PERAMALAN PERSEDIAAN OBAT FLU DAN BATUK MEREK SNF UNTUK TAHUN 2024 DI GUDANG PT BCD MENGGUNAKAN METODE DEKOMPOSISI
Bibliographic record
Abstract
PT BCD is a company which engages in the field of the distribution sector. Besides distributing, PT BCD also has a warehouse which is used as a place to store stock of goods that have not been sent to customers. The products distributed include medicines, pharmaceutical equipment, veterinary products and other consumer goods. However, recently the products most frequently ordered by customers are cough and flu medicines considering the unpredictable weather changes and increasing pollution. When compared with other brands, SNF brand cough and flu medicine products are the most frequently ordered products over the past two years. However, the amount of demand for these products is often uncertain every quarter. This sometimes causes customer orders set to a pending stock so that customers have to wait quite long for the product to be received or in other conditions the product in the warehouse is overstock so that the stock must be transferred between branches. To anticipate this happening in the future, which mean at 2024, forecasting needs to be done so that the supply of SNF brand medicine can be estimated more precisely. In this research, forecasting was carried out using the decomposition method to determine how many folding boxes should be provided in each quarter in 2024. From the forecasting results, we obtained an estimate of the stock that must be provided in the 1st quarter is 438 folding boxes, 340 folding boxes for the 2nd quarter, in the 3rd quarter approximately need 379 folding boxes and in the fourth quarter there were 270 folding boxes needed
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".