Lean Construction 4.0: Exploring the Challenges of Development in the AEC Industry
Bibliographic record
Abstract
In 1994, Lean Construction was understood as the application of Toyota Production principles to Construction.Since then, Lean Construction researchers and advocates have made two fundamental contributions: i) Lean Construction has become a production management theory in its own right; ii) Lean Construction has involved not only production management, but also people, technology, sustainability, safety, education, among others.With the arrival of the "fourth industrial revolution" or Industry 4.0, there has been seminal research attempts to acknowledge the influence of Industry 4.0 on the architecture-engineering-construction (AEC) industry (e.g.Construction 4.0), where the focus has been primarily on technology.However, for Lean Construction to keep evolving and serving the AEC industry, it must embrace the changes propelled by Industry 4.0, but maintain the people-processes-technology triad at its core.We argue that a shift towards Lean Construction 4.0 is needed, paying attention to the synergies between production management theory and digital/smart technologies.The term "Lean Construction 4.0" does represent the vision where we envision the AEC industry to be in the future, rather than its current status.The goal of this paper is not to propose an implementation plan, but to identify research needs and to motivate a discussion on the role of Lean Construction in facing the challenges of adopting Industry 4.0 in the AEC industry.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".