Infrastructure engineer-to-order production systems: Drivers, concepts and principles of quality II and implications for research
Bibliographic record
Abstract
Infrastructure Engineer-to-Order (ETO) production systems are often subjected to poor quality and low productivity levels, resulting in time and cost overruns and the dissatisfaction of customers and stakeholders. Quality II emerging from ‘best practices’ in relational ETO supply chains offers a means to improve quality and productivity, but has yet to be recognised as a formal approach that can be explicitly embraced and enacted in practice. In filling this void, we conduct a narrative review to ascertain and discuss the drivers influencing the need for Quality II, examine its underlying concepts, and derive new principles based on people’s well-being, operational performance, and decision-making to underpin its implementation in infrastructure ETO production systems. It is suggested that Quality II will stimulate the learning, innovation, and continuous improvement needed to lift productivity levels in ETO production systems. As Quality II is a nascent concept, we also discuss its implications for research.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".