Bibliographic record
Abstract
Lean Production System (LPS) has become very popular among manufacturing industries, services and large commercial areas over the years due to its production increase abilities. However, LPS practices can have both negative and positive impacts in worker’s psychosocial factors like motivation, satisfaction and commitment and physical and psychological health factor like musculoskeletal disorders (MSD) and stress. Since LPS is a very broad term, there is no simple relation between LPS implementation and its consequences over work environment and workers. Therefore, it is necessary to study the different factors that can affect the work environment in each case. A wide variety of LPS practices can have negative and positive impacts on workers. Furthermore, the effects of lean may also depend on the sector and country in which it is implemented. There are no studies in the literature that cover all these effects and analyse them together with the involved environment. In this study, articles were collected in scientific publications in the last 26 years and analysed. Results show that Just-in-Time (JIT) practices are strongly related with negative effects in MSDs and stress caused by intensification of work and increase of control over workers. However, JIT practices such as manufacturing cells can increase job enrichment trough multi-skilling. Respect for people practices can act as buffers to lean practices. Job rotation reduces human effort and work pace trough the increase of recovery time. Workgroups create job support acting as buffers to psychosocial factors. Results show a majority of negative effects in the automotive sector and in countries such as Canada, USA and UK. Scandinavian countries have implemented hybrid forms of Lean which are related to an increase in effects such as motivation and job satisfaction. However, the overall analysis is that the effects of lean on workers depend more on the way companies manage and implement it rather than the countries cultural factors. This study can be useful for managers and leaders who seek to transform traditional enterprises into exemplars of lean success, showing the need to balance lean and good working conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".