Cement Supply Chain Model of Palangka Raya Using System Dynamics Method
Bibliographic record
Abstract
One of the most strategic material resources in the execution of construction is cement. It is estimated that the demand for cement to support construction activities in Indonesia is approximately 78.66% of national cement consumption, with cement consumption in Indonesia increasing by 14.1% in the first quarter of 2021 to 18.19 million tons. The objective of this research is to understand how the dynamic supply chain model and cement inventory in Kota Palangka Raya will be from 2024 to 2028. In this research procedure, data analysis techniques use the system dynamic method with the Vensim program, utilizing data from interviews with distributors and retail stores scattered in Kota Palangka Raya and supporting research data. The results of the supply chain model for cement include three variables: stock, in, and out. The total inventory of Conch cement in 2024 is 1140555 zak, in 2025 is 1547654 zak, in 2026 is 6192387 zak, in 2027 is 1946944 zak, and in 2028 is 1918412 zak. The total inventory of Gresik cement in 2024 is 548287 zak, in 2025 is 1785559 zak, in 2026 is 1826060 zak, in 2027 is 1849395 zak, and in 2028 is 1838869 zak. The total inventory of Tiga Roda cement in 2024 is 42763390 zak, in 2025 is 250804 zak, in 2026 is 177705 zak, in 2027 is 321805 zak, and in 2028 is 183585 zak.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".