Modularization Business Case Analysis Tool: Learning from Industry Practices
Bibliographic record
Abstract
Modularization is a method of enhancing project value by exporting a portion of site work to fabrication/assembly shops/yards. Maximizing modularization’s benefits, however, is something the industry is still struggling to achieve. To achieve it, the construction industry needs a new modularization business case analysis approach and an associated computational tool. Thus the Construction Industry Institute’s (CII) Research Team (RT) 283 has developed a business case process to identify the optimum proportion of work hours to be moved offsite via module scope; the process also identifies the drivers of modularization. An optimal decision-making process is thereby established. Still missing from modularization business case analysis is a tool to support this process. This study develops just such a tool with the support of the CII Modularization Community of Practice. The tool manages information on module project drivers and, to the different parts of a module job, assigns a cost/factor/productivity. In developing the tool, researchers collected existing business case analysis tools from different companies and from the literature. The most suitable elements from these have been incorporated into a new modularization business case analysis tool. The tool identifies the optimum level of work hours to move offsite, providing specific savings, not just an indicative value. The tool, set up in three layers, permits details to be added and can be used, as a project is further developed, at successive phases with increasing rigor. This tool was subsequently reviewed by CII Modularization Community of Practice. This tool, by selecting optimum level of modularization, should help the construction industry maximize the benefits of modularization.
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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".