Developing a Construction-Oriented DfMA Deployment Framework
Bibliographic record
Abstract
Applying design for manufacture and assembly (DfMA) principles in the construction industry has gained attention in recent years. Studies convey that the application of DfMA in construction projects can significantly enhance overall productivity. However, the literature on construction-oriented DfMA is still limited, and its application in real-life projects has been stifled due to various constraints. Following a design science research method, a systematic literature review was conducted to identify the construction-oriented DfMA implementation challenges. To address these challenges, a construction-oriented DfMA framework was theorized, verified in a project-based context, and validated through focus group discussions with off-site construction industry experts. In this study, 45 challenges were identified and categorized into eight main constraint categories: contractual, technological, procedural, cultural, commercial, geographical, financial, and technical/cognitive. The foremost challenges to the adoption of DfMA in construction projects seems to relate to the contractual and operational aspects and their associated stakeholders. This study provides insight into the challenges of implementing DfMA in the construction industry. The investigated challenges contribute to the theoretical and practice-based checklists of limitations for implementing DfMA methods and can inform future research. Finally, this paper introduces a framework for implementing DfMA and provides supporting field-based evidence for its application.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".