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Enregistrement W4229565517 · doi:10.32920/14637564

Adapting Engineering Design Tools to Include Human Factors

2021· preprint· en· W4229565517 sur OpenAlexafffund
Judy Lynn Village, Michael Greig, Saeed Zolfaghari, Filippo A. Salustri, Patrick Neumann

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineDecision Sciences
ThématiqueConstruction Project Management and Performance
Établissements canadiensToronto Metropolitan University
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaWorkplace Safety and Insurance Board
Mots-clésEngineering design processProcess (computing)EngineeringProcess managementComputer scienceMechanical engineering

Résumé

récupéré en direct d'OpenAlex

<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>

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,372
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0020,000
Science ouverte0,0010,003
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,371
Tête enseignante GPT0,409
Écart entre enseignants0,038 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2021
Routes d'admission2
Résumé présentoui

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