A BIM-lean framework for digitalisation of premanufacturing phases in offsite construction
Bibliographic record
Abstract
Purpose The research introduces means for improving premanufacturing processes (design, procurement and bidding) by leveraging digitalisation in offsite construction. Specifically, this paper proposes a framework that provides measures for the planning and implementation of digitalisation in offsite construction by positioning building information modelling (BIM) as the key technology and lean principles to add value and reduce waste. Design/methodology/approach The paper follows the design science research approach to develop the proposed framework and attain the aforementioned objective. The developed framework includes data collection, value-stream mapping and simulation to assess current processes, develop and propose improvements. An empirical implementation is employed to demonstrate the applicability of both the framework and the measures used to evaluate the outcomes. Findings The application of the proposed three-stage framework resulted in 9.45%–23.33%-time reduction per year for the various improvement categories in premanufacturing phases. Employing simulation and applying the developed measures provide incentive for upper management to adopt the suggested improvements. Additionally, while the empirical implementation was tested on a modular construction company, the methods used indicate that the framework, with its generic guidelines, could be applied and customized to any offsite company. Originality/value While several studies propose that BIM-Lean integration offers an advantage in the context of production systems, this paper focuses on the initial design and planning phases, which are mostly overlooked in the literature. Moreover, the present study provides quantitative evidence of the benefits of data integration through BIM technology.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".