Application Of Leagile Theory In Construction Industry To Improve Value-creating Activities
Bibliographic record
Abstract
This study explores the integration of Lean and Agile methodologies, known as “Leagile”, in the construction industry. Lean construction emphasizes continuous workflow and process optimization to reduce waste, while Agile management embraces the inevitability of change, promoting continuous discovery and learning throughout a project’s lifecycle. Clearly, both approaches require effective communication with project stakeholders and customers to enable iterative improvements. In recent years, the concept of “Leagile”— blending these two methodologies — has already been successfully applied in various sectors such as manufacturing and supply chain management. However, in the construction industry, the hybridization of Lean and Agile management is still in its early stages. Initial experiments in this area have shown promising potential and opened up a wide range of new possibilities in construction management. This paper contributes to the increasing body of work on Leagile, especially in relation to construction, by proposing a framework for its implementation and assessing the viability of this management paradigm. A case study involving a single-family residential unit project illustrates this novel method’s application in a practical setting.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".