Workforce diversity and ergonomics challenges for sustainable manufacturing organisations
Notice bibliographique
Résumé
Demographically, it is evident that the composition of the workforce is becoming more diversified and this trend is very significant in most developed countries such as the US, UK, Canada and Australia. Workforce diversity covers a wide range of dimensions like age, gender, culture, ability, background, level of skill, marital status etc. Because of this, workers share different attitudes, working behaviors, needs, desires and values. Workforce diversity management needs the development and management of such an environment where all individuals with these differences can perform at their full potential, so that any organization can draw an optimum benefit from its diversified workforce. Like many others, manufacturing organizations are also facing the issue of workforce diversity where it affects work performance capabilities. Organizational sustainability can only be ensured by workplace safety, employee satisfaction and retention along with health and well-being. In spite of highly automated systems, manufacturing activities like manual assembly tasks with sustained high quality requirements demand highly repetitive movements with high physical demands at the highest level of work pace.\nErgonomics plays a vital role in the development of work environments that ensure a healthy, safe, risk-free and productive use of human capital. Yet there has been little investigation of workforce diversity management with reference to ergonomic issues, challenges, opportunities and strategies. This paper reveals the need for an ergonomics-based ‘design for all’ approach to address the issues of a diversified workforce. This approach is based on the use of a digital human modeling system where an individual’s actual working capabilities along with coping strategies are used at a pre-design phase for any design assessment. A database of 100 individuals belonging to different age groups and working capabilities provides an opportunity to assess any workplace, product, and process or environment design at an early design phase. In this way, it provides design solutions that are equally acceptable for a broad range of humans belonging to different backgrounds, age groups and levels of ability to do the work. Current ongoing research is focusing on capturing working strategies of a diversified workforce in the furniture manufacturing industry where workers belonging to different age groups, backgrounds, experience and levels of skill will be analyzed. Subsequently this data will be used in a digital human modeling system called HADRIAN providing designers and ergonomists with the ability to access and address the design needs of a more diversified workforce. This strategy helps in addressing global workforce challenges where organizations can effectively utilize their human capital by providing them with a healthy and safe working environment.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».