Impact Analysis of Complexity Drivers in the Supply Chain of Prefabricated Houses
Bibliographic record
Abstract
The lack of living space has recently increased particularly in urban centers. This deficiency cannot be remedied with the productivity status quo in the construction industry. One opportunity to significantly increase the productivity of the construction industry is the industrial modular construction. In order to achieve increased productivity, the value chain must act across the entire organization. A supply chain management is required to exploit the potential of the prefabricated construction. In order to develop a specific supply chain management, the corresponding complexity factors along the value chain must be known. The aim of the study is to quantify the essential factors which influence the value chain for prefabricated houses and form a basis for the future development of a supply chain management. The results of this scientific work clearly show that although an industrial modular production is carried out, the highest complexity drivers are still found on the construction site as well as in the logistics from the module production to the construction site. In addition, it is also apparent that special requirements as well as the size of the modules are decisive factors and as such need to be considered during the future development of the supply chain management concept.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".