Railway supply chain excellence through the mediator role of business intelligence: Knowledge management approach towards information system
Bibliographic record
Abstract
The features of a holistic view in an organization create the data value of the Business Intelligence (BI) and Knowledge Management (KM) in viewing the big picture of organizational performance diagnostics framework. This research focuses on the specific features of railway supply chain performance in viewing the decision-making process and creating better knowledge formation. The intention of the study is to structure supplier performance using BI-KM framework development to determine holistic perspective factors. The outcomes indicate that BI and KM significantly increased the railway supply chain and significantly increased the information system. This BI-KM framework relates the current analytic characteristics in designing the railway supply chain towards information system in determining the strategic theme of the decision-making process of the decision support system together with system features, characteristics of data, the content of the themes, and the effect of the decision-making process and for executive strategic performance diagnostics tool that provides effective strategic decision making in supply chain performance. The quantitative research method uses SmartPLS software version 3.2.8 for empirical analysis through distributing survey questionnaires to 320 railway suppliers in Malaysia. Using a model-driven development framework, to measure the implementation success of the decision support system, the study is conducted in the railway supplier focusing on strategic management that helps to make the decision and facilitate the organizational success.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".