Integrating Ergonomics Aspects Into Operations Management Performance Optimization Models: A Modeling Framework
Bibliographic record
Abstract
OCCUPATIONAL APPLICATIONS Employees play critical roles in improving the performance of operation systems. Work-related health disorders and injuries degrade employees' work performance and hence the performance of the system. Previous studies frequently reported the negative effects of poor ergonomic conditions of the workplace on developing occupational health problems. However, managers lack tools to consider occupational ergonomics aspects, employees' health conditions, and their financial consequences when the performance of systems is optimized. This research provides a hierarchical modeling framework to integrate occupational ergonomics aspects into system performance optimization models. With this modeling approach, ergonomists and human factor practitioners can better influence managers' decisions by developing analytical tools that demonstrate the health, economic, and operational improvement of ergonomics conditions in the system-level analysis.TECHNICAL ABSTRACT Background: Occupational illnesses have negative consequences for employees. They also reduce employees' overall work performance, creating negative economic impacts on companies. Occupational ergonomics studies mostly investigate work-related health and safety aspects of a system to reduce the rate of these health problems for employees. However, they do not usually involve optimizing the performance of a system and do not account for the operational aspects of the system such as demand, workstation interactions, and inventory levels. In contrast, operations management studies usually deal with these operational aspects while optimizing the performance of operation systems. Although human resources are involved in many stages of operation systems, operations management performance optimization models usually use simplifying assumptions regarding human operational characteristics. They rarely include causes and effects of work-related health problems. Purpose and Methods: This article introduces a modeling framework, integrating occupational ergonomics aspects into operations management performance optimization models. It provides a road map for assessing the impact of work-related risk factors, including physical and psychosocial aspects on system performance. Results: An illustrative example demonstrates the applicability of the developed framework in a real situation. The results demonstrate a 0.76% to 3.76% cost growth for a manufacturing system due to the poor ergonomic design of the workplace. Conclusion: By applying this framework, practitioners can find out how the performance of their systems will be enhanced by investing in safer working conditions for employees. Managers can also better assess the economic consequences of a poor ergonomic design and consequent occupational illnesses.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".