Improving Sales Forecasting by Combining Key Account Managers’ Inputs and Models Such as SARIMA, LSTM, and Facebook Prophet
Bibliographic record
Abstract
Sales forecasting is important for a company to plan its production. The quality of its forecasts influences finances and the product availability. The impact of sales forecasts on a company may result on an immobilization of cash flow by causing a high stock level, which is the opposite of out-of-stock impact. The purpose of this study was to find a suitable model for predicting the best company sales forecasts that has a better accuracy or production plan. The proposed method includes an adjustment of the prediction model by including the key account managers’ expertise as qualitative forecasting method. This adjustment was analyzed using different time series forecasting techniques such as exponential smoothing, seasonal autoregressive integrated moving average and Facebook Prophet. These techniques were compared in parallel with neural network approaches such as long-short term memory. Comparisons were made using root mean square error and residual stock to determine whether the forecasts were too optimistic or pessimistic. The proposed model is dynamic. Adjustments of the qualitative inputs could directly influence the proposed values obtained using different quantitative methods.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".