Critical Success Factors in the Development and Implementation of Special Purpose Industrial Tools: An Ergonomic Perspective
Bibliographic record
Abstract
The variety of different manual tasks performed in industry is infinite. In many circumstances, these tasks are carried out under difficult conditions with ergonomic concerns about the postures, force or repetitions involved. These tasks are sometimes performed using a dedicated special purpose tool, often developed by the workers themselves. These special tools are generally task-oriented only, with very little consideration of basic ergonomics. Therefore, in many cases, the design of a new or improved special purpose tool is one of the solutions that could enhance the ergonomics of the performed task. However, developing and implementing a new or improved tool is not an easy assignment and the ergonomist could face many unexpected challenges and pitfalls. This paper discusses the pitfalls that could compromise these ergonomic interventions and the critical success factors that should be considered. The main difficulties that could arise during such a project include the poor understanding of the user's needs, the hidden constraints related to the requirements of the task to be performed, the construction and testing of the prototypes, and the users’ resistance to change. Critical success factors related to worker participation, needs and constraints analysis, and the implementation of prototypes, are presented. Examples from industrial projects involving the development and implementation of special purpose tools are used to support the discussion. This paper should provide some guidance in this particular field of applied ergonomics.
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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 | 0.028 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".