Supply chain network design based on cost of quality and quality level analysis
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the impact of the cost of quality (COQ) expenditure allocations on a capacitated supply chain (SC) network. Design/methodology/approach This paper proposes a non-linear optimization model which integrates the opportunity cost (OC) (i.e. customer satisfaction cost), into the COQ with consideration of the QL in the supply chain network design decisions. In addition, it examines the effect of considering an investment at each SC echelon to ensure the best overall QL. A numerical example is presented to illustrate the behavior of the model. Findings The results show how the QL, COQ and facility location decisions change when incorporating the OC, investments and transportation costs into the SC model. Originality/value The novelty of this paper is that it considers the effect of OC, investment at each echelon and transportation costs on SC design by minimizing the overall spending on the COQ. These issues have not been explored, and for that reason, this paper contributes to the understanding of the critical factors that optimizes the SC COQ.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".