Process Mapping to Understand the Role of Human Factors in Design
Bibliographic record
Abstract
Integrating human factors considerations into the design of production systems can improve productivity and quality results while reducing injury risks to system operators. The researchers are currently conducting an action research study with a Canadian electronics manufacturer to improve their production system design process (PSDP), and thereby their production systems, by integrating human factors (HF). One of the first requirements is a clear understanding of the PSDP as a means of identifying and coordinating process improvements. Since there is no accepted approach to PSDP ‘mapping’, methodological development is needed. A proposed methodology for assessment of PSDPs is described. Data collection includes a combination of interview, observational, and document sources. Analysis uses a general inductive approach to develop a process ‘map’. Mapping options to be assessed for utility include decision trees, cross-functional diagrams, actor-network analyses and concept maps. The developed map will then be verified by company personnel and subsequently used in developmental workshops to help the design team identify possible process improvements. As part of the ‘action research’ methodology, researchers will make observations and field notes to better understand how such process maps can best be created and communicated to company personnel. Challenges include gaining access to confidential company data, and experience shows that the active participation of company personnel in data collection speeds and enhances this process. The utility of the PSDP maps to support process improvement efforts remains to be studied.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".