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Record W3209631928 · doi:10.32920/ryerson.14657409.v1

Developing human factors metrics and tools to support design and management of production

2021· preprint· en· W3209631928 on OpenAlexafffund
Michael Greig

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsWorkstationComputer scienceStakeholderKnowledge managementHuman resourcesProcess managementResource (disambiguation)Engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.609
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.104
GPT teacher head0.273
Teacher spread0.169 · 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 teacher head, 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

Citations3
Published2021
Admission routes2
Has abstractyes

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