the plant.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".