Analisis Perencanaan Produksi Agregat pada CV. Pelangi Rex’s di Denpasar
Bibliographic record
Abstract
The production function in every company has a very important role in developing a business, especially in the industrial sector. This study aims to determine the aggregate production planning strategy that has the lowest cost at CV. Rainbow Rex's. This type of research uses a quantitative descriptive approach. Data collection methods used are interviews and observation. Data analysis techniques are performed by forecasting demand using the Moving Average and Exponential Smoothing Methods, and the aggregate planning methods used are the Chase Strategy, Level Strategy, and Mixed Strategy. Based on the research results, the selected forecasting calculation for Croissant products is the Moving Average and Fresh Bread products in the selected forecasting calculation, namely the Exponential Smoothing method. The aggregate production planning strategy that has the lowest total cost for Croissants and Fresh Bread is the Chase Strategy. The implication of this research is to add references for academics who conduct research on aggregate production planning and can be used as material for company considerations in carrying out production planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".