A lean approach to optimize BIM information flow using value stream mapping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| 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 it