Process mapping as a tool for participative integration of human factors into work system design
Bibliographic record
Abstract
In this study, we develop and evaluate a method for applying business process mapping to document the production system design process (PSDP) within an electronics manufacturer. We then test the ability of the map to facilitate identification of opportunities to integrate human factors (HF) proactively into the company’s PSDP. Stakeholders (n = 31) from various departments were involved in recorded interviews and workshops (109 hours over 20 months) at three stages: map creation, map application, and evaluation of the mapping process. Map application resulted in stakeholders identifying 13 opportunities to integrate proactive human factors into specifically mapped points of the PSDP. The map allowed participants to see the flow of activities and information from a higher level. Methodological issues to resolve in undertaking a PSDP include definition of scope and level of mapping detail, content errors, missing information, and map overlap.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".