Resource Optimization for Modular Construction Through Value Stream Map Improvement
Bibliographic record
Abstract
Implementation of Lean manufacturing begins with the development of value stream maps, which depict process flow in the production line. However, the application of value stream mapping (VSM) in modular manufacturing has various shortcomings, due to the variety of products and the level of customization demanded. One of the challenges is assessing the production rate variations in modular manufacturing activities and resource movements within work stations along the production line. VSM also falls short of verifying prior to implementation that the proposed state will meet the efficiency demands for a variety of products. This research presents a model of resource optimization to develop the VSM, considering variety as an inevitable element in modular construction, and also evaluates the value stream prior to implementation. The methodology provides an efficient method to formulate a set of rules to quantify productivity rate, probabilistic duration, and resource requirements for fabrication of wall components. A simulation model is also generated in order to evaluate the proposed VSM. Currentand future-state maps of the factory production line are compared to prove the effectiveness of the proposed methodology. The proposed methodology is validated by a case study – a residential modular factory located in Edmonton, AB, Canada.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".