Impact Analysis of Complexity Drivers in the Supply Chain of Prefabricated Houses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it