Adapting Engineering Design Tools to Include Human Factors
Bibliographic record
Abstract
<p><strong>OCCUPATIONAL APPLICATIONS </strong></p> <p>In a longitudinal collaboration with engineers and human factors specialists at an electronics manufacturer, five engineering design tools were adapted to include human factors. The tools, many with required human factors targets, were integrated at each stage of assembly design to increase the proactive application of human factors. This article describes the process of adapting the five tools within the collaborating organization. Findings suggest 12 key features of human factors tools, most importantly that they “fit” with engineering processes, language, and tools; directly address business goals and influence key metrics; and are quantifiable and can demonstrate change. To be effective in an engineering design environment, it is suggested that human factors specialists increase their understanding of their organization’s design process, learn which tools are commonly used in engineering, focus on important metrics for the business goals, and incorporate human factors into engineering-based tools and worksystem design practices in their organizations.</p> <p><strong>TECHNICAL ABSTRACT </strong></p> <p><em><strong>Rationale</strong></em><strong>: </strong>Design engineers use diverse tools in design, but few incorporate human factors, even though optimizing human performance can further improve operational performance. There is a need for practical tools to help engineers integrate human factors into production design processes. <em><strong>Purpose: </strong></em>This article demonstrates how five engineering design tools were adapted to include human factors and were integrated into design processes within the case study organization. It also provides features of an effective human factors tool and recommendations for practitioners. <em><strong>Method:</strong></em> A longitudinal collaboration with engineers and human factors specialists in a large electronics manufacturing organization allowed in vivo adaptation and testing of various tools in an action research methodology. Qualitative data were recorded from multiple sources, then transcribed and analyzed over a 3-year period. <em><strong>Results: </strong></em>The adapted tools integrated into each stage of the design process included the human factors process failure mode effects analysis, human factors design for assembly, human factors design for fixtures, workstation efficiency evaluator, and human factors kaizens. Each tool had a unique participatory development process; 12 features are recommended for effective human factors tools based on the findings herein. Most importantly, tools should “fit” with existing engineering processes, language, and tools; directly address business goals and influence key metrics; and be quantifiable and demonstrate change. <em><strong>Conclusions:</strong></em> Engineers and management responded positively to the five tools adapted for human factors because they were designed to help improve assembly design and achieve their business goals. Several of the human factors tools became required targets within the design process, ensuring that human factors considerations are built into all future design processes. Adapting engineering tools, rather than using human factors tools, required a shift for human factors specialists, who needed to expand their knowledge of engineering processes, tools, techniques, language, metrics, and goals.</p>
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".