Smart Manufacturing Support to Product Platforms in Industrialized House Building
Bibliographic record
Abstract
Swedish house building companies currently face many challenges in terms of fluctuating market demand, need for flexible product offering, non-uniform governmental regulations, high costs, and long lead times. These challenges affect both internal and external efficiency of companies. Product platforms have been used for more than a decade in this industry to improve both internal and external efficiency. However, the industry is still criticized for its inefficient and costly process. Smart manufacturing has emerged as means to improve the efficiency of internal processes and the question is if and how smart manufacturing can complement and support product platforms in industrialized house building. The aim of this study is to explore the potential of smart manufacturing to complement and support product platforms in theory and practice in the context of industrialized house building. A literature review and a multiple case study were chosen to fulfill the study objective. In total fourteen semi-structured interviews were conducted in two timber house building companies. The data was analyzed within and across cases using four platform assets for categorization: components, processes, knowledge and relationships. The results show that the smart manufacturing technologies are in both theory and practice mainly supporting the process platform asset through developing vertical and horizontal IT systems integration, definition and digitalization of flexible building systems, and transferring explicit drafting and engineering knowledge into parametric modelling tools.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".