Modeling Cost of Quality in the Construction Industry \nA closer look at the Procurement Process using System Dynamics
Bibliographic record
Abstract
The aim of this research is to develop the Cost of Quality (COQ) model for the procurement process of the construction industry and establish a general course of action for minimizing quality costs. A case study in a large Canadian construction company was conducted and the use of the Prevention-Appraisal-Failure (PAF) approach for the COQ model of the procurement process was explored. In contrast to the conventional COQ analysis we take into account not only the internal quality costs within the company, but also the costs of its suppliers. Several different policies were designed and their effects on quality costs investigated through System Dynamics (SD) simulation. The findings suggest that Prevention costs should be increased to minimize failures. It was also found that Appraisal cost is quite high in the procurement process and should be reduced in order to minimize overall COQ. However, this strategy could increase failure occurrences thereby damaging a company’s reputation. The possible reductions of Appraisal cost in the construction companies should thus be carefully considered.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 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".