Developing human factors metrics and tools to support design and management of production
Bibliographic record
Abstract
This dissertation takes an exploratory look at the role of human factors (HF) metrics within an electronics manufacturing organization by focussing on three objectives: 1) determining company stakeholder views of HF metrics, metrics development and HF application, 2) developing a workstation level HF assessment tool for light assembly work, and 3) creating a tool that reports the level of HF integration and maturity in an organization. Mixed methods were used in an action research framework. Research at the case organization was predominantly qualitative and included field notes, audio recordings, and company documents. Identified gaps between engineering and HF metrics were due to HF metrics focussed more on health and safety measures and activities being completed, gaps in the understanding of HF contributions, and the need for new HF tools to generate reporting measures. Five identified themes affecting HF metrics development included 1) knowledge of engineer processes and of HF principles, 2) connection of metrics to the organization, 3) support of the organization and of the information to the organization, 4) resource availability and limitations, and 5) communication format of metrics information. Collaborative user-centered development of a workstation efficiency evaluator tool helped determine data of interest and effective communication of output variables for users. Design stage inputs create outputs that include HF and system information. The tool performed well in a comparison to an observation-based analysis and also demonstrated tolerance to input errors on workstation outcomes. The developed Human Factors Integration Tool assesses HF maturity across organizational functions. Face and content validity of the tool were tested in field testing and workshops. Participants communicated a need for the tool and its contents. Industry stakeholders found the consensus-based tool helped to establish the status of HF in the organization, plan projects to further develop HF capabilities, and initiate discussions on HF for performance and well-being. The created tools demonstrated approaches to the development of future HF tools. These dissertation findings illustrate the need for more HF metric work, including developing HF measures that contribute to organization metrics, and that the development of HF measures and processes need HF considerations in their development.
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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 | 0.067 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".