Implementation of enterprise human resources management standards to achieve supply chain excellence in fertilizer companies in Indonesia
Bibliographic record
Abstract
Employees have collective skills, abilities and experience that contribute to the interests of the company where they work, and can contribute to the success of supply chain and is a resource that is an asset in achieving supply chain excellence. To examine this relationship, this study investigates the role of human resources management (HRM) on the supply chain management of fertilizer companies in Indonesia, which is focused on the distribution and logistics of fertilizer from producer to their network and consumers. This study took samples from two fertilizer companies in East Java, structural equation modeling (SEM) analysis was carried out with the Smart PLS. 18 (Partial Least Square) program, and shows the value of t = 348.825 with p = 0.000 (p < 0.000) which means there is a significant effect of implementing enterprise human resources management (HRM) standards on company supply chain excellence and represents indicators of management strategy, cost leadership, focus on productivity, logistics, distributions, operational effectiveness, differentiation, and cooperation with companies or other institutions that support each other. The results of this study have provided an overview of the application of enterprise capable of supporting human resource management. Enterprise standards in both fertilizer companies are applied regularly, starting from checking the completeness of the employee database, including contact information, details of salary, compensation and benefits; attendance, employee performance, career planning, work relations, socialization, and informal communication so that the company would be able to adapt and be more flexible towards every need in the present and in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".