Risks Identification and Allocation in the Supply Chain of Modular Integrated Construction (MiC)
Bibliographic record
Abstract
Modular integrated construction (MiC) is an offsite construction technique which can improve construction quality, the certainty of the project cost, provide value for money and reduce construction time, waste generation, and carbon emissions. However, MiC is associated with a unique business model, engineering, supply chain, and stakeholder composition, resulting in bespoke uncertainties and risks. Prominent among them is the uncertainties and risk events in its linked supply chain segments. However, risks identification and allocation in the MiC supply chain segments is not well-established. This research identified and assessed 28 risk events (REs) across the manufacturing, logistics and on-site assembly segments of the MiC supply chain. A principal component analysis generated 10, 6 and 12 REs within the modular manufacturing, logistics, and on-site assembly segments, respectively. A fuzzy synthetic evaluation (FSE) modeling revealed that the on-site assembly REs are the most critical set of risk events with a criticality index of 5.58, followed by the modular manufacturing risk events (5.28) and logistics risk events (5.08). These rankings and criticality assessment have profound implications for the practice and praxis MiC risks management. It is a source of relevant information to stakeholders and practitioners in understanding the MiC supply chain risk events and may prioritize the riskiest events to improve the performance of MiC projects. Again, the assessed REs contributes to the checklists of MiC risk events and may form the basis for future studies on the risk of MiC. Future studies may examine the assessed risk events in different countries using larger samples.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".