The Integration of Human Factors into Discrete Event Simulation and Technology Acceptance in Engineering Design
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
This action research thesis aimed to: 1) develop and test a viable Discrete Event Simulation and Human Factors Modeling approach for an Ontario based telecommunication company, and 2) identify the factors that affect the uptake and application of the approach in work system design. This approach, which was validated at the Company, incorporated fatigue dose and learning curves in a Discrete Event Simulation model. The barriers to uptake included: Time constraints, lack of technological knowledge and initial cost. The uptake facilitators were: High frequency products produced, clear value added to leadership, defects reduction and the Company being open to new technology. In addition to helping design a manual assembly line with fewer bottlenecks and reduce the human factors risks for the employee, the developed approach showed a 26% correlation with quality defects. Further research is recommended to identify additional human factors and their benefits.
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