Production system design elements influencing productivity and ergonomics
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate a strategic change from parallel cell‐based assembly (old) to serial‐line assembly (new) in a Swedish company with special reference to how production system design elements affect both productivity and ergonomics. Design/methodology/approach Multiple methods, including records and video analysis, questionnaires, interviews, biomechanical modelling, and flow simulation were applied. Findings The new system, unlike the old, showed the emergence of system and balance losses as well as vulnerability to disturbances and difficulty handling all product variants. Nevertheless, the new system as realised partially overcame productivity barriers in the operation and management of the old system. The new system had impaired ergonomics due to decreased physical variation and increased repetitiveness with cycle times that were 6 per cent of previous thus increasing repetitiveness, and significantly reducing perceived influence over work. Workstations' uneven exposure to physical tasks such as nut running created a potential problem for workload management. The adoption of teamwork in the new system contributed to significantly increased co‐worker support – an ergonomic benefit. Practical implications Design decisions made early in the development process affect both ergonomics and productivity in the resulting system. While the time pattern of physical loading appeared to be controlled by flow and work organisation elements, the amplitude of loading was determined more by workstation layout. Psychosocial conditions appear to be affected by a combination of system elements including layout, flow, and work organisation elements. Strategic use of parallelisation elements in assembly, perhaps in hybrid forms from configurations observed here, appears to be a viable design option for improved performance by reducing the fragility and ergonomic problems of assembly lines. Originality/value The interacting design elements examined here pose potential “levers” of control by which productivity and ergonomics could be jointly optimised for improved total system performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".