The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand
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Bibliographic record
Abstract
ABSTRACT Forecasting for inventory items with lumpy demand is difficult because of infrequent nonzero demands with high variability. This article developed two methods to forecast lumpy demand: an optimally weighted moving average method and an intelligent pattern‐seeking method. We compare them with a number of well‐referenced methods typically applied over the last 30 years in forecasting intermittent or lumpy demand. The comparison is conducted over about 200,000 forecasts (using 1‐day‐ahead and 5‐day‐ahead review periods) for 24 series of actual product demands across four different error measures. One of the most important findings of our study is that the two non‐traditional methods perform better overall than the traditional methods. We summarize results and discuss managerial implications. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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