Integration mechanisms for material suppliers in the construction supply chain: a systematic literature review
Bibliographic record
Abstract
The construction industry has long been criticized for its fragmented, inefficient, and uncoordinated supply chain. Thus, construction companies are actively looking for new strategies to overcome these issues and to improve their productivity. Supply chain integration is one strategy and many articles have addressed the mechanisms to help integrate the construction supply chain. However, little interest has been paid to material supplier integration despite their important role and their vast experience in the market. Hence, this study aims to identify the mechanisms that could contribute to facilitate material supplier integration in the construction supply chain. A systematic literature review was conducted to uncover the studies on this topic. A total of 310 articles were reviewed and analyzed to first reveal six integration mechanism categories: supplier qualification, supplier development program, contractual and relational policies, information sharing and integration systems, joint team working and problem solving, as well as supplier integration evaluation. Secondly, this study proposes a roadmap to illustrate when these mechanisms should be implemented in a construction project, according to both the project phases and the project delivery system. Finally, research gaps in the field are identified as well as future research directions that could be further explored by researchers and professionals.
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.
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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".