Deploying Discrete-Event Simulation and Continuous Improvement to Increase Production Rate in a Modular Construction Facility
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
Aiming at continuous improvement, a modular construction company attained favorable results by implementing recommendations that were based on value stream mapping analysis. Yet, there is still a need to assess the production lines in a unified and integrated manner. As such, this study employed simulation to model five major production lines in the factory to evaluate their performance concurrently and suggest improvements. Bottlenecks were identified by tracking the waiting times at different stations, and an iterative and sequential approach was adopted. After eight suggested improvements and tested scenarios, results showed a 17.8% reduction in the unit cycle time and 22% increase in the weekly production rate. The study's major takeaway is the importance of studying improvements in an integrated manner to avoid shifting bottlenecks, achieving local improvements that do not guarantee global improvements, and underestimating the effect of minor changes on the overall process. Simulation modelling helped target these issues.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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