Lean Health Check Framework
Bibliographic record
Abstract
Lean construction is an approach that prioritizes enhancing quality and value while minimizing waste.Lean management comprises of principles and concepts that must be observed to effectively implement Lean management and leverage its benefits.In line with Lean concepts and principles, appropriate behavior and culture must be properly implemented to achieve successful Lean management, hence necessitating the use of an established health check assessment to evaluate the level of Lean maturity.This study aims to introduce a health check assessment to ascertain the level of maturity of Lean behavior and culture in the construction industry.The health check assessment was formulated by identifying Lean success factors, which were further validated by Lean experts.The methodology employed to achieve the study objectives follows a Design Science Research (DSR) approach, which involves creating a health check framework and evaluating it through an expert panel interview among project parties in a real case study project.In addition to the Lean expert panel interview, other performance metrics, such as percent planned complete (PPC) and constraint information, were collected.The proposed framework was validated, and the results indicate that there may be a correlation between effective team communication and project performance.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".