Modelling the effects of employee injury risks on injury, productivity and production quality using system dynamics
Bibliographic record
Abstract
The aim of the current study is to explore the use of system dynamics (SD) modelling as a tool to examine the impacts of human factors in production on worker low back injury, productivity and quality performance parameters. The SD model was created using relationships in the scientific literature. This data supplemented with input from both a quality and a safety manager in an automotive plant, who also reviewed the resulting causal loop diagrams. Results showed that, over the 5-year simulation period of the base model, percentage of operators reporting low back pain increased from ∼0% to 1.3%, human error rates increased by 40%, and production rate dropped by 0.2%. This example model addressed three risk factors for a single injury type – and is therefore an underestimate of total system impacts of poor HF. While the extension of the model is needed, the current example highlights a cautionary point for managers and designers who may not see an immediate impact of a poor design but may face increased injury, quality and productivity problems over time. This novel application of SD modelling can help isolate and quantify these effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".