Lean Construction Supply Chain: A Bibliometric Analysis of the Knowledge Base
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The construction industry known for its adoption of ideas from other industries has also adopted lean thinking from the manufacturing sector for managing construction supply chains. Towards the successful adoption of this concept, there is a need to establish the philosophy surrounding is application within the construction industry. Thus, this study conducts a bibliometric analysis with the intention of discovering intellectual progress within lean concepts, relevant authors and philosophies surrounding lean construction supply chain concepts. The study adopted VOSviewer software through the assistance of citation, co-citation and keywords analysis to present a bibliometric and network analysis. A total of eight hundred and fifty Scopus indexed articles were extracted and used for the analysis. Through the analysis we have revealed the most important authors, journals and articles supporting lean concept. Also discovered are the major school of thought related to lean construction supply chain which are; waste reduction, just in time, integration and pre-fabrication. This review points to the benefits of using bibliometric network analysis for unearthing the practices of lean construction supply chain. These findings contribute to using a new research methodology for analysing the contribution of lean concept to the construction supply chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.054 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it