A BIM-based Supply Chain Integration for Prefabrication and Modularization
Bibliographic record
Abstract
Prefabrication and modularization helps to reduce cost and schedule time for on-site activities. The use of Building Information Modeling (BIM) helps to improve collaboration and improve the construction process. The improved installation precision provided by BIM Model-Driven Prefabrication can decrease on-site labor time and increase productivity. Prefabrication, Modularization, and off-site construction transfers activities that would have been performed on site to earlier stages of the supply chain. The implementation of Just-In-Time (JIT) delivery transfers the costs and risks associated with inventory to the supplier. Construction Supply Chain Integration can help reduce cost and waste across the supply chain particularly for large and complex buildings. This paper presents a methodology that utilizes a BIM based construction supply chain integration to reduce cost and waste in the construction and offsite manufacturing processes. It utilizes the integration of BIM with the on-site schedule and the manufacturing or fabrication schedule of the different supply chain members. The methodology utilizes the onsite schedule, lead times of prefabricated elements or modules and the transportation logistics to help reduce cost across the supply chain. The information, material and cash flows as well as the transportation logistics is utilized in generating an optimized just-in-time delivery schedule for large and complex buildings. The optimized delivery schedule takes into account the variations in the on-site and off-site schedules to forecast delivery dates of precast elements or fabricated modules.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".