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Record W4245924072 · doi:10.32920/ryerson.14649144

The integration of human factors into a company's production design process

2021· preprint· en· W4245924072 on OpenAlexafffund
Judy Lynn Village

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsToronto Metropolitan University
FundersDalhousie University
KeywordsBenchmarkingProcess (computing)EngineeringProcess managementEngineering design processKnowledge managementComputer scienceBusinessMechanical engineeringMarketing

Abstract

fetched live from OpenAlex

Human factors (HF) considerations, integrated early in design of production assembly systems, can improve both worker health and business performance. A longitudinal case study using an action research style collaboration between researchers and a large electronics manufacturer was the platform for this investigation. The findings show “how” HF, previously outside engineering with HF specialists (HFS) performing reactive injury assessments, increasingly became integrated into each stage of the design process with HF adapted tools, enforceable targets, sign-off, and most HF work focused on proactive design alongside engineers. An operations research tool (cognitive mapping) was used to identify the HF perceptions of Senior Directors and link these to their strategic goals. As a result, HF specialists changed their focus from injury risk to reducing fatigue and improving worker performance and assembly quality. Several industrial engineering tools were also adapted for HF (eg. HF failure mode effects analysis, HF design-for-assembly) and used to quantitatively communicate HF concerns, drive continuous improvement, visibly demonstrate change, and lead to benchmarking. Qualitative data analyzed with a grounded theory methodology resulted in six constructs in the final “Design for Human Factors” theory. The theory propositions state that when: 1. HFS acclimate to the engineering process, language and tools; and 2. strategically align HF to the design and business goals, then HF becomes perceived as a means to improve business performance. This results in 3. HFS being pulled onto the engineering team, which increases HF application and engineers’ awareness of HF, and 4. Management hold engineers accountable for HF targets. Being on the engineering team leads to 5. Engineering tools adapted to include HF targets, and in combination results in 6. HF becoming embedded in the design process. Senior directors reported that increased HF application has improved the design of more recent assembly lines and made it easier for operators. The theory contributes an explanation about how HF can be integrated into design processes to inform researchers and practitioners and improve proactive HF application. Recommendations include increased education for HFS in engineering, and more collaborative research to develop tools that quantify and link worker performance to business metrics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.304
GPT teacher head0.555
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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