Twenty-year application of logistics and supply chain management in the construction industry
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 last decades have seen a growing interest in construction management amongst scholars, particularly, in how to apply supply chain management (SCM) strategies to improve logistics efficiency and project performance. Nevertheless, there is a lack of systematic literature reviews (SLRs) which integrate multiple quantitative methods to synthesise the literature on construction logistics and supply chain management (CLSCM) and analyse their trends during the last two decades. In this work, we concurrently deploy the rigorous six-step SLR protocol together with co-citation analysis, factor analysis, multidimensional scaling-based fuzzy k-means clustering, and keyword extraction and co-occurrence analysis to ascertain and examine clusters of CLSCM application. The results show that there are six established research clusters in CLSCM, namely, logistics and SCM for prefabricated construction, construction procurement, construction supply chain integration, green construction SCM, reverse logistics in construction and onsite construction logistics. Amongst these clusters, construction supply chain integration plays the most integral role. Informed by this ascertained knowledge structure, we explore the research trends during the period reviewed, propose a conceptual framework for CLSCM and suggest research avenues.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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