The integration of human factors into a company's production design process
Bibliographic record
Abstract
Human factors (HF) considerations, integrated early in design of production assembly systems, can improve both worker health and business performance. A longitudinal case study using an action research style collaboration between researchers and a large electronics manufacturer was the platform for this investigation. The findings show “how” HF, previously outside engineering with HF specialists (HFS) performing reactive injury assessments, increasingly became integrated into each stage of the design process with HF adapted tools, enforceable targets, sign-off, and most HF work focused on proactive design alongside engineers. An operations research tool (cognitive mapping) was used to identify the HF perceptions of Senior Directors and link these to their strategic goals. As a result, HF specialists changed their focus from injury risk to reducing fatigue and improving worker performance and assembly quality. Several industrial engineering tools were also adapted for HF (eg. HF failure mode effects analysis, HF design-for-assembly) and used to quantitatively communicate HF concerns, drive continuous improvement, visibly demonstrate change, and lead to benchmarking. Qualitative data analyzed with a grounded theory methodology resulted in six constructs in the final “Design for Human Factors” theory. The theory propositions state that when: 1. HFS acclimate to the engineering process, language and tools; and 2. strategically align HF to the design and business goals, then HF becomes perceived as a means to improve business performance. This results in 3. HFS being pulled onto the engineering team, which increases HF application and engineers’ awareness of HF, and 4. Management hold engineers accountable for HF targets. Being on the engineering team leads to 5. Engineering tools adapted to include HF targets, and in combination results in 6. HF becoming embedded in the design process. Senior directors reported that increased HF application has improved the design of more recent assembly lines and made it easier for operators. The theory contributes an explanation about how HF can be integrated into design processes to inform researchers and practitioners and improve proactive HF application. Recommendations include increased education for HFS in engineering, and more collaborative research to develop tools that quantify and link worker performance to business metrics.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".