The effects of applying just-in-time production system on maximizing profitability of small and medium industrial companies in Jordan
Bibliographic record
Abstract
This study aimed to investigate the extent of applying just-in-time production system on maximizing profitability of small and medium industrial companies in Jordan, where maximizing profitability of companies represented in both (reducing product costs and reducing ending inventory obsolescence) for those companies. In order to achieve the objectives of the study, a questionnaire was designed as a tool for study and was distributed to the study sample represented by administrative managers and financial managers in small and medium industrial companies. The study community consisted of (235) companies of small and medium industrial companies. The Robert Mason equation was used to determine the sample size which consisted of 146 companies, where this study distributed 292questionnaires, and 217 of them were received, while 33 questionnaires were excluded, and the final sample was 184 questionnaires. The results of the study showed that there is a statistically significant impact of applying just-in-time production system on maximizing profitability of small and medium industrial companies in Jordan, and there is a statistically significant impact of applying just-in-time production system on (reducing product costs and reducing ending inventory obsolescence) in small and medium industrial companies. The Study recommended a number of recommendations, the most important of which is: It is necessary to review the successful experiences of international companies that adopt applying just-in-time production system, and to benefit from them by small and medium industrial companies in Jordan in applying just-in-time production system as an advanced tool for cost management and thus maximizing their profitability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".