Workforce diversity and ergonomics challenges for sustainable manufacturing organisations
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".