Human Factors in Operations Management: Comparative Perspectives on Decision Support Models
Bibliographic record
Abstract
The integration of human factors and ergonomics (HF/E) into industrial and operational decision support modeling has grown rapidly over the past decade. In particular, physical aspects (e.g., physical workload, fatigue-recovery cycles) have been popular in developing human-centered solutions in operations management (OM). These solutions aim, first, to prevent both short- and long-term health issues among workers (e.g., work overload and occupational musculoskeletal disorders) and, second, to enhance the system performance of model-based solutions in real-world settings. However, adopting a human-centric perspective necessitates interdisciplinary knowledge. Specifically, each model or tool developed by ergonomists possesses unique characteristics (including the original experimental settings, the scope of collected data, and the intended application scenarios). They should thus be used with caution in managerial decision support models. To facilitate the knowledge transfer from HF/E to OM, this pilot study provides preliminary results of: (i) a scoping review of the integration of physical HF into decision support modeling in operations management; (ii) a critical evaluation of model assumptions and the interpretation of results from an HF/E perspective; and (iii) the development of a structural framework to suggest HF/E model choices. The current study presents preliminary results with an interdisciplinary perspective, which will be extended in future research.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".