Twenty-year application of logistics and supply chain management in the construction industry
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.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".