Quality cost of material procurement in construction projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Material procurement constitutes a large share of the overall cost of construction projects. Understanding the factors influencing the cost of quality (COQ) in the procurement process could help identify opportunities for lowering quality cost without compromising quality. The paper aims to discuss these issues. Design/methodology/approach In this paper, a COQ model for the construction material procurement process is developed using the traditional prevention–appraisal–failure (PAF) approach. Using data from a $4bn aluminum smelter construction project, the authors conducted a simulation of the COQ model to evaluate various quality assurance policies. Findings This paper confirms that raising the prevention cost leads to a drop in failure cost as well as COQ for the project studied. While the authors are unable to provide blanket recommendations as the results are derived from a single project case study, it does suggest that construction material procurement processes would benefit from a higher prevention expenditure. And for certain cases where the authors observe a deviation from the traditional Juran’s model of COQ – the high appraisal cost in the procurement process – reduction of appraisal expenditure may in fact be more beneficial than its increase. Originality/value The research results suggest that appraisal expenditure should be tailored to each purchase order in order to maximize the total benefits. Additionally, this paper presents the first COQ model developed for the construction material procurement process. Another unique feature of the model is its inclusion of supplier-side costs, which are excluded in the conventional COQ analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it