Demand Prediction for Food and Beverage SMEs Using SARIMAX and Weather Data
Bibliographic record
Abstract
The SME sector in Indonesia comprises 99.99% of businesses, employing 96.9% of the workforce and contributing 60.5% to GDP and non-oil exports.Despite their importance, SMEs face challenges including limited financial access, product hygiene concerns, and fluctuating demand.Accurate demand prediction is crucial for optimizing production, inventory, and resource allocation.SARIMAX and VAR models are commonly used for demand prediction, with SARIMAX proving more effective, especially when integrating weather data.Due to there are quite few literatures about SARIMAX is used at SMEs, in this study we utilized SARIMAX and VAR models with sales and weather data (average temperature and average humidity) from January to June 2023.SARIMAX with optimum parameters optimum parameters (d=1, D=1, p=2, q=3, P=2, Q=2, s=7) outperformed optimized VAR in predicting demand for food and beverage SMEs.SARIMAX obtained AIC 1070.11,MSE 80.393, MAE 7.513, RMSE 8.966 and reduced MSE by 86.35% compared to VAR.This research highlights the significance of accurate demand prediction for SMEs, emphasizing the importance of considering external factors like weather.Understanding and predicting demand patterns are vital for SMEs to make informed decisions and optimize operations efficiently.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".