A Comparative Analysis of Design for Manufacturing And Assembly (DFMA) Implementation Challenges In On-Site Construction (ONSC) and Off-Site Construction (OSC)
Bibliographic record
Abstract
Design for Manufacturing and Assembly (DfMA) offers a transformative approach to enhance productivity in the construction industry. Although the benefits of DfMA are widely recognized, challenges remain in the integration of its principles throughout the different stages of construction projects, which include both off-site construction (OSC) and on-site construction (OnSC). This paper presents a focused analysis of the challenges encountered in integrating DfMA into construction projects, with a particular emphasis on OnSC projects. Through a qualitative comparative analysis (QCA) approach and by using the literature review and expert interviews, this study conducts a comparative analysis between the identified challenges in OnSC and those common in OSC. By identifying the complexities of DfMA implementation in OnSC projects and conducting a comparative assessment with DfMA challenges in OSC, this study illuminates the evolving nature of DfMA practices. It sheds light on how these practices are adapting in response to the unique demands and characteristics of both OSC and OnSC. The results of this study have the potential to provide organizations with guidance for the successful implementation of DfMA strategies across all project phases, resulting in increased productivity. In doing so, the research contributes insights to the fields of construction management and innovation
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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 | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".