The Impact of the Bullwhip Effect on Sales and Earnings Prediction Using Order Backlog
Bibliographic record
Abstract
Abstract Recent work in the supply chain literature suggests that the variance in orders placed with suppliers will be larger than that of sales to buyers. This distortion in demand information increases as it is passed along the supply chain from customers to upstream suppliers and has been referred to as “the bullwhip effect.” In this paper, we argue that the bullwhip effect reduces the ability of order backlog to predict future sales and earnings for upstream suppliers. Results obtained from our empirical analysis support this proposition. We find that the impact of bullwhip on the predictive ability of order backlog is further accentuated in firms with longer operating cycles. Market intermediaries such as financial analysts, on average, are unable to fully account for differences in the predictive ability of order backlog. However, analysts belonging to employers that follow all firms in a vertical supply chain do a better job of understanding the impact of bullwhip on the predictive ability of order backlog. In additional tests, we find that the bullwhip effect also impedes the ability of inventory components to predict future sales and earnings.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".