Analysis of Performance and Efficiency of Supply Chain of Harum Maluku 52 Oil
Bibliographic record
Abstract
Maluku is an archipelagic province that is rich and famous for its abundant natural wealth.This abundant natural wealth is in the form of spices such as cloves (Syzygium aromaticum L), nutmeg (Myristica fragrance), eucalyptus (Melaleuca leucadendra), lemongrass (Cymbopogon citratus), and ginger (Zingiber officinale).CV.Alfa Blessing is a business actor for processed agricultural commodity products in West Seram Regency, Maluku Province.This company produces fragrant oils that are beneficial for health.The results of SCOR analysis, the performance of the Harum Maluku 52 oil supply chain is included in the good criteria with a performance value of 70.29 with the highest weight on the delivery criteria of 0.469 and the lowest on the source criteria with a weight of 0.049.To improve the supply chain performance of Maluku fragrant oil, CV.Alfa Blessing needs to pay attention to the plan criteria because it is the ideal culmination of the core process in SCOR.Companies must also consider the source criteria because it has the lowest weight.What needs to be done is to pay attention to the procurement of raw materials to meet demand and ensure that raw materials are not wasted due to a lack of demand at one time.CV.Alfa Blessing also needs to increase the working value of assets or capital to increase the ability to use assets productively.The analysis of the margin share, farmer's share, and cost-benefit ratio shows that both supply chain channels are efficient.If the two chains are compared, it can be seen that supply chain channel II is more efficient than supply chain channel I.This means that a short supply chain channel is more efficient than a long supply chain channel.Companies must continue to strive to improve company and employee performance so that the fragrant oil products are of higher quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".