Supply chain management evaluation in the oil and industry natural gas using SCOR model
Bibliographic record
Abstract
This study aims to evaluate supply chain management on fuel oil to optimize improvement strategies that can be applied to ConocoPhillips companies in Indonesia. Effective and efficient supply chain management is one of the goals to achieve the company's business stability in the fuel oil supply chain. Fuel oil is a very complex basic need for companies in carrying out industrial and transportation activities. The research method is measuring and evaluating company performance through a combination of the Supply Chain Operation Reference (SCOR) model and the Analytic Hierarchy Process. Research respondents through interviews with four informants from the company ConocoPhilips. Based on the SCOR Version 11.0 model, in this study the SCOR measurement is divided into four perspectives, namely Plan, Source, Deliver and Return. Furthermore, through the measurement of Key Performance Indicators, it is classified using five supply chain dimensions, namely reliability, responsiveness, agility, costs, and assets. The research resulted in the final value of supply chain performance of 74,992 which can be categorized as a moderate or intermediate level, this implies that the existence of an assessment system or measurement of supply chain performance on an ongoing basis can be used as a consideration in determining the optimal strategy. Research findings, improvements, and strategies are needed, especially in the perspective of delivering which has the lowest score.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".