A lean approach to optimize BIM information flow using value stream mapping
Bibliographic record
Abstract
Building Information Modelling (BIM) was introduced in the Architecture, Engineering and Construction (AEC) industry as a shared information platform that aims to improve productivity through better collaboration. The assumption is that a virtual integration of information among project stakeholders would reduce the issues around the fragmented nature of the processes that still prevail in the construction field. This paper aims to highlight the sources of waste in the information flows between an architecture firm, a Mechanical, Electrical and Plumbing (MEP) engineering firm, a general contractor (GC) and a MEP subcontractor (SC) in a BIM project – an aspect of waste little covered in the Lean literature. The focus is on the MEP process from early design to the final product. This research contributes to the identification of the main barriers to information flow, including the conflicts and waste sources that emerge from using BIM, as well as to the identification of emerging successes. Moreover, the findings offer practical implications by providing a visual of the patterns emerging from the use of BIM. Finally, by providing potential waste reduction strategies such as Value Stream Mapping (VSM) this work allows construction actors to identify and reduce sources of waste in their processes.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".