From Quality-I to Quality-II: cultivating an error culture to support lean thinking and rework mitigation in infrastructure projects
Bibliographic record
Abstract
While lean thinking may help tackle waste, rework remains an ongoing problem during the construction of infrastructure projects. Often too much emphasis is placed on applying lean tools rather than harnessing the human factor and establishing a culture to mitigate rework. Thus, this paper proposes the need for construction organisations to transition from the prevailing error prevention culture (i.e. Quality-I) that pervades practice to one based on error management (i.e. Quality-II) if rework is to be contained and reduced. Accordingly, this paper asks: What type of error culture is required to manage errors that result in rework and to support lean thinking during the construction of infrastructure projects? We draw on the case of a program alliance of 129 water infrastructure projects and make sense of how it enacted, in addition to lean thinking, a change initiative to transition from error prevention to an error management culture to address its rework problem. We observed that leadership, psychological safety and coaching were pivotal for cultivating a culture where there was an acceptance that ‘errors happen’ and effort was directed at mitigating their adverse consequences. The contributions of this paper are twofold as we provide: (1) a new theoretical underpinning to mitigate rework and support the use of lean thinking during the construction of infrastructure projects grounded in Quality-II; and (2) practical suggestions, based on actual experiences, which can be readily employed to monitor and anticipate rework at the coalface of construction.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".