Simulation-based decision support for production improvement using integrated ergonomic and productivity performance indicators
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
Workers in the construction manufacturing industry are exposed to labour-intensive tasks with ergonomic risks such as forceful exertion and repetitive motion. Due to increased productivity and repetitive motions resulting from improvement initiatives implemented in offsite construction manufacturing, the investigation of ergonomic risks associated with these changes is needed. In this context, this paper explores an existing panelised floor production line aiming to minimise its ergonomic risks while improving its current productivity rate. Information pertaining to human body motion and productivity is extracted from video recordings. The ergonomic risks associated with specific tasks are identified using two existing ergonomic risk assessment methods: the rapid entire body assessment and the rapid upper limb assessment. A simulation model is used to evaluate various process improvements from the perspective of both ergonomic risks and productivity to support the decision-making process and the prioritisation of process changes in the factory.
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 | 0.001 |
| 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