The integration of predictive analytics and machine learning for demand forecasting in e-commerce: A theoretical exploration
Why this work is in the frame
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Bibliographic record
Abstract
Effective supply chain management hinges on accurate demand forecasting. Yet, traditional methods often struggle with the noise and distortions inherent in communication patterns between supply chain participants. This paper explores the potential of machine learning (ML) to overcome these limitations in the context of e-commerce. We compare the performance of various ML-based forecasting techniques with established methods using data from a chocolate manufacturer, a toner cartridge manufacturer, and the Statistics Canada manufacturing survey. While the overall average accuracy of ML techniques doesn't outperform traditional approaches, a specifically trained support vector machine (SVM) incorporating multiple demand series emerges as the most effective forecasting tool. These findings suggest that, while further research is warranted, strategically leveraging ML holds promise for enhancing e-commerce demand forecasting by learning from complex, noisy data patterns.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it