Analysis of construction supply chain critical success factors: a multi-criteria decision-making approach
Bibliographic record
Abstract
This study analyses the critical success factors (CSFs) of construction supply chain management (CSCM) using a mix-method approach to identify and prioritise the CSFs for CSCM performance. An extensive literature review was conducted to identify the potential CSFs and with the help of experts, 21 relevant CSFs were selected. The analytical hierarchy process (AHP) was employed to prioritise the selected CSFs. Results showed that the top management commitment, information sharing and flow and supply chain finance are highly ranked among all the factors. Research findings will help to minimise resource wastage on less essential things and increase efficiency. This research is unique from several perspectives. Firstly, this study is one of the first studies, which identify CSF of construction supply chain in the context of UAE. Secondly, this study provides several managerial and practical implications to managers to minimise resource wastage on less essential things and increase productivity and efficiency.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".