Examining the Impact of Lean Practices on Flexibility Performance: The Moderating Effect of Environmental Dynamism
Bibliographic record
Abstract
This paper examined the effects of four core and internal lean practices on flexibility performance in Jordanian manufacturing companies. Lean practices included setup time reduction, continuous improvement, synchronization of operations, and pull system. A survey questionnaire was used to collect data from 157 manufacturing companies from different industry types. Hierarchical regression analysis showed that lean production posively and significantly affected flexibility performance. All lean practices proved to be positively and significantly related to flexibility performance. The most contributing lean practice was synchronization of operations followed by pull system and continuous improvement. The moderating effect of environmental dynamism was also examined. The results of the interaction terms showed that environmental dynamism positively and significantly moderated the relationship between synchronization of operations and flexibility performance. The findings of this study highlighted the important role of synchronization of operations, a widely neglected lean practice in the literature, in improving flexibility performance. Additionally, we contributed to the controversial issue in the literature concerning the impact of lean production on performance in a dynamic 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".