Process Mapping as a Tool for Integrating Human Factors into Work System Design
Bibliographic record
Abstract
This thesis explores the utility of a production system development process (PSDP) map as a tool for identifying process improvement opportunities with a focus of integrating human factors (HF) into work system design. In this university-industry action research collaboration with a Canada-based electronics manufacturer, 91 meeting events involving 31 personnel took place. The creation and application of the PSDP map led to process improvement ideas with an implementation plan over 14 sub-projects, as well as evidence of organizational change towards applications of proactive HF in design. Results showed that critical issues of PSDP mapping initiatives include data collection methods, scoping, level of detail, style, content and implementation. It was concluded that a process mapping approach to work system design is an effective method for identifying process improvement opportunities with consideration for human capabilities, though further research is required for implementing and sustaining process improvement changes.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".