Inventory Forecasting Analysis using The Weighted Moving Average Method in Go Public Trading Companies
Bibliographic record
Abstract
This research aims to analyze inventory forecasting using the weighted moving average method and then compare the trading companies' patterns. The research method used is quantitative descriptive with secondary data of inventory in the period 2018-2022 which provide quarterly. This research uses the weighted moving average method to calculate forecasting of inventory by Microsoft Excel data analysis techniques. This research shows the highest inventory forecasting on PT Sumber Alfaria Trijaya Tbk (AMRT) occurs in the first quarter of 2023 with the amount of 10.537.541 and the lowest forecasting occurs in the second quarter in 2023 with the amount of 10.431.677. The highest inventory forecasting on PT Erajaya Swasembada Tbk (ERAA) occurs in the second quarter of 2023 with the amount of 6.443.525 and the lowest forecasting in the fourth quarter of 2023 with the amount of 6.418.659. The highest inventory forecasting on PT United Tractors Tbk (UNTR) occurs in the third quarter of 2023 with the amount of 12.239.422 and the lowest forecasting in the first quarter of 2023 with the amount of 12.050.681. Based on the study's results, the tracking signal value at AMRT was 2,17, ERAA was 0.01, and UNTR was -0.08. The three companies' results prove that the weighted moving average can be used to determine inventory forecasting for the next period because the tracking signal value is still within the control limits of ±4.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".